Unlocking Business Insights through Data

Analytical knowledge 2021 03 19

Estimated read time: 1:20

Summary

The lecture on analytical knowledge by Tarja Keski-Mattinen dives into the intricacies of business analytics focusing on enhancing decision-making processes in organizations. The session explores various elements such as understanding analytics, the role of digital transformation, and the significance of business intelligence. Discussions also cover common misconceptions in analytical solutions, reasons for underutilization of analytics, and the essence of bridging the gap between IT and business knowledge. Through interactive activities, attendees engage in identifying stakeholder requirements, analyzing business content, and utilizing various analytical frameworks to create effective business solutions, all while maintaining a fun and insightful environment.

Highlights

Understanding analytics involves bridging IT and business worlds 🌐
Digital transformation supports decision-making with empowered analytics 🛠️
Gartner reports only 20% of analytics insights deliver business outcomes 📉
An anecdotal shopping video highlighted keyword accuracy in customer needs 🛒
Stakeholder engagement is crucial in successful analytics projects 👫
Analyzing the ecosystem helps implement data-driven decision-making processes 🌍
Effective dashboards require understanding user needs and decision-making scenarios 📊

Key Takeaways

Understanding analytics is key to transforming data into actionable business insights 🔑
Digital transformation is closely linked with analytics to support effective decision-making 🌐
40% of analytics projects fail due to poor functionality, complexity, and performance issues 🚩
Business intelligence involves integrating various data sources for better decision-making 📊
The gap between business and IT knowledge needs to be bridged for successful analytics implementation 🌉
Articulating business needs requires both qualitative and quantitative analytical skills 🧠
Effective communication and collaboration are essential in the realm of analytics 🤝

Overview

The session started with engaging questions about what constitutes analytics, prompting participants to reflect on their understanding of the topic. It highlighted that analytics is more than just crunching numbers; it's about transforming data into actionable insights for business strategy. Opening discussions revolved around the role of analytics in digital transformation and its importance in contemporary business landscapes.

During the interactive group activities, participants were tasked with identifying the right questions to ask when developing analytic dashboards. This exercise shed light on the critical need for communication between IT and business sectors to ensure all stakeholders' needs are understood and addressed. Attendees discussed various business scenarios, emphasizing the importance of aligning analytical solutions with strategic business goals.

As the session went on, real-world challenges in implementing analytics were addressed. Key hurdles include the complexity of technology, ambiguous stakeholder communication, and issues around data accessibility. The speaker underscored that effective analytics require both qualitative insights and quantitative data analysis to drive informed business decisions. This blend of theory and practical exercises provided a comprehensive understanding of analytical business knowledge.

Chapters

00:00 - 00:30: Introduction The chapter begins with an introduction to the topic of analytics in business. The narrator expresses a preference for an interactive session rather than a one-sided discussion and poses a reflective question to the audience: "What do you consider as analytics?" This sets the stage for further exploration into the analytical knowledge related to business content.
00:30 - 02:30: What is Analytics? This chapter introduces the concept of analytics, inviting participants to engage in a discussion about what elements or topics might be included under the umbrella of analytics. It encourages interaction either through voice or chat messages.
02:30 - 04:00: Supporting Business Decision Making Today's lecture focuses on enhancing efficiency in business decision-making through analytical knowledge. It discusses the prerequisites for roles such as analysts or consultants, aiming to improve skills in these areas.
04:00 - 06:00: Complexity and Understanding in Analytics This chapter focuses on the role of applications in supporting business and strategic decision-making, stressing the importance of decision-making and business support in modern courses. It highlights the significance of digital transformation and analytical knowledge in these processes.
06:00 - 08:00: Business Intelligence and Its Role The chapter focuses on the importance of business intelligence in the context of digital transformation. It highlights how analytical understanding aids in decision-making processes using data. The speaker draws insights from both academic and commercial experiences, and there's an open invitation for questions and discussions to ensure clarity.
08:00 - 10:00: Analytics Insights and Business Outcomes The chapter discusses the nature of analytics projects and the challenges faced in analysis. It emphasizes the difficulty in finding a single definition of analytics and the importance of understanding both analytical techniques and business context to effectively solve analysis problems.
10:00 - 15:00: Challenges in Data Analytics Adoption The chapter focuses on the challenges faced during the adoption of data analytics, referring to a holistic understanding of business intelligence initiatives. It highlights the catchy terminology used by Gartner for various business intelligence and application-related initiatives, and encourages those unfamiliar with business intelligence to seek clarification.
15:00 - 19:00: Decision Making: Intuition vs. Data-Driven The chapter 'Decision Making: Intuition vs. Data-Driven' discusses the elements of decision making, focusing on the balance between intuitive and data-driven approaches. The transcript begins by mentioning the concept of business intelligence, which involves utilizing data—mainly internal but possibly supplemented with external data—from various sources. This suggests an exploration of how data is integrated into decision-making processes. Although the transcript provided is fragmentary, it hints at a contrast between traditional intuitive decision-making practices and modern techniques that leverage data analytics for insights.
19:00 - 25:00: Examples of Decision Making in Business The chapter discusses the integration of various business systems such as customer relationship management, enterprise resource planning, finance, and HR systems into decision-making processes. It emphasizes the importance of having a comprehensive set of tools, procedures, applications, software, and human resources to facilitate business intelligence. Essentially, business intelligence is portrayed as a fusion of different procedures that aid in informed decision making.
25:00 - 30:00: Success and Failure in IT Projects The chapter explores the concepts of business intelligence and analytics, emphasizing the importance of integrating data into decision-making processes. It highlights how effective use of tools, resources, and skilled personnel can enhance decision-making capabilities. The discussion suggests that while business intelligence is crucial, analytics provides a broader context by encompassing additional applications beyond standard business intelligence practices.
30:00 - 35:00: Types of Knowledge: Business vs. IT The chapter explores the different types of knowledge in the realms of Business and IT, specifically focusing on data analytics, data science, and advanced analytics. It explains how various steps and processes can be integrated under the broader umbrella of analytics, providing a foundational understanding of how these elements interact in the business and IT sectors.
35:00 - 50:00: Dashboard Creation Case Study This chapter focuses on the exploration and discussion of analytics and business intelligence, emphasizing deeper insight into the ecosystem of data. The speaker, presumably Tara, will provide hands-on experience on syncing, and promises to delve deeper into the terms and processes related to these fields.
50:00 - 55:00: Optimizing Analytical Solutions The chapter titled 'Optimizing Analytical Solutions' focuses on integrating analytical solutions into decision-making processes within business settings. It emphasizes understanding data-driven strategies and how to conceptualize decision-making rather than delving into technical specifics. The aim is to provide a framework for thinking about the role of analytics at a business level.
55:00 - 59:00: Understanding Business Content in Analytics The chapter 'Understanding Business Content in Analytics' delves into the role of analytics in providing insights that support managerial decision-making. It highlights the expectation that despite the availability of analytics, only 20% of insights will result in business outcomes by 2022. This points to the challenges in translating analytical insights into actionable business strategies, emphasizing the importance of effective monitoring and discussions.
59:00 - 69:00: Dimensions and Measures in Business Analytics The chapter discusses the usage of reports, dashboards, and data visualizations in business analytics. It highlights a surprising statistic that only 10 to 20 percent of these tools created are ever used, which is seen as an astonishingly low figure. This points towards inefficiencies and maybe the need for better alignment with business user needs.
69:00 - 74:00: Importance of Stakeholders in Analytics The chapter discusses the significance of stakeholders in analytics and highlights the underutilization of analytical solutions. It mentions that a staggering 80% of analytics insights do not yield business outcomes, according to Gartner. This emphasizes the importance of effectively involving stakeholders to fully utilize analytical tools like dashboards, reports, tables, and data visualizations.
74:00 - 84:00: Transition from Business Needs to Requirements The chapter discusses the hesitancy seen in businesses when transitioning from identifying business needs to specifying requirements. It highlights the fear and perception that gathering and utilizing data for business benefits is a significant step, often attributed to a lack of resources. This perceived deficiency of resources is considered a primary reason for the slow transition or reluctance.
84:00 - 95:00: Predictive vs. Adaptive Approaches The chapter discusses the fear and hesitation companies might feel when embarking on new projects, particularly those that involve data collection and analysis. It highlights that starting new initiatives can feel overwhelming because they represent a significant shift from established practices.
95:00 - 102:00: Direct Interaction and Collaboration This chapter explores the challenges of data analytics, emphasizing the communication gap between data scientists and business professionals. It highlights the complexity of data analytics and the difficulty in establishing a common language to effectively utilize data between these groups. It underscores the necessity of building a shared understanding to bridge this gap and enhance collaboration in utilizing data analytics for business purposes.
102:00 - 120:00: Identifying Needs and Requirements The chapter titled 'Identifying Needs and Requirements' addresses the challenges and complexities involved in recognizing and understanding needs. It highlights the importance of a common language in bridging the gap between different perspectives. The discussion acknowledges the fear and apprehension that might arise from uncertainties and misunderstandings among people, pointing to these as previous issues that have been encountered. The chapter underscores the need for clear communication to overcome the barriers posed by different languages and complexities.
120:00 - 142:30: Concluding Remarks and Q&A The chapter delves into the common fear of confronting new experiences or concepts. It highlights an important point about understanding decision-making processes, referencing Daniel Kahneman's book 'Thinking, Fast and Slow.' The discussion encourages readers to not be intimidated by unfamiliar situations, emphasizing the value of embracing new challenges.

Analytical knowledge 2021 03 19 Transcription

00:00 - 00:30 i will record it it's working so uh so for the analytical knowledge of business content to enable analytics maybe just the first question to you i hope we can have some interactive sessions not to have like three hours of flow from my side what do you consider as analytics you know it might be slightly challenging uh i've mentioned that several times so
00:30 - 01:00 any guesses what we might include what should be in analytics topic feel free to unmute yourself or you can also put it into chat if you feel more comfortable with that try to monitor that any guesses
01:00 - 01:30 okay so maybe let's skip it open this question i will introduce you the aim of today's lecture and what i would like to discuss you to show you how to be more efficient if we are talking about analytical knowledge and also provide some prerequisites for the positions of being analysts or let's say consultant
01:30 - 02:00 if we are talking about applications that are supporting business and strategic decision making so today it's going to be a lot about decision making about uh supporting business and and talking on this level of the course uh as we were discussing together we said at the beginning i guess like digital transformation is definitely uh part of this or or maybe it's it's like analytical knowledge
02:00 - 02:30 analytical understanding that is connected to supporting decision making using data is really connected to the topic of digital transformation so i will try to give you as much as i can both from my academia from my world from my from my commercial practice so feel free to ask and then jump whenever you do have the feeling that it's not clear it's definitely good to discuss that and for for the agenda that i'm such a
02:30 - 03:00 thing i would like to see some kind of like types of projects that we are talking about and also discuss discuss analysis problems in practice and analytical knowledge of business content to start as i was talking about analytics but if you google that it's not super easy to find the world analytics and have and find one single definition
03:00 - 03:30 or one single understanding basically and i like pretty pretty a lot like gartner approach it's some kind of like catchy term for a variety of different business intelligence and application related initiatives so anyone who is not aware of business intelligence i and i should explain that feel free to to to speak up
03:30 - 04:00 this is all new to me so please anything good good good to hear that thanks for this comment so so uh business intelligence is we do understand that it's taking basically data mainly let's say internal data but it might be enhanced for external data from from various data sources which might be
04:00 - 04:30 customer relationship management system enterprise resource planning finance hr system whatever systems and and taking this data into decision making processes and to be able to do this we need some kind of set of tools procedures applications software people and other types of resources so basically if i'm using like business intelligence is some kind of mixture of procedures
04:30 - 05:00 tools resources people to be able to use data and implement them into decision making processes and enhance and make decisions better basically that's my understanding of business intelligence and analytics is more i would call it more general term that is either including business intelligence itself but also other other applications
05:00 - 05:30 or other spheres of of the world of data analytics such as data science and advanced analytics and all these steps basically we can cover under analytics right does it make sense somehow yes good anyone who would like to hear more explanations on this level
05:30 - 06:00 and we will continue with the discussions discussions related to analytics and business intelligence today so we will we will go these um terms through and we will go deeper today later today okay good so i i guess tara is going to show you also the whole ecosystem of data and and so on today i will probably like give you some more hands-on experience on like syncing
06:00 - 06:30 and the ecosystem implementing it into decision making processes not showing you technical details but really giving you some kind of background what you can expect from this so i'm not like going to for within this lecture to talk about like technical details but staying on like thinking how to think about data and uh basically decision making processes and analytics in business level and
06:30 - 07:00 what's interesting if you see like basically analytics is giving you some insights into the companies supporting managers with data underlying data for for decisions and if you see like expectations which we can see that only 20 of analytics insights will deliver business outcomes through 2022 it's super crazy if we were doing like monitoring and having discussions
07:00 - 07:30 with business users how many percentage of reports and dashboards and basically some kind of data visualizations are used unlike ever used we can get on pretty similar number it's like 20 to 10 maybe 10 to 20 percent that is ever used which is super crazy so meaning we are creating
07:30 - 08:00 analytical solutions and if i'm saying analytical solutions in the super easiest way it can be dashboard it can be a report or just just some simple table or visualization of data and we are saying that up to or we are saying that 10 to 20 is never used it's super crazy and if you and did you see you see this confirmation coming from gartner that only 20 percent of analytics insights will deliver business outcomes it's
08:00 - 08:30 really crazy why do you think this happens maybe people are afraid to use or start gathering data and using it for the benefit of the business they think that it's a big step to start doing something like that and they don't have the resources okay okay so and and like missing resources you do have the feeling that that's the main reason being afraid or
08:30 - 09:00 are there any other reasons why they afraid of that well maybe just um starting something new if the company hasn't gathered before this kind of data they're starting something new so maybe that is like feels too big of a step for them to take perfect super super great point and do we have any other points i think one one uh point is also
09:00 - 09:30 or what i have come across is um uh data analytics is seen as too complex and that there's a sort of gap between for example data scientists and business for example that they don't share a common language it's difficult to really build a common understanding of how to actually utilize data
09:30 - 10:00 that yeah perfect perfect so several parts complexity as the first one that's really important and i'm going to talk about it shortly and not having common language that's exactly why we are having this this lecture so thanks for this point so we do have complexity we do have different languages or not understanding uh different worlds and and we also have the previous that we've discussing that people are afraid of
10:00 - 10:30 that being afraid of something new right any other points really good points because we i will jump back to the first one being afraid if you are thinking how people are deciding like maybe you've seen like thinking uh fast and slow or how it is called the book or if you are just just like just try to
10:30 - 11:00 sing how people can can sing or how they can make decisions it's like they can start from their intuition meaning if i'm going to buy i don't know brad i just see that and i like that i like the shape i i do have good feelings okay it's going to taste good and i'm just going to buy that i'm not checking like how big it is whether it's more like 50 grams bigger or not or whatever
11:00 - 11:30 so i'm just like having the good intuition this is going to be good right i do have some kind of experience and and also intuition that's that's one extreme and on the second side of the river of this decision making processes we can see like data driven which are decisions that are fully supported by data and i would say only supported by data automatic systems so if the if we are having like air conditions in the in the room
11:30 - 12:00 saying it's going to switch on if we have something like 24 degrees celsius or whatever right and it's not asking users are you feeling hot or cold whatever right so it's not taking that kind of feelings and emotions intuitions so that's just like two sides intuition and data driven purely data-driven decisions right and what we are saying with our
12:00 - 12:30 folks and i hope we agreed also on on this with stereo last week in the discussions it's good to like take the best part of these two extremes and really combine it to let's say data inspired data inform decisions that is supported by analytics and analytics insights right i do have two videos for you i will try to play them i hope it's going to work let
12:30 - 13:00 me share that it should be here no okay [Music] sorry give me second i will try to see i hope [Music]
13:00 - 13:30 so i will try i hope you can hear that just if you can confirm that because sometimes it's not transferring that sound can you hear that or not no sound no sound [Music] the same situation as you had
13:30 - 14:00 i can share the link that would be probably the best option yeah let's give me another short try share sound now it will work is it working it is working good good so i will play that
14:00 - 14:30 so hello uh just after semi-skimmed milk your search for semi-skim milk returns zero results you don't sell milk your search for milk returned 52 256 results
14:30 - 15:00 your top hit milk of magnesia no milk floats of yesteryear no this milking family wall planner no sorry i'm i'm just looking for you know normal milk couldn't find what you're looking for that's what i'm saying i couldn't find it so i'm asking you if you could advance that all right let's do this keyword now go uh yeah okay
15:00 - 15:30 milk please narrow your search by using one of the following filters kayaks packet 12 self cleaning breakfast and condiment that one breakfast one result milk skim semi semi skim milk that's what i first asked you for uh it's milk skimmed semi so that was the first video i do have the second one for you
15:30 - 16:00 and then we will discuss them uh the second one i will just switch on the subtitles if audio just slightly gripping yo bro you on woo-woo you kidding me everybody's on woo-woo [Music] lock and load people we're going all in on woo woo okay mark compass up a profile page susie write us some posts i'm writing
16:00 - 16:30 grace upload some videos uploading i want sponsored moves i want targeted woos we want to be all up in your woo-woo feet gordon register our woo-woo hand don't you know the uh wu-tang clan let's get them involved we need an ethnically ambiguous woo-woo mascot we're cashing in the q4 budget people and we're buying some followers [Music]
16:30 - 17:00 dude are you still on woo-woo nah man my mom's on woo [Music] good these were two examples of like let's say decision making and and using analytics let's say can anyone comment that what what are the differences
17:00 - 17:30 so i would say that it's it's about the keywords that you need and how you are going to use it it's very difficult to decide what the customer is looking for you have to be very precise with that and you have to
17:30 - 18:00 try to figure it out what is the customer's need and what do you need to do like if we are talking about the first video like shopping but you need to know what do you need to understand like you were saying needs right yeah the customer needs how how is he going to try to get the the yes
18:00 - 18:30 so that uh that's just the point that when we don't know with which uh words do our customers use when they are trying to search specific needs and we we have to try to figure it out on the sales perfect very good point very good point and i if you are not taking anything else from today's lecture i will stress this understanding the needs
18:30 - 19:00 understanding the needs right really and any other comments to the first video yeah i think it's about narrowing down the selection of because you know what your customers are coming in for then you don't have to give them everything there is in the world you know concerning selection you can narrow it down exactly so like using
19:00 - 19:30 how we can call that common sense would i would say maybe giving better experience to the customers right so if i should like put it in the words that i was using before i played at videos i would probably say this was super data driven just just taking data right i do have some kind of keywords and then having the focus on on particular keywords without any other environment like
19:30 - 20:00 needs sentiment and other stuff so really super narrow analysis that is saying give me the keywords and i will i will give you the results nothing else basically right what about the second video how do you see that
20:00 - 20:30 well that's the that's main point of targeting the media in time that uh what is the channel what is the correct channel what is the right channel for your for your audience you know when if we are marketing in in facebook but our customers are not in there so we are all late we have to be on time and try to figure out
20:30 - 21:00 the clients of the customers we are reaching for where they are what are they spending what is the what is the goal for them so we need to know where they are and if we are late then like like here as we can see that the youngsters are or some those are very pretty pretty difficult uh customers to reach yes
21:00 - 21:30 easier to keep some other groups how they were deciding going to [Music] how they decided to go there uh there was there was this rumor i think yeah just just like staying in the lift right basically and hearing hey there was no data basically nothing and compared to the previous one right that was just
21:30 - 22:00 data driven and now it was just just a pure intuition or some some kind of rumors or something like that how they can make it better this this kind of decisions and and as you were saying uh being more on the edge especially for the second situation what they can do to understand better you have to research and study and ask of course and if we are talking about these young guys how we can do that like you were saying
22:00 - 22:30 research study fully agree with that anything any other comment any other suggestions for these two let's call them corporate guys how they can integrate this young target group there is a there are analysis that you you can research and analyze where
22:30 - 23:00 what these youngsters are using but yeah what about bringing these youngsters as you call them uh bringing them into the culture into the company yeah it's just saying hey guys let's visit us yeah let's go with us and then you can work with us right on this because to have some kind of more analytical thinking from their side because if we are targeting on this particular group let's let's take them really and use
23:00 - 23:30 that within within analytics right good really good thoughts thanks for this i do have some some statistics regarding project reality success and failures and this is considering all i.t projects anyway going from pmi uh as the project management institute all the links are going to be in comments i'm going to send you the presentation uh so you can find that and search for more details but if you see that we can say that just 70 percent
23:30 - 24:00 or something between 60 to 70 percent are meeting original goals it doesn't matter too much whether it's purely iot project or analytical project right and if we see a number of projects that fail that you can see by the dark orange on the screen uh by this color it's something like 30 to 40 so basically substituting to projects that are failing and they are failing on let's say budgets but if we
24:00 - 24:30 see other failures we can see like deemed failures we can see like experience scope creep and other types of failures so and as we were discussing previously and then you had really great points if i say like id and dna like data analytics dna stands for basically failed due to lack of functionality complexity and performance limitations so these are
24:30 - 25:00 like top three what we've seen across industries across research and you know what's challenging it's it's challenging that the problems that we are talking about like like of functionality performance limitations complexity are linked to the quality of analysis and design and critical thinking right so just try to imagine the projects maybe
25:00 - 25:30 you do have the experience from the industry and like quality of analysis and i'm not saying that we are talking about agile projects we are talking about waterfall projects it doesn't matter it doesn't matter which kind of methodology we are using whether it's scrum whether it's print stew whatever it's like really if we see the problems very often like it's linked to quality of analysis and design because imagine we are talking about complexity it's challenging to understand the whole
25:30 - 26:00 complex world and the quality of analysis supporting that right does it make sense what we are discussing here yes it it does good good that's good thanks thanks for this uh i do have one task for you but just before we jump there a few more informations uh regarding analysis and you've already
26:00 - 26:30 mentioned that if i see it from like type of knowledge i would say there are like two groups the first one talking about i.t oriented knowledge describing the whole i.t world id methods methodologies framework tools describing of description of i.t products services and other parts of basically active world and on this on the other hand we do have business oriented knowledge something
26:30 - 27:00 like management methods methodologies understanding of kpis seeing that this particular kpi is having this formula seeing organization organization structure right so and very often even on on the like university level we are teaching students let's say on business oriented way and an i.t oriented way right and they are getting super nice education in i.t
27:00 - 27:30 and other folks are getting really good education in business and and both of these are being educated on like methods methodologies everything and getting like understanding their work which is making the challenges that that we do have like business knowledge that you can see on the left side and we do have iit knowledge basically like id word on the right side and as you were correctly saying at the
27:30 - 28:00 beginning of the lecture we are missing some part of like analytical knowledge of business content especially if we are starting new projects and people starting working on like analytical insights iit projects so we have identified that there is some kind of solution of knowledge gap and we need to we need to really find a solution on that so we see that there is there is really gap regarding analytical
28:00 - 28:30 knowledge of business content and this is the place where we are mixing business knowledge and i.t knowledge right so our aim with the folks and is on our board as well it's really to show you the combination of both worlds like business knowledge giving giving you some kind of business background
28:30 - 29:00 and idea knowledge and giving you proper business content and analytical thinking on this level so that's that's why we are talking about this and you have indicated that really correctly if we are talking about let's say business content knowledge and methodology knowledge it's something like 80 to 20 and business content knowledge is really important because you can have really great
29:00 - 29:30 methodologies i've already named scrum prince2 whatever right these are really great concepts and i'm fine with that even like being prince2 certified so i i don't like disagree with them but it's it's like some kind of methodology or toolset that we know that but you need to have some kind of sense of the business what the business is doing and why we are doing basically as you were saying
29:30 - 30:00 these are the needs so from our experience from the case studies that we've done should be something like 80 to 20 you can pretty easily understand the like methodology knowledge and and get it right just just like you can you can go through prince2 or other project methodologies you can quickly like understand that but understanding the business content is more challenging
30:00 - 30:30 i do have the case study for you imagine a customer business partner is coming to you and he is basically saying can you please create the following dashboard for me just that's that's like i would say pretty normal situation that i'm facing almost every day really like customer business partner is coming to us and saying hey guys we would like to have this kind of dashboard right and now i would like to ask you to
30:30 - 31:00 work in small teams let's say for 20 minutes let's see how it is going and try to specify what questions you will ask so you now are going to be the delivery team or the plus a provider or vendor that is going to deliver or the aim of for your teams is to deliver the dashboard the solution right and i would like to see what are you
31:00 - 31:30 going to ask just just realize the situation is that the customer is coming to you or sending you an email and saying can you please create the following dashboard for me and what would be good if you can really specify the questions you will ask for or whether there are other information that you will need and you will see and you will you basically want from from the
31:30 - 32:00 customer right and just uh i haven't checked how many folks we do have on the call 35 so we will probably have more teams that we will have uh time for uh so be ready to present and we will we will choose several of you after that does it make sense prior i'm going to show you the dashboard questions
32:00 - 32:30 okay looks good so that's the dashboard right basically human capital management is the business partner was coming to you and asking can you please create this dashboard for me now i would like to really see how are you going to what are you going to ask for so i'm
32:30 - 33:00 going to prepare the breakout rooms for you uh how many breakouts we would like to have something like nine three to four people on the call on within the breakout rooms is it fine for everybody yes good
33:00 - 33:30 [Music] it's okay okay for me i can i can be wherever okay you were assigned the room too so i will i will jump from the room to room and see how it is going okay i'm opening that uh let's say 20 minutes we do have uh we do have uh
33:30 - 34:00 four days so to do to do the whole and and we will meet each other i will be jumping from the room and i will definitely call you back
34:00 - 34:30 so who is going to present first i see really good progress while i was
34:30 - 35:00 checking your teams really good so so i'm really looking for that i will share my screen back with the with the dashboard and really i haven't probably stressed that when we were here together but i will i mentioned that individually to every team that the task is that the customer is coming to you with this kind of dashboard maybe he might be just draw it by pen and pencil or whatever
35:00 - 35:30 and your task is really to create this you will be discussing that with staria what does it mean what type of analytics it might be and all the stuff around for this phase just would like to hear the like steps the questions that you are going to ask and the information that you need using you need for the creation so any voluntaries
35:30 - 36:00 okay can you continue because my voice is not feeling good who is going to start tony or someone from the group um i can share can you hear me
36:00 - 36:30 yeah yes great so i will share the screen from here we we don't have a beautiful visual presentation because of technical difficulties with powerpoint but we made some notes do a screenshot um i can't share okay now i can
36:30 - 37:00 here you go it's not beautiful but can you see it perfect perfect so uh we took a screenshot from your presentation and uh wrote down some notes uh to it uh we started with finnish but moved to english afterwards so the first question uh when starting the project would be what's the schedule uh when do you want
37:00 - 37:30 it done and so when we have that that thing cleared out uh before going going to the dashboard uh we would like to know the actual contact uh persons regarding the project because when we come come down to the systems involved and watching this uh dashboard visualization
37:30 - 38:00 uh we have we have some questions about which systems are involved uh we we think about hr system uh based on this data here uh probably we need some data from erp system also and some point some kind of uh employee nps or or such uh satisfaction survey system so so we have a question about
38:00 - 38:30 we think there are many systems involved so probably there there are also uh many contacts uh involved so we would like to know who are uh responsible of what uh in in the customer's end perfect and when it comes down to this visualization i think uh most of these uh charts are quite uh self-explanatory but uh
38:30 - 39:00 this uh third chart raised some questions about what is this chart actually showing and should we make some changes to it so that uh the reader can understand what it what it is showing so you mean employee satisfaction survey our status yes yeah so because we we didn't quite understand what's the reason of this pie chart here
39:00 - 39:30 and some other questions uh regarding the dashboard what would be the update interval of the data here and also regarding that uh are there uh apis available to these systems is it manual updating how should this uh integration be be done who is responsible about that and
39:30 - 40:00 and also uh who where should his dashboard be hosted uh does the customer have uh some existing system which we should implement or are they available for our suggestions about the system and also are there some needs uh besides this dashboard uh should we be able to export the data and
40:00 - 40:30 do something else with it also and one more question is there already some kind of data warehouse that we can use uh for this chart so the here are the first things that that that we discussed about this chart okay really good good source uh thanks for them and making the notes of the questions uh really thanks really appreciating how you are thinking of that
40:30 - 41:00 i'm sorry guys sorry don't worry don't worry so really good work uh thanks for this any other themes anything to add what we've heard i can show room sevens good thinkings good so here can you see this
41:00 - 41:30 so there are the questions that we were thinking about behind this dashboard so in in what shape and format this data is now uh where are we getting it from where is it now stored how are you going to use this dashboard and is is this the or what are the visual ways the data is one want to be shown and how you want to categorize different sections like you have here the americas and
41:30 - 42:00 asia pacific and such should this be more insightful do you want to go deeper on certain information what is the time frame the data is collected and the time frame how it is wanted to be shown and who are the people who get to see this and are there some limitations on what different user groups are allowed to see and what is the goal in using this data and getting the data into information in real time
42:00 - 42:30 so those are our thoughts about this yeah i hope i'm pronouncing your name correct really good thoughts really good thoughts um honestly speaking evid i really like your questions regarding how are you going to use this dashboard what are you expecting to be the answer can you give me some examples um
42:30 - 43:00 does anyone here in our group have have an answer just doesn't have to be precise what they are going to tell you but just like what type of answer can you hear from well maybe in some like uh like in some kind of meetings um going through the well there's a lot of information about the employees and their like employee satisfaction and
43:00 - 43:30 maybe something for the hr to go through so maybe uh let's ask let's because they you know sometimes it happens like you are asking the customers and asking them how are you going to use this dashboard and they are saying okay i'm not sure right and they might not be sure so you can like ask for example for the processes and ask them which kind of processes are you going to to use this dashboard
43:30 - 44:00 in right or or see how are you going to decide based on this dashboard what if you see the headguard and the headcount is under target what are the actions that you are expecting and you can like basically give additional questions not guiding them or not giving them the answers but asking the additional questions and the cases are very often helping really like what if you see that the
44:00 - 44:30 head gown is under target what are the actions that you are going to do are you going to to call someone who is responsible for that would you like to see some more details and so on right so really good points yeah and yeah i will probably give give time for for other team because this this is really good any other volunteering team
44:30 - 45:00 i don't want to pick any well i can share good there you go so we took the same screenshot uh we discussed a lot about uh what kind of a problem should we be solving
45:00 - 45:30 with this data what kind of users will be using as the previous uh what would be the timeline for this project uh how would this be monitored after projects this was mainly we talked about how to use the data and how to analyze it after the project is it continuing longer or is it just one time
45:30 - 46:00 data how to make this data to visually present technology of the project so we saw a lot of data about how to use it in knowledge to make it work for the people that it is made for what are the data sources as the previews and targets versus kpi a key performance indicator so that was our questions and wonderings good really good points really good
46:00 - 46:30 points guys uh i see that you're really advanced how are you thinking of that it's really great to see this especially what i would recommend up here and and we are going to talk about that um i've just added uh one slide to my presentation so i will share my deck back just give me a second it's here yes so i've just tried to
46:30 - 47:00 summarize that and just quickly put it there and like spread it into two pieces like how are you going to use this what are the use cases or scenarios basically and we have also heard what kind of problems should we should we be solving and should it be more insightful really like seeing and analyzing the problem that's that's from my and really having some kind of analytical knowledge that's what we are talking about seeing the needs right that's really
47:00 - 47:30 important and i would really stress that and putting this kind of information above data because if you start asking about data very often like people are starting okay we don't have this kind of data maybe we will like need to go like that which is challenging and they will start thinking more on technical way like business intelligence if i we we've defined as together and business
47:30 - 48:00 intelligence is here with the word business it's business intelligence first is business right so very often we are saying that these like teams should not be sitting under i.t they should be really like very close to business and they should be serving business decisions and enhancing that so starting the questions like how are you going to use that what's the actual problems that you are aiming to reach that's the perfect like aim how to start
48:00 - 48:30 that and how to think of the situation because then you are able to analyze that but please the the the right part is also important and it's necessary to do it together because if i'm just coming to you as an analytical team and you are the customer right now and i'm making these notes to my whatever right and just just like drawing that and writing this is the definition
48:30 - 49:00 then i can very often discover that i don't really have the data and i have to spend another year creating data warehouse or whatever to be able to provide this so the left and the right side have to really go together really it's necessary that you are like starting from the business issues and then going and doing i would say almost in parallel streams and asking them for details probably not the same persons but their
49:00 - 49:30 teams regarding data regarding update intervals measures data warehouses whatever right dimensions and all the stuff around you will be talking about this during following classes but really starting from that does it does it make sense but it's fine with this yeah it does good really really good work i'm super happy for your swords
49:30 - 50:00 and when we and and just like to add because i've seen that that you can like really think that from business perspective uh when we are talking about business and management knowledge we are saying that it's necessary to see all aspects and it should be like specified and analyzed not only from business and management viewpoints but also from analytical viewpoint
50:00 - 50:30 so coming back to the dashboard there were questions regarding uh would you like uh that was the third question should it be more insightful how would you like see the granularity or some kind of these questions uh this is exactly the analytical thinking so i'm not giving you just just the formula some team ask me the meaning of attrition rates like that's that's another good question right what does it mean and then you
50:30 - 51:00 have to start thinking like what's the impact and can we if we are saying it should be more insightful can we aggregate it on particular levels so does it make sense if i give you attrition rates for the whole company or should i always see it just by departments because it doesn't make sense and we are never using this as the whole number comparing to headcounts right so these are real analytical solves that you should
51:00 - 51:30 proceed so how are you aggregating this and how are you working with particular measures and dimensions for for the solution that's necessary to really ask and work with that i guess we should make sure break guys right because i haven't made any break and it's almost like one hour 20 from from the beginning uh so what about making short break because you've done really great work here so 15 minutes 10 minutes is okay for me
51:30 - 52:00 15. that's fine okay so let's meet at the you are having 10 right 10 35. and so we basically end up after
52:00 - 52:30 your case study and your really great questions and with the business knowledge to be able to let's say understand that we are saying there should be some kind of corporate anatomy understanding or field of studies and field of approaches that belongs to corporate anatomy basically we can you can see some kind of parallel in anatomy with like human being
52:30 - 53:00 but this is for for corporates right but we think that we might apply similar principles as for for the human being but just for the corporate and for for business environment and as we have already started discussing it's good to understand business content uh but also to business context and after that do some kind of optimization i will show you business content and
53:00 - 53:30 after that context and optimization uh so starting with the content business content as the first part hope you all can hear me yes good good just just don't want to talk on mute so we are having some fun with with the folks in on my projects who is going to talk uh longest period on mute
53:30 - 54:00 so i don't want to complete this over here so from the business content perspective we are going to talk about processes scenarios metrics factors roles data and documents methods applications and dimensions so pretty several parts of that but basically following what we are already talking so starting with the processes and tasks as you probably know that process is some kind of sequence of steps leading from a defined problem to
54:00 - 54:30 results and what's important that processes should be aligned to business strategy business content and vision mission and goals right that's the reason why we are having like processes on the general level we are definitely going to have like core processes supporting processes and other types of processes but it's good when imagine the task that i was giving to you start thinking from the process perspective like imaging and asking the clients you
54:30 - 55:00 probably do have some kind of processes can you describe them and tell us where it is sitting so you do understand the process and based on this you are able to then derive the usage of particular solution right so and process is giving you business content understanding business goals strategy and vision that i was already mentioning and of course it's about granularity of
55:00 - 55:30 processes this is not a process lecture but we probably all can agree that processes are having different granularity and different maturity uh not probably all processes has to be on the highest level depending on particular solution and this is also important information for your analytical solution and analytical thinking because if we are talking about processes of serving customer in fast foods it's probably going to be super matured
55:30 - 56:00 like optimized if we are talking about creating some marketing campaigns in some some agency doing that like thinking and creative part of that it's probably not going to be super described and measured on individual elements right and while you are analyzing that and designing the solution you really have to understand like type of processes it's granularity and maturity so this is
56:00 - 56:30 really necessary to to have in mind when you are asking your clients about this and uh this is just just to sum up that probably each task and and not probably about each task and the process is described by purpose key activities and critical success factors and this is very important very important to think of this because i was giving some hints to one particular team that asking
56:30 - 57:00 the clients what's going to happen if we you are below targets are you going to call someone are you going to ask someone to do extra anything extra or really how are you going to behave and this is really connected to critical success factors of individual processes and this brings me also the idea about ownership of individual measures of individual
57:00 - 57:30 solutions attrition rate that we have already discussed is good to ask about the meaning who is owning that because if we are saying that martin is the owner i would probably need to be fully responsible for that and and be sitting on the business side so imagine the situation that i'm having very often the discussion with the teams on commercial projects and they're like i was in the situation they were saying you should be the owner
57:30 - 58:00 of the dashboard it was the owner for customers and i was saying honestly speaking i don't mind being the owner but i should not be because i don't care whether there is going to be five for this measure or six i don't have this is not my process this is not my goals this is not part of my job right so critical success factors and activities and content of processes of work of individuals it's really
58:00 - 58:30 important to analyze how they are going to behave and how they are going to use the solution and based on this you are able to draw the scenarios so basically all the questions that we were doing should be leading and related to processes and providing comprehensive some kind of checklist or should be defined for common management fields meaning on pretty general level if we see that from from management's management fields or they should be like
58:30 - 59:00 specific for individual segments individual cases individual scenarios for for people and here we are really asking what are you doing with that is it like you are hiring based on this if i'm taking the case that the to you were analyzing is that you are firing people are you going giving them bonuses based on their like feedback or or bonuses to their managers that's completely different and you need to do the analytical
59:00 - 59:30 thinking of that and analytical work to be ready to answer this as you can see this is the recommendation on how to tackle the scenario and how to go what are the concrete steps basically that you should take of that and based on this you are really able to prepare the solution that is going to work and just just know that analytical
59:30 - 60:00 questions could be effectively used like in meetings between analysts and clients or users to increase some kind of efficiency of that process so the questions that you were preparing are really supporting the whole efficiency and especially if like you know what it's going to be challenging very often if a customer or business partner is coming to you and saying please can you draw this
60:00 - 60:30 can you prepare this dashboard this report for me and i've been in this situation they and you will start asking the questions that you've prepared and are really good they will not know the answers honestly speaking because they've received the task from their managers they haven't asked or they didn't get the answers so they maybe asked the similar questions to their managers but they haven't received the answers and they are not going to tell you hey
60:30 - 61:00 guys we don't know so it's going to be challenging honestly speaking but just this is the only way how you can get to the working solution i was in this in a situation when a customer was coming to us and and asking us for for a super huge table meaning like 20 columns millions of rows and we were saying and we were not ready from technical perspective right we were not able to do that basically and we were asking them what
61:00 - 61:30 are the scenarios how are you going to use them and it was super huge discussions because people that are that that were giving us the task they didn't know that they they basically received that or they didn't want to tell us because there were some reasons for that they received for example funding for just one table just one report that was just just the founding but they need to serve two departments so they've put
61:30 - 62:00 everything together and at the end we were having really huge arguments can you tell us and we were really like analyzing very deeply and asking their scenarios how they are using how they are going to use that what are the processes where they are integrating them and based on this we were able to really discover that we were serving smaller pieces it was more than one but supporting their needs and we were pretty happy because we didn't have to do any huge changes
62:00 - 62:30 on like background and backhand part but really it was not easy discussion so just be ready it's not going to be the discussion and metrics very good questions that i've already mentioned what does it mean right and so so we are saying okay we do have some kind of measures metrics kpis pgis pri's whatever like performance indicators go indicators resource
62:30 - 63:00 indicators or results indicators whatever but like what's important is to understand like objectives exact definition source of data possible calculations and analytical functions and set of practical recommendations for use in business analytics and this is really key of that because if we are saying we would like to measure this we need to know why we are
63:00 - 63:30 measuring that where we are going to get data from and how we are going to calculate that and what we were discussing is about granularity are we able to aggregate that does it make sense if i aggregate it like if i say okay we do have five employees in the whole company and saying okay for these particular departments we do have one two two that makes sense to aggregate that but some numbers are not really making
63:30 - 64:00 sense to aggregate that or people are not going to use that right when i'm going when i'm talking about measures i will probably jump to do i have it here sorry for jumping here dimensions sorry i haven't put it together can anyone give me the meaning of dimensions i know this is your first class of the
64:00 - 64:30 course so and like maybe you don't have id or or bi background but anyone can give me some examples of dimensions dimensions are used at least in google analytics okay what what might can you give us some some like dimension like uh particular examples well at least there are of course
64:30 - 65:00 be custom dimensions used i i have worked or my client has been a big finnish aviation company so we have had a lot of custom dimensions used which are particular in uh in the when you book a flight okay
65:00 - 65:30 can you can you give us some simple example of dimension just just like name them one two three anyone who can help us completely blank don't worry don't worry so basically i guess we all can agree
65:30 - 66:00 about the meaning of measures right saying like having profits individual numbers number of sent emails if we are talking about marketing campaigns number of employees that's the measure right and if i'm talking about dimensions because measures are very often going to dimensions as we had heard in indo analytical tools like what was that google data studio or something like that so dimension is showing some kind of
66:00 - 66:30 view on measures view on data anyone can give me one example of that it's like uh meaning like accuracy or or some kind of uh it's some kind of like parameters of data i would call probably that okay and all this stuff i can give you an example
66:30 - 67:00 so i'm a book accountant and we are using in our customers they are having these projects and when i use this dimension number it gives me the values or the data in the project so you can have so there is a big data and then there's a smaller data and the dimension is the one that you can split the data yes so
67:00 - 67:30 projects might be dimension right some more traditional or this is a good example projects i will give you some more or maybe slightly easier for understanding so imagine you are having number of employees to staying with the same subject era that we have started so number of employees is is a measure right it might be kpis it doesn't have to be but basically some kind of measure in dimension might be time so based on this i can analyze
67:30 - 68:00 number of employees in march or in february right so dimension is is time basically view on data how i see that so i do have like measures this number of employees and i see particular like time frame time is dimension here or projects like number of employees working on that individual projects that's
68:00 - 68:30 projects is also damages or very often companies are analyzing like geography or they're like regions so let's say lacte is or geographical dimension and i can see number of employees in lahti right something like that or products so i can see number of employees that are delivering these products or are working on this this particular products
68:30 - 69:00 and again for for dimensions you need like objectives you need content data sources particularly calculations of individual elements because there are some hierarchies that's that's probably lightly out of today's lecture but just just for now and very often we are like combining measures and dimensions together does this make sense what i'm explaining right now
69:00 - 69:30 yes it does yes good good so just just see that basically from from the analytical perspective try to see what we are measuring and from which perspective we are seeing that because based on this you are able to match it to processes and and two scenarios that we were talking about sorry i will jump a few slides back uh we were talking about measures uh factors pretty important uh element this is something that is
69:30 - 70:00 influencing the whole solution it might be for example industry type or some kind of regulations imagine that you are working for a regulated company like healthcare some health care facilities very often regulated by laws international laws or original law right and this is going to impact the whole like business management analytical solution
70:00 - 70:30 and also the content so for example when we were talking about attrition rate and having it for practical departments i've seen countries that were not allowing to compare individual departments from performance perspective they were saying it's illegal or they were having some like parts of the companies that were saying you can't do that
70:30 - 71:00 right and and basically you have to sort them alphabetically and that's also part of the analytical thinking because you need to be able to really give the full solution that is going to work so that that are factors uh from from from this perspective rose you've named them like you were very correctly asking for stakeholders they are super important because they are going to
71:00 - 71:30 give you like money going to approve the whole solution if you are talking about like q users uh the main stakeholders so they are like really important and it's good to understand that because if you don't know who they are you you can deliver that honestly speaking on measures and definitions you can spend ages ages right and you need someone who is going to
71:30 - 72:00 approve that i was just thinking whether we can i can give you some some some example right do you want to have one example about that how we can do that exactly good so it's going to be a bit technical like marketing conference right and in previous years marketing campaigns were like sending
72:00 - 72:30 emails maybe some some of the companies are still doing that and some of them were measuring number of delivered emails can you give me the example about the meaning of delivered email how you can discover that the email was delivered how you can know that the email was delivered some id guys on the call well
72:30 - 73:00 sometimes you get the bond emails that it can be hard bones or soft bones but after all uh if if they are not receiving this kind of email back then we can assume that it was it was going through perfect really really good example because we are saying we've sent five emails and for two of them we
73:00 - 73:30 receive bounce right so you are saying that three emails were delivered that's that's what what we agreed and for for the stakeholders they can agree and they are fine with that there might be one more definition of or or maybe two of them can can you give me maybe one more thought about this what might be other approach if the
73:30 - 74:00 email was clicked then it was released perfect perfect so if there are some kind of actions doesn't have to be click very often we are measuring open so if we are saying we haven't received bounds but we know that there were some particular actions like opening email or clicking on particular elements we know that it was it was delivered right or you can be more technical
74:00 - 74:30 and say if we have received a confirmation from the server that it was delivered you know what's the issue with this and this is super technical that's super technical uh it's not mandatory to send it back that it was received so the smtp protocol is not saying you always have to send it back
74:30 - 75:00 and and the like receiving protocols of emails are not requiring that some servers are doing that but that's completely different measure if we define it in this way and you see that we have like three different measures the defined thanks for this source really good so and someone has to say which direction we we are going like because we had still like number of delivered emails but the
75:00 - 75:30 numbers are going to differ and someone has to say okay this is the right definition so that's the reason why you need to have the definitions of roles because they are going to approve that otherwise you will be spending hours ages like weeks years and just like saying this is wrong or bad none of this definition is wrong no really none like that they're all correct it just depends how we are seeing that and really thanks for the cooperation
75:30 - 76:00 because you see that just within a few minutes we prepared three definitions totally different definitions of what number of delivered email means and we will get different numbers believe me i've seen that so that's this and very often people are saying this is wrong definitions i'm not i'm saying them always it's not wrong it's just like different understanding for different draws so just just be very careful about roles and
76:00 - 76:30 knowing them at the beginning good so these are all data documents we were asking for that just really think of that for for the case of like marketing campaigns regarding emails we're probably going to have that kind of data sometimes you have to get there manually and it might be challenging regarding data quality regarding analytical understanding and all this stuff run so it's it's good to understand
76:30 - 77:00 structure and content of data methods we were talking about them when i was like describing business and it world and that's important to to understand and applications just slightly technical to see like systems resources and applications we have already discussed about dimensions and that's about content now it's important to understand context basically relationship
77:00 - 77:30 between what i was just right now describing so it's not about like seeing just measures or seeing just dimensions seeing just roles it's about how they are cooperating together we are in a company company is some kind of system right and and system is some kind of like cooperation of elements so it's about relationship between what
77:30 - 78:00 i was just right now describing and not only about relationship above individual components but also values of selected components so it's really necessary to see the whole picture right now as you were analyzing your cases and then really asking the questions so it's not about only analyzing like seeing the processes but also seeing okay we do have this
78:00 - 78:30 certain process and we need to see the values that the process are reaching right so this is this is really important to to understand the relationship within above above like the components and their values and how they are being influenced that's part of analytical work because then you are able to see that for example if i just pick dimensions
78:30 - 79:00 and measures very often we are like drawing like measures met where we are showing these particular measures are influenced by these other measures and they are having these kind of relationships and really drawing this as some kind of map that this is showing to us and based on this we are able to analyze values that that we are getting from that so very important part and as a final step we are able to do
79:00 - 79:30 optimization if we know content context based on this we can start with the optimization proper implementation in into processes and how to do the optimization several ways and you're probably more advanced than i do in this kind of optimization and business part but regarding like modeling we were talking about advanced analytics data mining predictive analytics and and really
79:30 - 80:00 other pieces that i'm not going to spend too much time on this part but it's important here you can see just like the the visualization what i was talking about on the right side you can see business content so basically like scenarios factors roles measures and all this stuff around and you do see like management
80:00 - 80:30 corporate processes and all this stuff around and you see the the relation you do have some kind of procedures like methodologies methods tools that are with standing ahead or stan standing out of the business content and context but are also important and you are going to talk about them later during the course
80:30 - 81:00 any questions okay if there are no questions important part just coming back we were talking about two different words that are basically uh causing some kind of troubles that's the reason why we are having business analysts what would you consider as the key
81:00 - 81:30 skill for business analyst some kind of skill set or key knowledge that what coming from today's lecture from your experience basically as someone who is sitting between these two worlds an endless need to seek information oh that's good that's good
81:30 - 82:00 uh this might be challenging i i fully agree endless need but they have to be able to say that's enough otherwise they will get mad with that so and lastly fully agree with some ability to say this is enough you know what i mean because we have to deliver this solution
82:00 - 82:30 very good point anything else i guess he would need to understand both worlds and the language of business and information technicians language so he can transfer the information from other to other yes yes to really being able to
82:30 - 83:00 speak both languages if we are talking about like business analytics they must be able to speak data definitely to read them and speak data right so very important to understand both worlds from like terminology perspective anything else
83:00 - 83:30 okay i can give you a comment uh you should be a very good listener if you want to be a good business analyst that's a super important point really like listening maybe even more than you are talking you know what as a business analyst very often you are in the situation when you are sitting among for example business users right as your customers and
83:30 - 84:00 very often imagine the case study that i was giving to you i would say there are going to be more than one people who you are going to talk imagine you are like we are talking about international corporates for example having different countries having different attitudes and you must be able to listen and to give them some and then slightly lead them but not be in the center of like argues because
84:00 - 84:30 they are going to have discussions and you don't want to be that person who is saying it should be in this way you should be more listening and slightly like facilitating them and not saying hey guys let's go to this direction they should take me so really listening is important part anything else
84:30 - 85:00 good and i would add their analytical thinking really like think of that why we are doing that having critical thinking what's what are the reasons and then being able to ask this it's not easy especially if you are not like senior or not feeling confident among that for example customers it might be challenging really to coming there and having some senior guys from business and just
85:00 - 85:30 start asking them that maybe it might be like dummy questions right why do you need that what are the scenarios it's not easy it's not easy but you have to be able to do this and really try to think of that i am very often giving example with the chocolate anyway you do have really good chocolate in finland imagine with your friends you are eating a bar of a bar of chocolate right real bar
85:30 - 86:00 and you are having the some kind of discussion who is eating more how would you do that how would you discover who is eating more you are having a butter bar of chocolate really like bar right and you are like cracking it and eating it with your friends how do you think of that how can you do that
86:00 - 86:30 any ideas results well you would have to make a chart and put everyone's name in it and somehow decide uh if it's based on how much you give them the chocolate and then at the end check out how how much everyone has eaten from it
86:30 - 87:00 how are you defining how much is it number of pieces or number of grams uh well i think it might be more accurate if it was number of grams okay if everybody if everybody gets the same amount okay how would you get number of grams you would have to have a scale yes so meaning you need to update the whole process how are you eating that so meaning you
87:00 - 87:30 first crack that put it on the scale put it on a recorded number and then eat it right so that's the way how you should be thinking about these business problems so you really need to maybe change the process of meeting and is the process still going to be funny i don't know right well maybe not when someone is looking at the amount of chocolate you're eating you don't feel like eating it anymore
87:30 - 88:00 yes so just just think of the value right what are the reasons maybe your friends are still cheating you and you are just buying bars and they are eating going and you do have feelings that they are eating more maybe that makes sense to measure that right but you have to agree on that that that while i was asking like are we going to agree it's going to be number of pieces or number of grams if we both agree that it's going to be number of pieces that's the common
88:00 - 88:30 agreement and that might be fine i'm not saying it's wrong right just just like that's just the way how we should be singing any comments to chocolate i'm really missing the finished chocolate just if anyone can send me a box it would be perfect so the the analysis uh and the faces like starting from the goal definition it's really important and you've seen that i've named it what's the
88:30 - 89:00 what's the goal of measuring the chocolate and and how we are eating that is it really having some goal because it's going to really prolong the whole process it's it's a super simple and as maybe you can say it's stupid example but it represents the like what we should do in the companies like really see does it have any reasoning in goal right and then see the process like i was describing okay we are going
89:00 - 89:30 to change the process how we are eating chocolate and we have added like at least two more steps in that and definitely need more resources for that and then key elements definitions so that's that's the unit how we are measuring that is it pieces of or grams and and like really this kind of definitions and identification of resources you've named we will need to scale
89:30 - 90:00 and we need some documentation where we are going to record it so these are the resources and also the links are we going to have some kind of digital scale that is automatically going to record and saying okay martin it's martin and i can select it on the scale and it's going to be automatic record or should i do that manually right and based on this we are able to choose correct methods how we are going to do that
90:00 - 90:30 is it really like manual calculation like really counting that on paper or are we going to have some automatic systems or do we need anything like deeper if we are saying that the goal is to understand who is eating more chocolate so are we really saying that's just like simple calculation or are we going to see some are we going to use some other methods maybe for this simple case that's just like calculations but for other it might be definitely much deeper
90:30 - 91:00 so applying the methods we should also verify that because it doesn't have to give us the right results and based on this we are able to prepare the results and present them to particular users and stakeholders and if we are saying okay let's implement let's let's do this as a rubric solution so implemented to information systems so you've seen that
91:00 - 91:30 the like analysis is having some faces if you see around the globe you will see different methodologies different approaches they might have like different number of steps different naming or slightly different different orders but the key concept is going to be the same right so starting from goal definition process descriptions definitions of elements and
91:30 - 92:00 then going down it's very crucial anyone who knows this picture no one okay i don't know why i believe this this is an old picture so i i believe that they know this or maybe it's too old zarya so basically it's important i will uh briefly i will try to briefly
92:00 - 92:30 uh describe that it's like how customer explain what's what's needed right and see the first and last picture so this is how customer explained that and then we are going through different steps to be able to deliver that so we do have some like project leaders they understand it in this way we do have analysis and then they design it in this way
92:30 - 93:00 we do have programmers who prepare that and they've done it in this way and we do have business conductance who were like coming to the customer and saying hey guys this is the solution that you are going to receive and then we do have some kind of documentation we do have operations and we do have costs basically how the customer was built how it was supported and what customer really needed so and as i was mentioning really see
93:00 - 93:30 the first and last picture how the customer explained that he was saying okay i would like to have these two the ropes and these three pieces of wood right and you know what's the issue here what's the issue between the first and the last picture well the customer maybe the customer
93:30 - 94:00 doesn't know what they really want what what's the best solution for them yes or they are not able to explain that properly right so maybe they they do have it in the head but they are not able to to like transfer their ideas and say them right and this is this is this is key when we are talking about analytical thinking of even
94:00 - 94:30 like business content and to have efficient analytics because having some kind of abilities to discover real needs that's the key success i know you will say okay that's like super theoretical and we should do that but how to do that i've already show you some some pieces how to get that pool and that's really important that you are trying to aim that i know it's challenging i've been in that situation many times
94:30 - 95:00 and sometimes it's really challenging but as i was showing to you there are some some processes some some techniques that you can use to get there or at least like be able to to reach that because you know what if we are like hitting the goal from the first try like really delivering what the customer really needed then they are happy and they're maybe
95:00 - 95:30 even able to play more right you probably know that if you are having some like experience from like constructions building or whatever or even some like buying some stuff it's it's like you are happy when you are delivering what you had in your head but what were the needs and and you just got it you didn't have to call back the guys and saying them hey guys take it
95:30 - 96:00 back re redo it and all the stuff wrong really so it's good to try to understand the real needs and that's that's why i was talking about and why we were having that uh scenarios because there's the same situation with the dashboard like customer might be coming to you and saying okay this is what i would like to receive but their needs are completely different right so just try to think of that
96:00 - 96:30 some folks are really saying some of my colleagues are saying okay let's deliver them this and charge them as as this right it's not fair it's not going to work in a long-term solution but as i was mentioning sometimes customers are willing to pay more if they receive the real value good source thanks for this uh i do have few more slides but i will probably jump some of them uh just just just a quick note
96:30 - 97:00 requirement what does it mean for you if we are saying we are gathering requirements and we are analyzing them what requirement means for you any guesses
97:00 - 97:30 so i do have few definitions i will jump off some of them and jump to the uh to the last two so basically it's a capability needed by user to solve the problem right or to achieve an objective so if you see it might be a condition or capability needed by a user to solve a problem or uh achieve an object and uh i like the last one it's basically
97:30 - 98:00 definition it defines the solution system boundary so saying it should be from this side to this side right if you see other definitions you you can read them later but basically very often even from ebi ba which is like international institute of business analyst uh having a babok framework that i'm going to talk about it's having uh the similar definitions like this is describe the capabilities and
98:00 - 98:30 qualities of solutions that meet stakeholder requirements provide the appropriate level of detail to allow for the development and implementation of the solution just be very careful this is tricky appropriate level of detail that was the reason why i was saying you need to have stakeholders and you need to know them because they can say this is appropriate they are part of that right you can always go one level down one level down
98:30 - 99:00 one level down and you can be like describe that describe that analyze that more more and more right but you need to find proper level as we were discussing for for skill set of business analysis and what's important here is that you do understand that there are some business requirements coming from business itself and there are there some some stakeholders environments
99:00 - 99:30 there are some stakeholder requirements i hope you can hear me i had some some message here but i hope it's working so stakeholder requirements and solution requirements and it's good to understand them really like having business requirements coming from the business side we do have stakeholders coming from sponsors project participants and we do have also solution requirements
99:30 - 100:00 that are describing individual solutions i will skip this but i will focus on this one like if i start from the business requirements so why do i want it where it is fitting that's reflecting to business goals and business solutions then we do have stakeholder requirements that are describing what are the needs so why do i want first then i do have
100:00 - 100:30 what are the needs and what do i want that's the exact solution so the example might be i need to move from one country to another but there is a river between them so why need why do i want that because i need to move there for particular reasons the need is to cross the river and what i do i want is i need some kind of bridge right
100:30 - 101:00 so that's the first part and the last part a are transition requirements any guesses what uh transition means and can you give me some examples at least i will check that that the sound is working good how would you can you give me some examples for transition requirements
101:00 - 101:30 anyone do you mean that something something just should change if anything is not changing then it's not worth to do it uh i would agree with the first part and i would vote for the first part of what you were saying so anything that i need to change
101:30 - 102:00 so for example the the case with the bridge imagine there is already the old bridge but it's not uh fulfilling my needs because it's this wooden bridge i can't cross it by car for example so transition requirements means i need to first destroy the old one and prepare the surrounding for for the new one right and that's just like going to exist for particular peer time
102:00 - 102:30 period to be able to to get from the state a to state b right good good points just a note and that's more about like type of requirements but it's good to understand it if we see functional requirements basically describing what the solution is going to do we know that there are like some going to be some kind of functions and they are super aligned to vision and scope of the company
102:30 - 103:00 of the business needs business requirements and based on this we are able to say these are like users and and like user requirements and we know that we need some kind of functions like calculation of number of employees and on the other hand we do have non-functional that are more describing the quality or some kind of constraints may also include business rules any
103:00 - 103:30 examples of non-functional requirements one quick example therefore we mention functional
103:30 - 104:00 so non-functional might be for example security that these particular security protocols might be used or if we are talking about digital transformation very often we need to support certain people in a certain way meaning response time for example like a response time might be the example of non-functional requirement so saying the response time might be this or available very important part and if you combine
104:00 - 104:30 all these you will get a requirement specification for the solution that uh that we are designing this is something that i would like you to take from today's lecture and probably the second part and this is coming from babok the book coming from w-i-b-a and that's pretty anyone who knows bobok
104:30 - 105:00 b-a-b-o-k no one maybe try to google that later on uh you are going to have the links also in the comments from coming from the slides but what's important is this schema describes needs and the solutions basically and as we have already discussed together we do have some needs coming from
105:00 - 105:30 like stakeholders and from from the business and we need to deliver some value to be able to deliver we are using solutions because we are like delivering solutions that is bringing the values right but that's not the these are not the only elements that are in the whole ecosystem because we do have stakeholders because we were talking about it the
105:30 - 106:00 needs are pretty tied to stakeholders right and to be able to deliver the solution as we are very often delivering is through projects so and these are going through changes so we need to really start changes to be able to deliver a solution right and the whole everything is part of like context this is mixing all together
106:00 - 106:30 bringing like individual factors influencing that's basically the environment right so and what's i was like describing in the sense that i do understand that but maybe you can start from the other way around because as you can see that the relationship everything is connected together right so just be very careful and i honestly speaking i recommend starting from needs even though you still need to see other
106:30 - 107:00 elements of like uh of of the whole ecosystem so this is this is important uh now i would like to mention one important part like when you are starting this kind of projects it's always good to see some kind of like best practices or some kind of frameworks that are already described or that are describing the
107:00 - 107:30 situation and management of business and informatics is one of them it's basically describing how we should manage business informatics but as we are focusing on business informatics analytics is part of that and you can see that there are like certain tasks and scenarios so basically you can ask and see tasks that are able that you are able to solve and see suggested solutions suggested metrics
107:30 - 108:00 dimensions that should be measured right and there are certain um this is the schema the whole schema that you you do have like tasks you do have some uh you have roles you do have metrics and other parts so really some kind of guidance that can serve you and mbi is not uh not the only one there are like tens of them globally so it's always good to see if there are some pearls on the
108:00 - 108:30 market that you can really work with and see some kind of recommendations or some like best practice coming from the market basically that you can that you can use these are like the examples of that and you can see that they're like tasks and some tasks belongs to analytical tasks right and there are like analytical and planning tasks that are related to strategic business management
108:30 - 109:00 financial business management and so on so this is this is one example we do have unfortunately a lot of content in check but uh i guess you can find many other examples that that are serving the needs so really giving you the examples how you can proceed and when you can get the inspiration from few important parts a is equal to b
109:00 - 109:30 b is equal to c a is not equal to c any examples or any real life cases when you have seen this
109:30 - 110:00 i would use for example the case of delivered emails when we have found different definitions and if we use them and calculate them i would be super sorry but i am almost sure that the numbers are not going to be equal so this is this is exactly and still is going to be delivered emails right so if i say
110:00 - 110:30 delivered emails calculated in this way it's b delivered emails or or something like that that is this is coming from b is equal to c but they are not going to be the same customer is king is always king right and it's always right any comments to this any experience
110:30 - 111:00 like the example that i was giving to you and the case that you were working on imagine that the customer is really coming to you and saying this is what i want doesn't mean that you have to accept that for the first class right i've been in the situation when we have really nice analytical solution and the customer was coming and asking them hey guys can you give me their our at their
111:00 - 111:30 this type of graph the spider chart and we were like okay what are the needs and and really starting analyzing that and we discovered that it doesn't have to be in in the form of spider chart so the customer is from my perspective it's it's good to have the respect to customers but try to have the partnership so it's not working like that you are just serving them but the aim is to really be the true partner for for customers
111:30 - 112:00 and i would say you probably know this it must be perfect we need it tomorrow it must be cheap anyone who knows that who can comment that how it end ups what can happen if we are saying it must be perfect we need it tomorrow and it must be cheap
112:00 - 112:30 they will not get anything oh that's a good point exactly my thoughts right they will not get anything we will not deliver that because you have to choose that very often it's it's um we show them as a triangle basically when we say quality time and price right it's good to communicate and you know
112:30 - 113:00 it's also part of the need analysis because very often customers are just just taking this as an example uh because when customers are asking it must be real-time analytics really saying we don't want to have big or near real time we don't want to have big delays in time delivery right but it's going to be expensive because it's having some some kind of
113:00 - 113:30 like prerequisites it's it's having some some like specifications and specific requirements to the environment so it's not going to be cheap and it's probably not going to be tomorrow right so it's going to be perfect it's going to be real time but we can't deliver it for free but if you are analyzing really the needs sometimes you can discover that
113:30 - 114:00 maybe weekly is enough right so just just really try to start from the points that we were discussing what's important when we were talking about process of business analysis it's good to set up the approach basically sit together with your team with the business partners with your customers and say what are the like overall goals of the change as you can see up here
114:00 - 114:30 so we can we can have overall goals of the change and based on this we are able to set up proper approach and coordinate business analysis task with other activities and goals of the changes right and also manage risk but that's that's something that is pretty evident right this is coming from from babock again you can you can see that and you can analyze that more deeply but basically setting up the
114:30 - 115:00 approach it might be predictive and it might be adaptive right any examples of that so predictive is saying if we are saying we do have predictive approach we are minimizing uncertainty and solutions definitions at the beginning of projects and we would like to maximize control
115:00 - 115:30 and minimize risks right and for the adaptive very often called agile we focus on fast delivery so if i should name that and if i give you the examples the first one means we would like to understand the whole issue the whole problem and the whole requirements prior we start developing we start really like doing coding and all the stuff around so
115:30 - 116:00 very often it's called like waterfall rigorous approach when we are describing the first phase then going to the second third and so on right but if we are following predictive approaches mean we will sit together with the business customer and i will be asking him okay why do you need that can you give me the solutions and so on i will everything write down let's say describe the solution
116:00 - 116:30 or describe some some kind of like requirements and i will ask customers to sign it and based on these side versions we will start working on that and then we will deliver that and we will introduce it to to the customer if i took in into agile world very often maybe you have heard about scrum or iterative approach we are focusing on fast delivery
116:30 - 117:00 and short kind of iterations so we are not describing the whole solution but we are focusing on particular smaller pieces that we are going to deliver right but there is some kind of uncertainty about final product because we don't know this the example i hope everybody is like at least partly watching myself the first one it's also connected
117:00 - 117:30 to what we are doing so if i see some kind of this pen right and i would just like to change the naming that i do have something like think differently so let's say you would like to produce similar pens or pencils just just with the different naming right that's the analytical task or or the the delivery tasks
117:30 - 118:00 is just please can you deliver me 500 pounds let's not sing different but put there like lap university of applied sciences right that's i can definitely describe that prior or start doing that because i'm 100 sure that i would like to have this pen with this blue cap and i would like to have the same stuff just different text on that right that's that's pretty easy
118:00 - 118:30 but if i would like ask you to deliver the reporting analytical solution that i was giving to you and i as a customer even not sure how to describe that and probably it would be better if i if you give me some kind of prototypes or minimal viable products or some kind of this that is going to [Music] have certain level of uncertainty within like product
118:30 - 119:00 definitions but it's going to focus on edit value and precise definitions and you should agree on this at the beginning you should know that are we are we really like certain what's expected solution or not and are we able to do that there are like certain checks you can you can google that and based on this follow follow that approach is is this clear to everybody
119:00 - 119:30 i guess tara is going to give you more examples and more like uh naming or um descriptions of uh this waterfall and agile approaches later during the course so you can definitely discuss that i will skip this for more and i've already described that it's good to
119:30 - 120:00 have some like types of activities that you are going to do within business analysis to reach the aim so you should sit together with the stakeholders and with the team and design that and you must say okay we are going to have this kind of activities for example identification of data that's one right as you were correctly mentioning that during the case and you should divide that into phases or inter iterations based on the
120:00 - 120:30 operator that you are using timing very important timing uh because in the organizations very often you do have some some other activities like stakeholders are not working are not going to work only for you for for for the projects very often they are to have their core work their their day-to-day operations or whatever
120:30 - 121:00 and they are going to serve as part of the projects right the situation uh can anyone give me the example when for example companies are super busy well the end of the fiscal year yes yes
121:00 - 121:30 that's that's very important part for example end of the fiscal year so when you would like to do some either analysis or including the whole delivery at the end of the fiscal year very often these people are going to say no because they need to like close their periods here and they really need to concentrate on this so now they are not able to dedicate enough time to that's that's the exact example that i wanted to mention and for it guys it's not such a easy
121:30 - 122:00 task to understand this honestly speaking i've been in the situation when customers were saying hey we need to close the euro and then we can start the discussions and they were not able to understand it so just be very careful about this uh we were already talking about complexity and when we are talking about analysis it's also important to say how are you going to accept it
122:00 - 122:30 how would you do the acceptance how would you do that any suggestions any tips we stop a meeting with the client and
122:30 - 123:00 go through the solution and then ask that is it what you have asked for that's one example you can introduce them show them some kind of mock-ups or or some drawing and asking them is it okay these are the calculations and i'm really going one by one i can just send the whole documentation like 200 pages 500 pages and ask them please review them inside
123:00 - 123:30 them are they going to read that it would be good if they can't read that very often they are just signing it off so it's good to agree right it's really good to agree and identification of needs last point we do have like 13 minutes i will definitely put some times for for the questions and answers so it's good to be ready for for the discussions as we were discussing taria has
123:30 - 124:00 mentioned it's good to listen and elicit it properly so it's really be it's necessary to set up proper meetings and proper way how are you going to do that i will show you some some methods shortly but it's really necessary to say we are going to work on that and dedicate that proper time and it's good to confirm that so very often what i'm doing with my clients
124:00 - 124:30 it's like i'm trying to listen i'm not the best listener still but i'm i'm trying to learn so i'm trying to listen making some notes and and from time to time i'm like doing some kind of checkpoints during that but very often at the end i'm trying to summarize that and trying to tell them this is what i understood right and and include my analytical thinking in that so based on this i'm trying to explain them my understanding and my
124:30 - 125:00 analytical approaches and this is having the advantage when they hear that again so that's different process when they are saying that it's different process because from from there like speaking they're now listening they start to think of that from different angle and you can be more aligned or you can confirm that you understood correctly and there they they do have slightly different dimensions so they can have different
125:00 - 125:30 thoughts so this is this is this is important and identification so how to do that you can have direct interaction with stakeholders meaning we will sit together and you will you will ask them right that's just a direct one you can do some kind of research discovering studying information from materials from resources you might be
125:30 - 126:00 describing you might be studying the processes there might be some kind of process maps uh models of processes so you might be uh you might be studying that or doing some observations within within the research seeing how they are behaving right doing the experiments uh maybe giving them like use cases that i was talking about very often connected to collaboration because you are like saying okay what's going to happen if
126:00 - 126:30 you see these numbers if you see them in this structure right so that's kind of experiment so you don't know the results you don't know it's it's going to be probably some kind of like control environment because you are not going to do that in real life you are just like testing that but that's that's the experiment right so these are like three types how you can identify needs and techniques there are really
126:30 - 127:00 hundreds of techniques if you see babok and google that you will see nice description of these techniques what's important very often it's not single usage of techniques so it's not single select right uh for example the project that i was talking about that i'm allocated on is providing some kind of consolidated reporting across
127:00 - 127:30 different regions different countries let's say 120 countries at the beginning we had something around 30 countries so how we how we proceed that so and then there are like certain measures that we are reporting and providing uh providing the numbers to stakeholders right mainly from marketing departments and so at the beginning we done the document analysis studied their solutions and
127:30 - 128:00 as we have done that we asked them and we use surveys to ask them what they consider as the most important part and what they are reporting so that was like first document analysis right after survey and as we've done that we selected the focus group because you just simply can't have 120 countries in the room
128:00 - 128:30 so we've picked and and like if you are him you if you have even more than one people per country you can have like 20 200 folks on the go because you will not move so we selected the focus group and prepare a workshop right and as we've done the workshop basically saying these are the measures that we are going to focus on and everybody agreed on that we prepared prototyping from the visualization perspective so you see this is like some kind of
128:30 - 129:00 mixture of techniques that you really need to use maybe right now we would like to hear some cookbook or guidance what to select unfortunately i don't have the answer because you have to reflect the situation like how many stakeholders are you having is it international is it national project uh what are you going to use so it's always about like certain factors that are influencing
129:00 - 129:30 proper selection and i don't have just the gold answer that is going to say just use this it's going to work it's about like continuous learning and proper selection which you have to somehow frame based on the situation that you are living guys i guess we are at the end of of the lecture i would like you to think
129:30 - 130:00 of the solution probably we don't have time to now jump back to breakout rooms just in your heads try to think about youth design and [Music] what would you do differently right based on what what we were discussing right here questions comments really thanks for your ideas um super happy for this kind of discussions very interesting
130:00 - 130:30 points and i'm more than happy to answer or answer the questions i have one question you are working there in central europe and uh obviously also working with some companies what kind of use cases are you doing outside of this teaching work i
130:30 - 131:00 mean that what kind of cases and maybe big companies small companies with data science science so what kind of things are you doing there that's a good point thanks for this because i haven't mentioned one and this this lecture was not in this sense but uh these are like i'm mainly on like business intelligence part so very often i would say traditional reporting like predefined standardized reporting that is serving the business needs
131:00 - 131:30 it's not advanced analytics so if i should frame that is sitting into like fundamental analysis or analytics and in providing predefined or regulatory reporting it might sound slightly boring not so catchy as data science or advanced analytics but it's having certain fun regarding complexity on all the stuff around what's important is if as you were mentioning
131:30 - 132:00 like different types of companies and startups is to be very careful about the maturity of the companies so right now we were talking about goals processes and all stuff around but really understand the culture and understand the companies because very often companies are not ready for certain types of analytics so when we are talking about and taria is going to mention that
132:00 - 132:30 i hope she is going to show you that great diagrams that i've seen so if you are having like fundamental analytics really understanding mainly what happened in the past and if companies are doing this probably they are not able to jump to super highly advanced analytics and you must reflect the situation right so if they are just saying we do have five customers and they are not targeting in this field it's it's kind
132:30 - 133:00 of step that you have to take and you have to go for to really move further on on the scale on on the ladder basically to be able to do advanced analytics so having some kind of maturity is important for this i hope i've answered your question but from broader perspective yeah thank you thanks for the question it's very good
133:00 - 133:30 i have a question um um uh i hope it's not too vague or unclear but i'm having some challenges and understanding that how does uh uh accompanies uh digital maturity um uh how does it actually
133:30 - 134:00 how hand in hand does it actually go with being data data driven or data informed that's that's a good question thanks for this it's pretty complex honestly speaking uh it's like there are certain approaches that are measuring that i will try to very briefly open one presentations that i do have here uh
134:00 - 134:30 and i hope i can i can uh shortly find that because this question consists of quite a lot of um quite a lot of sub questions basically right because you were saying data maturity and it consists also from data management maturity and also it might
134:30 - 135:00 include like data driven environment which consists of processes so and and culture so if i should this is really complex question and we might spend the whole lecture on on this and that's that's very interesting topic so if right now what i was saying at the beginning to start it's important to understand how people and companies are making decisions whether it's there by intuition or whether it's somehow
135:00 - 135:30 data supported and how in the steps and tara is going to show you the schema later during the course but it's good to understand this and see whether the particular company is still doing decisions based on feelings just based on feelings or whether they are able to accept data to do this it's not easy task to move from the pure intuition to somehow
135:30 - 136:00 data supported or data inspired decisions i had several discussions with like cdos with people who were driving analytical teams really big one and we agreed that this is important part of that of changing the culture basically and it's about analytical thinking implementing that but you have to show them cases that's that's my understanding really show them
136:00 - 136:30 how you can use analytics to improve your daily lives basically that's the that's the most important i can share very briefly uh the presentation that is showing regarding maturity levels that's please take this just just like one example this is for what we are using for our mba students uh so i hope can you see data management maturity assessment
136:30 - 137:00 yes good so you can see that they are like and this is framework that that we are using uh we are working on that with kpmg but there are like many others right the same as for process maturity so you can see that there are like levels from one to five in this framework and you can see that for for the perform you can see that processes are like ad hoc primary at project level and not apply across business areas activities are
137:00 - 137:30 primary reactive and repair over prevention right so this is the first level and you can see different levels up there to to optimize when you can see that processes uh performance is optimized through applying level four uh we do have some kind of targeted identification of improvement and we do have best practices are shared with peers within that industry so we are really measuring that and what you can do
137:30 - 138:00 you can basically if i jump in you can have certain let's say capabilities and you can design desired levels where you are heading right so it it's exactly what i was talking about and i don't have again like one super great answer for this but you can say okay we are measuring like decisions and how we are supporting decisions we are focusing on let's say uh
138:00 - 138:30 parts for example for data management itself our technologies and you can have really certain let's call them capabilities that you are measuring on this wheel and saying this is our target date and this is our uh our current situation right i do have feeling that i probably haven't answered flair questions uh your question it it's really
138:30 - 139:00 interesting i hope i've gave you at least some kind of hint that that you can start thinking of that yeah i think it's a wide topic so i think this was very very interesting and a good introduction to that so thank you good thanks for this and we will continue in the afternoon uh also really and
139:00 - 139:30 related to this kind of maturity level so good we can continue with this topic too so any short questions yeah i've seen that we are already over time yeah but uh it's not a problem for me to to still continue if somebody wants to ask something
139:30 - 140:00 so if there are no questions or if there are right now no questions i would really like to thanks for your cooperation up here it was very interesting for me seeing your points i'm super happy for them because i've seen really advanced analytical thinking already in place which which is perfect and i guess giving great prerequisites uh i'm very active on social networks like
140:00 - 140:30 linkedin so if you are there feel free to just like connect me add me into your groups and if you are having any questions uh feel free to just send me emails send me a message on wherever you you prefer uh i'm fine i'm more than happy to discuss this tarya thanks for the invitation
140:30 - 141:00 it was a very interesting and i think this gave a lot of thoughts to students and uh and i really want to remind also this group that martin will visit our university also in next week if somebody of you uh if you are interested of the information visualization so so it's also possible to join that
141:00 - 141:30 lecture i will i will send you or we can continue discussing about these two later today but maybe we will meet you perfect that would be good we will yeah because the second lecture is more focusing on like really visualization and bringing yes data into decision making processes in a proper way so it's some kind of continuing of that and doing the visualization and presenting
141:30 - 142:00 the results yeah i do have a another student's group then but also this group students can journey if they want so maybe maybe also this this group okay thank you so much and um uh you will send me and uh recording links in cloud somewhere i hope i hope i will receive
142:00 - 142:30 that i hope you understand that yeah i hope too and uh i think now it's time to have a lunch break and uh very i'm very pleased that uh everything went so well today none any any kind of uh connection problems or anything like that looks good looks good yes uh again thanks for the invitation really appreciate that always happy to to have a chat with folks from finland i hope next year we will meet uh each other in person
142:30 - 143:00 uh going for sauna doing some and eating chocolate of course yes you have mentioned so many thyme and chocolate today you're really missing that i've been really missing yeah yeah okay so we are having break and uh 40 45 minutes break so