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Summary
In this introductory session, HR analytics is explored, highlighting its significance in modern HR management. The session outlines the foundational functions of HR analytics, such as recruitment, selection, learning, development, performance management, and compensation. It stresses the importance of implementing descriptive analytics before advancing to diagnostic, predictive, and prescriptive analytics, emphasizing the necessity of statistical knowledge and data processing capabilities. The lecture encourages HR managers to utilize analytical tools and techniques in their decision-making processes to enhance organizational performance and HR effectiveness.
Highlights
HR analytics is an essential tool for modern HR management, aiding in decision-making across various HR functions. 📊
Descriptive analytics is the foundation; focus on it before moving to advanced analytics techniques. 🧱
Data visualization enhances understanding—tools like Tableau, PowerBI, and Excel are invaluable. 🛠️
Develop analytical skills and technological infrastructure in HR for successful analytics implementation. 💡
Understanding and utilizing HR analytics can improve organizational processes and outcomes. 🚀
Key Takeaways
HR analytics is akin to people and workforce analytics - they're all the same! 🤓
Start with descriptive analytics before progressing to more complex levels. Baby steps! 🐣
Understanding statistical tools is crucial for effective HR analytics use. Know your averages and deviations! 📊
Data visualization tools like histograms and pie charts are your best friends for descriptive analytics. 🎨
Descriptive analytics answers 'what happened', while diagnostic dives into 'why'. Investigate those relationships! 🔍
Overview
Welcome to the world of HR analytics—a tool that's revolutionizing HR management! In this foundational session, we dive into the basics of HR analytics, exploring its definition, types, and application in decision-making processes within HR. 🧠
The session sets the stage with descriptive analytics, the backbone of understanding 'what happened' in HR scenarios. While descriptive tools and visual aids simplify data interpretation, setting the groundwork for more complex analytical endeavors. 📈
As we journey through the realms of HR analytics, the emphasis is on acquiring skills and building infrastructure. HR managers are encouraged to grasp data processing techniques and become proficient in statistical methods to effectively implement analytics in their organizations. ⚙️
Lecture 01: Introduction to HR Analytics Transcription
00:00 - 00:30 [Music] [Music] dear participants in this session we will learn the basic things about the HR
00:30 - 01:00 analytics what HR analytics is what are the types of how HR manager makes the decision what are the things that HR manager does what HR manager should know in order to use the HR analytics in his or her organization so it is the basic and introductory session on HR analytics so let us start with the content what is the content so first thing that we will
01:00 - 01:30 understand that is the uh introduction to HR analytics so that this is the first thing that is what we will discuss next question that HR analytics can answer what type of questions that HR analytics can answer what type of questions that you can frame and then you can get the questions answers of those questions next one is the types of an analytics what are the types of the analytics so here you can see there is a
01:30 - 02:00 three types of the analytic four types of the analytics descriptive right and then what are the tools of the descriptive analytics how you can visualize Predictive Analytics right and what are the questions that HR managers should answer types of analytics difference between Mis and HR analytics right so these are the things that we will cover in this session so let us start with this introduction to HR Analytics
02:00 - 02:30 so HR if you are into in this field then you might have heard some people say it is the HR analytics human resource analytics some people say it is the people analytics some people say it is the workforce analytics so but meaning is the same right things that are done under these analytics is the same so what I suggest for all HR Manager for starting purpose what this should do
02:30 - 03:00 about this HR analytics so this should remember the six functions of the HR right so being a in any organization you are working each manager or each HR department has to perform minimum six functions so what are those functions so first first function is the recruitment right second function is the selection right third function is the Lear
03:00 - 03:30 learning right so some in some organization you will see training right so learning fourth function is the development right you need to develop the employees fifth fun function is the performance measurement of performance management system and sixth function is the compensation you need to give a salary also right so related to these function each manager has to make a
03:30 - 04:00 decision so in this particular course we will learn the basic Matrix related to these six functions right how you can manage the recruitment effectively by using those matrics right next function selection how you can manage the selection how you can manage the learning how you can manage the development how you can manage the performance management system and compensation right so under these three
04:00 - 04:30 so these three are the synonyms of the HR analytics whether you call it HR analytics whether you call it people analytics Workforce analytics but meaning is the same right so basically you will see the matrices related to these six functions right so I always say to the all HR manager whichever Department that you are working first ask what are the basic questions that you need to answer in day-to-day work life right so while working what are the questions that you
04:30 - 05:00 need to answer so if you are working in the recruitment Department then people may ask you how many people you should attract to fulfill the vacant positions which are there how you should if you are working in the L&D Department training and development department then you may ask what what is the level of the skill that is required in your employees right and how they can achieve that level of uh uh skill right skill Gap analysis that you made do it in
05:00 - 05:30 performance you may ask who is the best performing which department is the best performing how the salary component should be decided so such kind of questions that you need to answer in day-to-day right so how you can find out the solutions or answers of those questions through the analytics that is what we will learn in this course right I hope various matrices that we will discuss in this course that those matric will help you to find out the solution
05:30 - 06:00 so I hope so if somebody is saying HR analytics people analytics Workforce analytics so meaning is same there is no difference in these terms right people analy whether you call it people analytics HR analytics Workforce analytics everything is same right so I already said what are the things that will come right I'm not saying if you want to take some other things also that also you can include like engagement is there leadership analytics is there right motivation that is what you you can case but most of the topics that you
06:00 - 06:30 can cover under these six functions only so engagement that you can bring it under the uh performance right and motivation so if you can see the Learning and Development and performance and compensation both things can address this motivation issue of the employees so that is how you can bring the motivation aspect under these functions so if you will think any aspect related to the R analytics you
06:30 - 07:00 can divide or you can bring under these six categories just think about it right now question comes what type of questions that you can answer so what has happened right descriptive questions like what has happened so what has if you are saying your recruitment is effective so why what has happened so why that recruitment is effective right so if you know the number of application that you receive after the uh job
07:00 - 07:30 posting so because of that you are saying that recruitment is effective so why it is effective what has happened so the number of application has come more so that is why we are saying if you are saying selection is effective that you have uh selected the people those are effective people so you are seeing on the basis of quality of hire right that people that you have hired that is good they are good so that is why and why it is happening where it is happening how it is
07:30 - 08:00 happening and what is the underlying cause whatever is happening right so such kind of questions that HR analytics can answer for the HR manager HR analytics will help you to find out the answers of such kind of questions so let us understand the types of analytics so so that you will understand which type of analytics will help you to answer which type of questions so let us
08:00 - 08:30 understand so four types of the analytics that we discuss in any so so four types of the analytics so first type is the descriptive analytics second one is the diagnostic analytics third one is the Predictive Analytics and fourth one is the prescriptive analytics right so in this course we will focus more and more on this descriptive analytics right so all 60 lessons that you will see in this
08:30 - 09:00 course they are focusing on descriptive analytics diagnostic predictive and prescriptive after this course we will develop new course in that we will focus on diagnostic predictive and prescriptive but in this course uh you will see we are focusing only on descriptive so I suggest the all HR manager before going to this diagnostic predictive and prescriptive first implement this descriptive
09:00 - 09:30 statistics in your department and then think about diagnostic then think about predictive and then go for the prescriptive right because the moment you will move from descriptive to diagnostic little more little bit more statistical knowledge that you required the moment you will go diagnostic to predictive then more advancement Advanced Techniques that you have to learn right and when you will go predictive to prescript Ive then you
09:30 - 10:00 have to learn this mathematical modeling and you have to learn uh operational research tool tools and technique also so I can say that the mo when you will move from descriptive to prescriptive your complexity will increase right and currently most of the organization they are not having the sufficient things which is required to develop a effective HR analytics department so that's why I
10:00 - 10:30 recommend in initial stage you start the using this start this Statistics using uh analytical tool that you start using uh that is the descriptive you should use the descriptive analytics more and more in the initial stage the moment you feel you have developed the framework uh for this uh descriptive analytics then you should uh then only you should move toward the diagnostic
10:30 - 11:00 and Predictive Analytics right because until or unless you have the knowledge of this statistical tool and techniques you don't have the uh database sources of the data in your organization to collect the right kind of data which is required and you don't have capability to transform the data right so if you don't have such kind of capabilities then think about developing these capabilities and after that you implement the all types of the analytics right so in detail we will discuss what
11:00 - 11:30 type of capabilities is required to develop a effective HR analytics unit in the uh organization so in detail we will discuss but as of now you understand if you don't have the effective team then don't think about advanced level of the HR analytics you start with descriptive so I hope so descriptive will answer what kind of questions what has happened and what is happening right and diagnostic will answer why did it happen
11:30 - 12:00 whatever has happened so why it happened right so that relationship related things or difference uh related things uh that is what this diagnostic analysis will answer so what are the tools and techniques that will be used under each category we will discuss in upcoming slides now third type of analytics Predictive Analytics will tell what will happen in the future right so recruitment that you said this year thousand people thousand application that you have received so next year how
12:00 - 12:30 many you will receive that you can predict through the Predictive Analytics this year you selected 100 employees next year how many employees that you will select that you can predict through the Predictive Analytics prescriptive so here just you can understand right if you have to hire 200 employees right and then how many HR manager you need in the organization if you want to add identify how many number
12:30 - 13:00 of HR manager is required to hire 200 employees so in this case you can use the prescriptive so this is a optimization problem right to hire the 200 employees how many HR managers is required effective number so through the optimization you can find out the solution for this prescriptive analytics so that is how you can use this different type of the analytics to answer these type of questions so I already say if you don't have uh enough
13:00 - 13:30 capability to use this analytics in detail then you can start with descriptive one right so I hope this all HR manager understand so in which stage you are what kind of capabilities you have so accordingly you can decide the level of uh analytics that you will Implement in your department but this four type of analytics that exist in data science next so let us understand what are the tools and technique that
13:30 - 14:00 you can use for the descriptive analytics so first thing that you can use average simple average so in coming sessions that you will see I will use I will calculate various types of average related to the uh recruitment selection performance compensation so simple average that you can understand what if you want to know how young your department is right so simple thing that you can do you can calculate the average
14:00 - 14:30 age of your department employees simple so if you can calculate the average age of your EMP in the department in whichever Department that you are working that will tell you how young your department is right so whether it is Young between uh 20 to 25 25 to 30 30 to 35 40 50 what is the age that you can see in the same way you can calculate the a average
14:30 - 15:00 salary right average salary that is what you can calculate and then you can understand same way you can if you want to calculate the gender pay Gap then you can calculate the salary for male and you can calculate the salary for female so that is how you can calculate the various type of averages that is what we will discuss in in upcoming sessions related to Performance selection compensation uh recruitment right Learning and Development and then
15:00 - 15:30 you can uh make various decisions so this is the descriptive analytical tool first second one that you can see standard deviation so deviation from the mean so whatever so that if you want to understand the outlier how many outliers are there right so deviation from the mean so what is the average mean and how much is the deviation so if deviation is high then you can say that data is very variation is there same thing uh that
15:30 - 16:00 you can see uh in the variance also so how much variance is there in the data how much it is varying whether it is concentrated towards the mean or it is deviated from the mean so that is what you will understand through the standard deviation and mean so if standard deviation is very high in term of your age right then you can say that some of the young people are there in your
16:00 - 16:30 organization and some of are very old if standard deviation is very low then you can say that the ages of the employee who are working they are very close and people are from the same age group deviation is high then you can say that people are there in the department they are not from the same age group because deviation is there standard deviation is very high same that you can use the mode mode is frequency right so whichever number is coming so if you want to know
16:30 - 17:00 from which age category maximum number of people are there right so mode that you can calculate certain age categories that you can say so let us assume 25 to 30 30 to 35 so from which age category maximum number of people are there so now you can see the mode that is what you can see from which age category which age group most of the people are there simple counts that you can count like number number of application number of selected candidate
17:00 - 17:30 number of rejected candidate so simple count that you can have that also can give you the number and based on these numbers you can calculate the ratio you can calculate the percentage you can calculate percentage raos right and then you can present effectively so these are the descriptive analytical tool that you can use in your HR related functions in order to make the decision so if you are
17:30 - 18:00 using these kind of tools and technique then you can say you are using the descriptive analytics in the HR functions right so I hope these are the simple tool and technique that each HR manager would be able to interpret so that is why I was saying you should focus first on descriptive analytics and then you think about the uh Advanced one so these are the calculations that that you can do related to the performance
18:00 - 18:30 compensation learning development Recruitment and selection these all Concepts that is what you can use and then you can do the calculation in these functions and then you can make a decision related to it right and now let us come to the visualization from the descriptive analytics perspective so here you can make the histogram right so histogram that you understand right write that you can make the graphs and in detail you will
18:30 - 19:00 understand in upcoming sessions where I will discuss various types of the histogram related to recruitment selection performance compensation learning development right by using all three visualization tool you will see I have used Tableau I have used powerbi I have used this Excel by using these all three tools you can make histograms it is not necessary you have to use only
19:00 - 19:30 powerbi and uh tblo or Excel any tool that you can use in order to visualize this data whichever you are comfortable with but in this course we will be learning all three tools to visualize the data so when you are using the descriptive analytics so you can use this histogram to visualize the data one of the tool is there next thing that you can say to show the proportion of the things right so let us take the recruitment you want to show the sources of recruitment right so internal versus
19:30 - 20:00 external so what is the percentage of the internal uh sources through which you have received the application and what is the proportion of the external through which you have received the number of application right so that you can put it through the pie chart right next thing that you can use the bar graph right bar graph that is what you can make it right so bar graph that is what you can make you so number of employee how many employees are there in
20:00 - 20:30 various Department what is their gender what is their salary what is their performance level performance rating so these are the things that you can show through the bar graph so you have understood in descriptive analytics which analytical tool that you which statistical concept that you can use and how you can visualize that particular data now let us move to the diagnostic right so in diagnostic first let us
20:30 - 21:00 understand what kind of questions that you can answer so initially I can suggest you can start with descriptive and then slowly slowly you can move towards the diagnostic also right so I hope HR manager has this much knowledge to uh implement this diagnostic after the descriptive but start with first descriptive and then go for the diagnostic so what type of questions that you can answer why did we are observing occur
21:00 - 21:30 right whatever is happening so why we are observing this where did it occur so where it is happening is the Matrix we are monitoring related in any way to the things that we have collected the data for so whatever data that we have collected related to the dependent variable so is there any way it is related to the independent variable
21:30 - 22:00 because if independent variable and dependent variable are not related then what kind of relationship that you are calculating if you are calculating the relationship between number of application right that you received and number of people who left the organization right so are you able to establish the relationship through any logic is there any logic right so if
22:00 - 22:30 that logic is missing between these two independent and dependent variable in which you are trying to explore the relationship that is what you need to think of right and if logic is there and then what is the strength of the relationship so you already know the correlation value right 0 to 1 and + 1 + 1 2 - 1 between zero right so in between
22:30 - 23:00 somewhere you may say you may get so correlation value could be positive and negative also how much of the variability of our Matrix is accounted from the data that we have collected so whatever data that we have collected so how much variability our matric has accounted so such kind of questions that answer of such kind of questions that you can get through the diagnostic analytics now let us come to to next aspect what are the
23:00 - 23:30 tools and technique that we used in diagnostic analytics so in diagnostic analytics that you can see from number uh from descriptive in numbers means mode median standard deviation variation from here we have moved to the some advanced level tool so here you can see that we we will calculate correlation and regression analysis of variance is Anova right Anova that is what we will so one
23:30 - 24:00 category Anova two category Anova right and T Test also we can say t test so T Test anoba correlation regression factor analysis cross tabulation principal component analysis correspondent analysis multiple correspondence analysis so such kind of analysis that we use under the diagnostic analytics in this course we will learn only about descriptive analytic tool the next
24:00 - 24:30 course that uh we will develop in that course we will talk about diagnostic analytics in detail right there we will discuss about correlation regression analysis of variance Anova factor analysis cross tabulation principal component analysis right so these all things that we will discuss in the next course but as of now you understand in diagnostic analytics these are the tools that comes now let us talk about the next aspect ECT that is the
24:30 - 25:00 visualization so how you will visualize this data which is related to the diagnostic analytics so you can use the scattered plot to visualize the data regression plot plot of residual box plot multiple density curves so these are the graphs that you can make right my again my advice to all HR managers before the making these all
25:00 - 25:30 graphs please understand your variables carefully establish some logic between those variables and then use this uh data visualization tool Excel PBI or Tableau to make these graphs right so for Diagnostic analytics you can use this scatter plot Recreation plot plot of residual box plot and multiple density curves right so that is what you
25:30 - 26:00 can use now third type of analytics that comes that is the Predictive Analytics right so in the case of Predictive Analytics what are the things so this type of analytics also we are not going to cover in our uh course right so next course that we will build in that we will focus on descript Diagnostic and predictive in this course we have covered in detail descriptive analytics so all these
26:00 - 26:30 descriptive numbers mean mode averages that is what you will see all decisions that we are making in this course related to the HR functions that we are the that we have made only on the basis of the descriptive so six functions that we have covered in our uh in this course those functions are recruitment selection learning development performance and compensation so for Predictive Analytics what are the tools uh so tools First Tool is the regression
26:30 - 27:00 analysis so various types of the regression that we have linear regression curvy linear logistic so these are the various types of the regression when we will develop the next course in that we will discuss in detail decision trees and its variance random forest and discriminant analysis so these are some of the tools for the Predictive Analytics right so that we will cover in the next uh course that we will develop
27:00 - 27:30 and these are the visualization tools that you can use line chart scatter grams and correlation plot in order to visualize the predictive data and this is the prescriptive so if you will see very few organizations have used this type of analytics till dat right so because for applying this analytics you need to have you high level of statistical knowledge and operation necess knowledge and mathematical
27:30 - 28:00 modeling if you know these things then only you will be able to do uh you you will be able to use the prescriptive analytics in your organization right so after uh this second uh course then we can think about this prescriptive also right what is the difference between Mis and analytics right so if you will see this HR misis in one line I can say that HR Management in information system is the source for the
28:00 - 28:30 data right so on a if you are having this H Ms then related to employee you will get the all information right so you may get a information which you want related to the employee age gender Department wise right but when you have to make some decisions then you have to capture the data through some Matrix right so you will develop some Matrix and hrms will give you the uh raw data
28:30 - 29:00 from that raw data you will process you will transform you will make it meaningful by using HR metric and then you will make a decision so I can say that HR IMS is the source of raw data right that you are needed to make a decision so that raw data that you can collect from the HR IMS and then you can process it according to the HR metrics which metrics that you have make it and by by make uh by calculating these
29:00 - 29:30 matrices you can take a certain decision related to the HR processes so in the case you can say that HR analytics offers the more than HR metrix through its potential to connect with HR processes and decision with organization performance so HR Matrix will give you that data and then as a HR manager you will link with HR process and organization and performance and then you will take a decision related to the
29:30 - 30:00 HR function so that is the HR analytics HR IMS will give you just data and that data you need to process and then you have to make a decision so this is the difference between at HR IMS and HR analytics so basic questions that you might be asking to yourself how does this HR analytics works so one thing that I want to tell you that you should remember this lamp model so whatever
30:00 - 30:30 analytical tool that you are using right so first thing that you should remember this lamp model so lamp model L stands for logic so whatever thing that you will do in HR analytics without logic you will not do anything right m stands for major so first you will apply logic and then you will measure and then you will process it and then you will make a DEC related to that particular thing right
30:30 - 31:00 so without logic so if you are applying analytical tool without logic that may not give you the right answer which you are looking for so first you need to apply the logic so if two relationships are there how this recruitment is related with the selection right effectiveness of the recruitment is related with the selection Effectiveness if you find any logic then apply that develop the majors then develop measures means develop the HR
31:00 - 31:30 metrix develop the HR metrix process that data and use that processes process the data or outcome of that metrix all right and make a decision so this is the one model that you can see this how HR analytics Works second thing that you can see that like a balance scorecard HR scorecard is also there whatever HR initiative that you have taken like related to The Learning and Development you have initiated two to new training program so how it has impacted the
31:30 - 32:00 organization processes right related to for example customer handling so this new training programs how it has reduced the number of customer how it has improved the process so you can see some process related outcome like number of customer complaints and when number of customer complaint have reduced then how it has impacted the profit of that organization so that is how you can d the HR score card in order to develop
32:00 - 32:30 the logic and linking the people strategy and performance of the various department so if you have taken any initiative related to the HR department so how it is impacting the business processes and how these processes are impacting the business outcomes so that through that link you can develop this HR scorecard and then you can so you should know how this HR analytics works so these are the two approaches that you can uh think how HR analytics works so
32:30 - 33:00 first and foremost important thing is before applying any analytical tool you should be very much clear with your logic what why you are doing this particular analysis if you have this answer then go ahead with this and then measure it process it and make a decision related to that particular problem so if you remember I had suggested the way to apply in the organization being a HR manager whatever questions that you are having right make
33:00 - 33:30 a list of all those questions after making a list of questions make a list of variables right about which you need to collect the data list of variables and then think about the data analysis after collecting the data so in these four steps you can use this HR analytics next so what does HR Analytics process so you can see the processes
33:30 - 34:00 related to these statistical analysis that the whatever we have discuss descriptive prescriptive so these are the processes of the HR analytics what is required to successfully implement the HR analytics so first thing that I would say the analytical skills of a person because so skill of the HR professional so person who is working in the HR department they should have the analytical skills and second thing that information technology so HMS I was
34:00 - 34:30 talking about so you should have this HMS so that you will be able to have a data you will be able to capture the data and you will bring that data together and then you will be able to analyze it and third thing that I would say basic knowledge of data analytical tool and technique so if you are having these three things then you can s successfully you can implement the HR analytics in your department all right what are the outcome of the HR analytics so you can
34:30 - 35:00 see very uh you will be able to understand the relationship between various processes of the HR and out business outcome so in this case that you can see Employee Engagement how it is related with the store performance so HR metrics are the key majors of the HR outcomes so what are the HR outcomes what are the outcome of the recruitment potential candidate how many candidates have applied so that will be measured through the HR Matrix how many people
35:00 - 35:30 are selected after the selection process will be measured through the HR Matrix so thank you I hope you would have learned the basic things related to the HR analytics so welcome to this course thank you [Music]