# An In-Depth Guide to HR Analytics: Applying the Data Science Framework

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## Introduction

Human Resources (HR) analytics has emerged as a crucial component in the strategic management of workforce-related activities. In this article, we will delve into the **data science framework specifically tailored for HR analytics**. Understanding this framework will empower HR professionals to effectively gather and analyze data, define problems clearly, and better support organizational goals. Throughout the discussion, we will cover essential steps in the HR analytics process, from defining goals to presenting actionable insights.

## The Importance of HR Analytics

HR analytics allows organizations to make data-informed decisions regarding various HR functions such as recruitment, performance evaluation, training, and employee retention. By leveraging data, organizations can enhance their talent management strategies, thereby boosting overall efficiency and productivity.

### Key Benefits of HR Analytics

**Enhanced Decision-Making:**Data-driven insights allow for more informed decisions regarding employee management.**Efficiency:**Streamlined processes can be developed based on analytical findings, which can lead to reduced costs and improved operational efficiency.**Employee Engagement:**Understanding employee behavior through data can lead to better workplace practices and increased employee satisfaction.

## The Data Science Framework in HR Analytics

### Step 1: Define the Goal

Defining the goal is the foremost step in applying HR analytics. Clarity regarding the objective ensures that subsequent actions, such as data collection and tool selection, align with the organization's desired outcomes.

**Why is defining the goal important?**- It narrows the focus of your analytics efforts.
- It determines what kind of data needs to be collected.
- It influences the analytical tools that will be used.

For example, if the goal is to reduce employee turnover, HR professionals must define key metrics (e.g., turnover rate, reasons for turnover) before proceeding.

### Step 2: Define the HR Problem

Once the goal is clear, the next step involves pinpointing the specific HR problem that needs to be addressed. A well-defined HR problem should include:

**Statement of What:**Identify the nature of the problem (e.g., high attrition rates).**Statement of Why:**Analyze the underlying reasons for the problem (e.g., poor employee satisfaction, salary discrepancies).**Desired Outcome:**Determine the outcomes you wish to achieve (e.g., lower turnover rates).

### Step 3: Collect and Manage Data

After you have defined the HR problem, it's time to gather the data. Data can be sourced from various internal systems such as Human Resource Management Systems (HRMS), payroll data, feedback surveys, and more.

**Important Considerations:****Data Quality:**Ensure that the collected data is accurate and reliable. Poor quality data can lead to misleading conclusions.**Structured vs. Unstructured Data:**While structured data (like numerical datasets) is easy to analyze, unstructured data (such as employee feedback) might require additional processing.

### Step 4: Build the Model

Once the relevant data is collected, the next step is to develop a model for analysis. This involves choosing appropriate statistical methods for analyzing data that aligns with your HR problem:

**Descriptive Analytics:**Helps in summarizing historical data (e.g., number of hires per department).**Diagnostic Analytics:**Assesses why certain outcomes occur (e.g., analyzing correlations between salary and attrition).**Predictive Analytics:**Uses historical data to predict future outcomes (e.g., forecasting employee turnover).**Prescriptive Analytics:**Suggests approaches to achieve desired outcomes based on data patterns.

### Step 5: Evaluate and Critique the Model

Evaluation of the model is crucial to ensure that it effectively addresses the identified HR problem.

**Assess Model Performance:**Verify that the chosen metrics closely relate to the problem statement and yield actionable insights.**Review Limitations and Strengths:**Acknowledge what the model does well and where improvements can be made.

### Step 6: Present the Results

Finally, after validating your model, present the results to stakeholders in a clear and actionable format. Utilize data visualization tools such as Power BI or Tableau to help simplify complex data and convey findings effectively.

**Communicate Insights:**Ensure that stakeholders understand the significance of the data and subsequent recommendations.

## Summary

Implementing HR analytics effectively requires a structured approach using a data science framework tailored for the HR context. By following the defined steps—defining the goal, identifying the HR problem, meticulously collecting and managing data, building an effective model, evaluating the model, and presenting clear results—HR professionals can derive meaningful insights that drive strategic decision-making. As HR continues to evolve, adopting robust analytics practices will be key to navigating future challenges and opportunities.

### Conclusion

The world of HR analytics is ever-changing and significantly influenced by technological advancements. Establishing a solid foundation grounded in a data science framework can allow organizations to maximize their HR potential, leading to sustainable success and workforce enhancement. Embrace the journey into HR analytics for better decision-making and improved organizational outcomes.

have learned learn the basics of the HR analytics so in first session if you remember we discussed the what are the

various names of the HR right how we should apply the basic concept related to the HR right so that is the basic

understanding that we had developed related to the HR analytics in first session in the second session we had

developed the basic understanding about the data right how we should develop the use the good dat data in order to

analyze and make some result related to the data analytics so in addition to that in first two sessions in this

session we will learn a framework through which you you can analyze the HR data so this framework that you can say

data science framework so any kind of analytics in any area that you are planning to apply this analytics you can

use this framework right so in HR area if we are applying so steps are same in marketing if you are applying or you are

applying any other area the steps are same but in so the framework that we we we will understand in HR context right

so what are the steps so here I have written the steps all steps of that data science framework right so that is the

content so first step is to define the goal right so that we are keep discussing in our sess

if you have to start this analytics first thing that you have to do you have to define the goal right so as we this

is the course related to the HR analytics so that is why we have written HR problem so any HR problem that is

what you have to Define right second step is related to the definition of HR problem should

cover so whatever problem that you are defining so you have to clearly Define the all things related to

that HR problem so right that is what you have to do next step that you have to you have the moment you have defined

the problem after defining it you have to collect the data related to it and manage it right so manage it means data

analysis structure data un structure data missing data quality of data so these are the all issues that we had

already discussed disc in the second so uh second session so this is the second step of the data science framework right

third after collecting the data you have to build a model right so in detail we will discuss in depth how you can build

a model in the HR so basically it is a matrix right that is a matrix that we need to develop so in the second session

if you remember we had discussed the how to develop the Matrix and what are the criteria for a good HR Matrix right so

here you have to build a model so that Matrix that you have to develop through which you will analyze the data the

moment you have developed this model right then you have to evaluate and critic the model why it is important

because we should know all matrics that we are developing right we should ensure that they are able to solve the problems

because all Matrix that we develop related to our problem they all all are proxy nobody no Matrix will give you the

closure they have proximity to the defined goal right so in detail we along with the example this thing also we will

discuss right next is the moment you have developed the model then you evaluate and critic the model right you

evaluate whatever what are the limitations what are the strength of that particular Matrix whether it is

able to capture the real phenomena or not whe whatever that uh data wants to communicate whether you are able to

understand or not through that Matrix right so these all things that you have to do in this particular step right so

in detail we will discuss it and then finally you have to present the results right related to that particular model

whichever model that you have developed if you feel yes this results are good right and giving you uh the better

understanding about the problem then you can deploy that particular model in your organization so

are applying this HR problem so I always suggest you go through this uh these steps right if you will randomly you

will do before if you have not defined the problem and collecting the data and then presenting the result without

evaluating that particular model that you have developed or that metrix so you may commit many mistakes while doing

this process so I suggest all HR managers to follow this these steps and apply the HR R Analytics tool so let us

start with First Step defining the HR problem right so being very clear on this is very important that defining it

is very important because the specific choices we make further about data and tools is fundamentally dependent on this

right so I hope you can understand why it is important because two reasons are there first reason is there if you are

not clear about about your goal what exactly that you want to do it right then you will not be clear about the

data which data that you are supposed to collect and which tool that you are supposed to use right so that's why it

is important so to define a problem so how you can define a problem like at how many people are

leaving right so then you can tell this is the percentage of people who are leaving from this particular Department

this particular reason people are leaving so you can do in-depth analysis of the at and then you will understand

you have to collect the data related to the attrition how many people are leaving the organization similarly

selection right so what kind of people if you want to do the analysis of selected people so out of 10,000 people

let us assume you have selected uh 200 people now you want to do the analysis of the selected people that quality of

high right how good these people are so now you understand you have to collect the data related to the quality of fire

because you want to understand how good they are if you want to understand their demography then you will collect the

data related to the demography so if your problem statement is is to evaluate the quality of quality of

people whoever whoever have been selected so you will collect the data related to the quality of fire right and

demography demographics of the selected people from where they are what is their age what is their expectation what they

like what they don't like if you want to understand these all things then you will collect the data related to the

demography so that is what I was saying in the first step it is very very important to define the problem clearly

if you are unable to define the problem clearly you will not be able to select which type of data that you are supposed

to collect and which tool that you need to apply to analyze that particular data so that is why it becomes very very

important to define the problem clearly right whether it is related to the any function so I already said to Define

selection training development performance compensation and then you can go in depth then you can go in depth

and understand what is the problem and then you can decide which data that you have to collect related to the

Recruitment and which tool that you are supposed to apply so in the first session if you remember we had discussed

the various types of the statistics so descriptive statistics diagnostic statistics predictive statistics and

prescriptive statistics so again which type of Statistics that you will select that the method to analyze the tool

related to which type of analytics uh that you will select which tool that you will select related to which type of

Analytics tool that you will select that also will depend on the goal right if your problem is related in term of

exploring something then you may uh select the tool related to the descriptive like for example if you want

to understand how many people are leaving right then here simple count that you can apply from which department

they are leaving which area they are living which gender is living more right so problem is to understand

that particular phenomena that attrition phenomena so simply that you can apply descriptive Analytics tool if your

problem is is there any relationship between age and people who are leaving in that case you can apply this uh

diagnostic so in that case you can plot a correlation graph right so if you want to understand the relationship between

work uh uh work experience and the people who are leaving from the organization right so if you want to

understand who is leaving more people who are having more experience or less experience who are leaving more in whom

uh there is a high level of atra rate early joining people or people who are having more experience so the moment you

define a problem in this way it clearly indicates that you need to select a uh you need to select a tool from a

diagnostic statistics right so here you need to you can build a plot right you can make a graph who is leaving more

experienced people or inexperienced people or people who are having a specific scale those people so which

type of people are leaving more so that is what you that is what so in the first step that what I'm I'm trying to make

you understand first thing Define your problem clearly and why it is important because of two reason first is what type

of data that you need to collect that is dependent on your definition of your problem and second which type of tool

that you will use to analyze the data that is also dependent right so if you are not able to Define this problem or

illc conceived it is not appropriate in that case you may not be able to achieve the desired goal which you you are

expecting to achieve it right so this is the first step that is why it is always important to Define your problem clearly

right whatever uh problem that you are dealing with so related to the HR because we are discussing to the HR so

you can take functions and then related to the any HR any fun HR related function define what is the problem is

and then think about the data and Tool it is dependent on that so if your problem is not defined clearly then you

may face this problem so this is the first step of the data science framework let us move to the second right so now

question comes what ah HR problem should cover so statement of what and statement of Y and statement of

desired outcome so these are the three things that a problem statement should cover right so statement of what so what

is the problem attrition is the problem why attrition is a problem right because people are less paid people are more

understand the Aton that we are talking about so why people are leaving so through the data that you have to

explain right and now what exactly that you want to do it right or what you are trying to achieve it so you you want to

achieve the percentage of people who are leaving right and you want to know in which department most of the people are

leaving right so that that is what you want in the example that you can take related to The Oar operations efficiency

so in how many days you are able to complete the recruitment what is the cost of recruitment per application

right that per application cost per candidate selected candidate cost right what is the cost so so that these are

the matrics that you can calculate and then you can defied whether HR operations were efficient or not but you

have clearly identified for efficiency these are the parameters that you are that you want to calculate right so that

is what you have to Define so basically it is asking about the Matrix that is what on through which you want to

analyze that particular phenomena so these all three things your problem statement should have in order to make a

good problem statement related to the HR analytics so statement of what statement of why and the Matrix which through

which you will analyze this particular phenomena if you have these three things you will not face any problem so I have

given you the example of HR efficiency so I have taken the example of this recruitment so in the

recruitment uh what is the problem number of applications are less or more related to why it is less because of the

advertisement like so now you can uh calculate how much you have invested per application so let us assume you receive

10,000 so what is the amount and the total amount that you can divide by the 10,000 the total amount that you have

invested for the job advertisement right so that is how you can calculate per application cost that you have invested

so so that Matrix that you have already with you through this you want to describe this particular problem so

these are the three things that you should have when you are defining the HR problem so I hope you would have

understood next is collect the data and manage the data right so from where you will collect the data most of the

data which is required to analyze the data right which is required to analyze the data that may not be the may not

provide you the complete data because some data is available in marketing department some data is available in

finance Department some data is available in uh operations department so sometime this hrms may not provide you

Matrix then think what could be the data source for the remaining data so this is the first step so you have checked your

hrms and what which data is not available think about that and then uh you can collect that data also so so the

moment you have collected the required data from various sources then what you can do then you can Define what are the

dependent variable and independent variable right so dependent variable is interest variable that is what I would

say so for example this work experience right if you want to do study related to the work experience right and

variables that may come under the dependent variable so simple I would say which which variable is your

interest variable second is independent variable what is the reason why more experienced people are applying to your

organization right so work experience is your study variable dependent variable interest variable why more work

experience people are applying that is what you want to know so whatever is the reason you can say that because of

employer branding because of the salary that you uh provide the kind of culture that you have so these

applying to your organization right so that is the that is how you have to Define after the collecting data how you

will decide this dependent variable which one is a dependent variable you can analyze what you can

analyze uh the your problem statement so in a problem statement what is your interest what exactly that you want to

know so from there you can drive or you can reach to the dependent variable and then from there whatever data is

available what could be the uh possible cause of that particular dependent variable why it is happening so through

the independent variable that you can do so when you are trying to establish some relationship between independent and

dependent variable you should have the logic behind it right so that lamp model if you remember in the first session

that is what we had discussed so lamp model so without any logic please do not consider all variables as an independent

variable right so you should have the logic uh this thing may lead to this particular thing right if that is not

there in that case you need to think about it right you need to think so all independent variable that you have to

identify through the uh logic right if logic is not there in that case you need to be worry about it without logic do

not select the independent variable so there has to be some logic then only you apply or you use that variable as a

independent variable right so the moment you have defined this variable independent variable dependent variable

whatever data was not there that also you have collected in a Excel format so now you need to perform three things

that you have extracted if that data is not as per the requirement right requirement for what

for the analysis right for example you are needed mean mean value right but mean value is not there right then what

you can do you can calculate the mean value and transform that particular data right so and then you can load on a

software through which you have to analyze the data so software that data visualization tool that we will learn in

this course pivot table through Excel powerbi and Tableau these are the three tools that we are going to learn so

before analyzing the data through these uh these three tools we need to uh transform that particular data and then

you have then we have to Lo load so these three terms that you can remember extract transform and load so whichever

data is not available then you have to extract if it is not up to the mark it means it is not uh in a same format in

which we require to analyze the data then we need to transform and then we need to load so these things that we

will learn when we will use the software in this course right so I hope you would have understood and second thing last

and uh not last I would say most important thing related to this data that we need to understand the

trustworthy if data is not trustworthy data should be trustworthy if it is not trustworthy otherwise garbage in and

garbage out whatever you will put you will get out right and you will not be able to make the meaningful results so

that's why you need to be very very careful about your data right you should check the trustworthy of n of all

trustworthiness of that particular data also so I hope you would have understood how to collect the data and manage the

data next build the model right so that model that I was talking about so that Matrix that you had decided right

right so now you have to decide the input data so if you remember that dependent variable independ

variable right is descriptive so all data that you have condu collected in Excel format so that will consider as a

particular step so now in a proc process step that you already know four type of statist uh analytic analytics that we

had discussed right so descriptive diagnostic predictive and Pres scriptive so in this step after finalizing the

input data you need to apply this particular analytical tool to analyze that data right and then you will get

the output so if this data is trustworthy your output also will be trustworthy if data is not trustworthy

then your output also cannot be trusted and you cannot take the decision base on this right so that is how you need to

build the model you need to input right and you need to check the output and you can apply so this is this is in

identified based on your problem statement you have already finalized the tool so after finalizing the input data

now you need to apply the analytical tool it could be descriptive diagnostic predictive prescriptive and then you

right next so whatever model that you have developed right now you have to evaluate and critic the model so in this

step what exactly that you are supposed to do in this step you have to evaluate so whatever variable that you have

collected right why it is you have collected how close it is to the problem that is what you need to understand

right because every Matrix that you through which you collect the data that doesn't give you the exact that doesn't

describe the exact problem of the related to your goal right but each Matrix that you are collecting it should

have the higher level of the proximity to describe that particular problem or to ident if y the solution so if that

both Matrix are giving describing the problem but one is close and another one is not that close right so for example

that I talk about the effectiveness of uh effectiveness of the Recruitment and selection activity right so two

right so both on the basis of both matrics you can establish the effectiveness of the recruitment right

so one Effectiveness that you can say number of application that has increased as compared to the last year and based

on that you can say that whether this recruitment was effective or not second that quality of people who have applied

to the available position based on that also you can say uh whether the recruitment was effective or not but

what do we want we want quality of people or number of application whether they are appropriate or not so it is

obvious we want quality of people right both so in this case you can see this quality of higher is having more

proximity to the effectiveness of recruitment than the application number of application although both uh matrixes

are very very important to understand the effectiveness of the recruitment but as far as my concern this quality of

high is more appropriate than the number of application right so so that is why I'm saying so you might be having

multiple matrices related uh to one particular problem so which one you have to select which one is more uh closer to

the problem right so that is how you have to evaluate and critic that particular model so that is how you have

to critic that particular problem particular model right and after CR after this then you have to present the

result whatever results that you are getting it right and if you find these results are suitable and then you can

deploy that particular model right so thank you I hope you would have learned the data science framework in this