# Comprehensive Guide to HR Data Preparation in Analytics

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## Introduction

In today's data-driven world, Human Resources (HR) departments are increasingly leveraging HR analytics to enhance decision-making processes. This article delves into the vital aspects of HR data preparation, underscoring its significance in ensuring that the data analyzed is accurate, reliable, and impactful. We will explore the types of questions HR professionals must ask concerning their data, how data can be measured, and strategies for preparing HR data effectively for analysis.

## Understanding HR Analytics

HR analytics involves the systematic collection and analysis of workforce data to improve an organization's performance. By translating data into actionable insights, HR can make informed decisions about hiring, retention, training, and overall employee satisfaction.

## The Importance of Data Preparation

Data preparation is the process of collecting, transforming, and organizing data into a format that is suitable for analysis. Poorly prepared data can lead to inaccurate insights and misguided decisions. This session will cover the foundational elements of data preparation in HR analytics, allowing practitioners to ask the right questions concerning their data.

### What Questions Should HR Professionals Ask?

#### Why Questions are Crucial

Regardless of how trustworthy one might feel about their data or the analytics tools used, continuously asking tough questions is essential for maintaining a high standard of data integrity. Here are fundamental questions HR professionals should consider:

**What is the source of your data?**

Understanding the source is crucial since data may come from internal HR systems or external benchmarks. A reliable source enhances data integrity.**How well does your sample represent the population?**

When generalizing findings, ensure that your sample closely aligns with the characteristics of the entire workforce.**Does your data distribution include outliers?**

Outliers can skew results. Identifying and understanding their impact is vital.**What assumptions are inherent in your analysis?**

Clearly define assumptions to validate the context in which the data is interpreted.**What is the reliability and accuracy of the data?**

Assess the completeness, applicability, and variation within the data set to prevent errors in decision-making.

## Measuring HR Data

Data measurement is pivotal to the analysis process. Different methodologies can enhance the clarity and precision of HR data, and include:

**Absolute Values:**

Present data in raw numbers (e.g., number of applicants).**Ratios or Percentages:**

Represent data as ratios (e.g., male to female ratios) or percentages that provide context to the figures being analyzed.**Percentiles:**

This method helps in comparative assessments, such as gauging performance against benchmarks.**Correlational Information:**

Utilize correlations to demonstrate relationships, for instance, linking performance ratings with sales metrics.

## Data Preparation Strategies

Once the data is collected, several strategies can be employed for effective analysis:

### 1. Data Reliability and Accuracy

Examine the reliability of data sources and ensure data accuracy by checking for completeness and correctness to establish a strong foundation for analysis.

### 2. Organizing Your Data

Document all matrix processes clearly. Divide metrics into six categories relevant to HR functions, such as recruitment, performance management, and compensation.

### 3. Establishing Metrics

Every Key Performance Indicator (KPI) in HR should correspond to measurable metrics. For example:

**Recruitment Efficiency:**The number of candidates per position filled.**Employee Retention Rates:**The percentage of employees remaining after a year.

### 4. Documenting Definition and Processes

Each metric must have a clear definition, including:

**Mathematical Formulas:**For example, absenteeism can be calculated as the total number of absences divided by the total number of employees.**Purpose for Capturing Data:**Explain why this data is collected and how it will be used in the decision-making process.

### 5. Information Accessibility

Ensure the documented metrics display the source of original data for verification purposes. This transparency improves trust in the analysis results.

## Conclusion

HR data preparation is a critical step in harnessing the power of HR analytics effectively. By asking the right questions, employing suitable measurement methods, and meticulously preparing data, HR professionals can derive meaningful insights that lead to informed decision-making. Comprehensive data preparation not only enhances analytical outcomes but also supports the overall goals of the organization.

analytics we had learned the basics about the HR analytics what this analytics is what type of questions that

can be answered in what situations a manager can take a decision right so these are the things that we had learned

in the first session in this session we will learn how to prepare this HR data right data which you need to collect and

then you have to transform you have to make some changes before analyzing the data so here we

will learn how this data should be collected what type of preparation that we should do before collecting the data

and after collecting the data what we should do so right here so the data preparation thing that is what we will

understand right in this session so let us start so this is the content of your data so questions related to the data

that you should ask so what are the questions that you should ask related to your data so that you will see your data

is accurate reliable and applicable so what type of questions that you should ask in first thing that is what we will

discuss next thing that we will discuss about the data measurement how this data should be measured whatever data that

you are right and third thing that data preparation how this data should be prepared what are the things that we

should consider right the moment we have decided the measurement of data then we should think about how we should develop

the HR Matrix through which we will collect uh through which we will analyze the data right and what are the criteria

for good HR activity metrix right any activity that is taking place so what are the criteria so these are the things

that we will discuss in today's session so let us start with the questions that a manager should ask to himself or the

team which is going to deal with the data right so this is the thing that I always say no matter how much you trust

your Quant or your data do not stop asking them tough questions it does not matter how much you rely on your data or

your numbers but is still you ask the tough questions right so what are those tough questions that you should ask

related to your data that is what we will discuss but this is the thumb rule for the data preparation it does not

matter how much you trust your number if you have a 100% trust also then also you should ask certain questions so what are

those questions that is what we will discuss right first question that you should ask related to the data what was

the source of your data from where your data was collected right so as you know there could be many sours of the data

some data that you will get related to the HR internal right which is there within the organization and for

some metrics you have to collect the data from outside also so outside in the case of salary right in the case of

salary if you have to compare whether whatever salary that you are giving it is equalent to your competitors or not

so in that case you are collecting the salary or CTC data from outside the organization right so you need

to understand from which source through which source you have collected the data if that source is not reliable then

reliability of the data may be question questionable right so that is why I hope you can understand now why the source of

data is important right in the similar way in internal also sometimes some data is not available within the HR

department for example that performance rating of all employees is available with you but that sales data how much

sales that each individual has achieved right that data may not be available with you right so in that case if you

have to correlate performance rating and sales data right you have to correlate these things then also you have to take

that sales data from the marketing or sales department right so whe although it is internal data but still you have

to ask what is the source of that data right so in so data can be collected through internal sources or external

sources right so that is why it is important to ask the first question that is the what is the source of data so if

you believe your source is reliable then you can rely on the data also if you believe that source is not reliable

right from where you have collected the data then your data is question may be questionable and your results through

that data may be questionable so that is what you can put it as a limitation if you don't have any other source to

collect the reliable data if that is the only source and you are not sure about it you can collect data but you can put

sample the whatever sample that you have collected because it is very difficult to do the analysis on the entire

population that so I have already given uh the example of this external data of salary so if you have to compare the

salary for one job position with your competitor then it is not possible to collect the salary structure or CTC

information about all employees who are working with your competitor right so that is not possible so for for this

kind of analysis you have to take some sample from if five competitors are there so from each competitor you can

take five employees six employees it is up to you right so what do you have to think about whether the sample is able

characteristics are not matching then you cannot make the prediction about the population characteristics right I hope

you understand what I'm saying sample characteristics and population characteristics should be same if both

are not same then you will not be able to make the predictions about the population car istics based on the

sample analysis right so that similarity that you have to check right so before the data analysis if you whatever data

that you are collecting then you should ensure that whatever sample that you have taken for the analysis that same

sample should have the similarity with the population characteristics so that you can predict the something about the

outlier right so if outlier are there then so then if your answer yes or no outlier I hope you understand like

whatever is the normal data so CTC that you are collecting for manager position right in all organization that you said

have collected the data of six manager so this 2 lakh will be the outlier right so so values which are

very low or extremely high as compared to other samples right so that is what you can say outlier so

if question is yes your data include the outlier the next question that you should ask how did they

affect your result because if you will include the outlier you will not understand how it impacts

the result then your analysis may be misleading right so if you don't want to give misleading

result or if you don't want to make inference uh misleading inference based on your data analysis then think about

the outlier right what assumptions are behind your data analysis so when before data analysis you need to clarify what

are the assumptions that your data analysis is having right that is what clearly you have to Define that

particular thing what are the assumptions why did you decide that particular analytical approach so

whenever you will analyze the data so before analyzing you have to take one approach so you can select one of the

approach from descriptive statistics right diagnostic statistics predictive statistics or prescriptive statistics so

whichever analytical approach that you have selected from this analytical approach descriptive diagnostic

prescriptive and predictive right so you have to give a reason behind it what is the particular reason behind this

particular approach that you have selected and what are the Alternatives is available if you are not selecting

this particular approach then what or the alternatives are available so these are the questions that you should keep

on asking so here you can see some of the questions are about the sources of the data some questions are about the

data analysis some questions are about the representation right approach so these are the questions that you must

ask yourself before analyzing the data in order to increase the validity and reliability of your data next aspect

whatever data that you are going to collect HR related data next how you will measure that data how you will

measure you will take it as a number absolute value like number of applicants number of applicants right so

have you decided to take it as a uh number right or in the form of ratio or in the form of percentile right so that

you have to decide so Rao that also you can decide how many applicants are there number Rao male to female Rao so what is

the male to female ratio is there so this data you can present it in the form of percentage like 24% is males are

there and 64% is female are there and 2 is to three Rao also you can present it so it is up to you which type of major

that you want to do for a particular data in the same way percentile would you like to present the data in the

percentile percentile format so here result that you are declaring right so that is what result that you can declare

the way you would have seen the cat result common admission test result is there that is also presented in the form

student are ahead of you right so that tells you what is your position among all students right so if you are having

98 percentile it means 2% students are before you right so that is how you can present this data so result data that

you can present in the form of the percentile fourth form of data presentation that is what you will see

present without in just in number format that may not give you the very big meaning but when you will present in the

correlation format so one example that I have already given here second example that I can give

sales Target achieved so if performance ratings are high and sales Target achieved also High

it so that data gives you the meaning right so here you will be able to see some pattern same thing when you will

see leaders behavior and employee engagement so Leader's behavior that you can present in the form of whether

leader is respecting his or her subordinate or not if leader is respecting and then you can see the

engagement level so 1 27 level that is what you can 1 to 7 scale you can measure the engagement and 1 to7 scale

you can measure the leaders Behavior so if leaders behavior is employee oriented right relationship oriented and then you

can see the engagement so in the case of relationship oriented respect oriented task oriented so in which orientation

leaders uh behavior is having the impact on the engagement when we are presenting this dat in the form of correlation that

will give you the aot information so second aspect that you need to decide about the data how you will measure this

data whatever HR data that you are going to collect how you will measure will you measure in the form of absolute number

will you measure in the form of ratio or percentage or will you present the data in the form of percentile and will you

present the data in the correlated information right so because each data has a nature and how it should be

presented so that it will give you the more meaning and more information and it will increase your ability to analyze

more so that is what you need to think and then you have to decide which type of measurement you will do with the

particular data right so I hope you would have understood the importance of this data measurement right so now data

preparation so what are the aspects of the data that is what you have to check so reliability of data that I already

said if from which source that you are collecting the data if that source is not reliable then your data reliable

reliability Also may be questionable same thing with the data accuracy right how accurate that data is so that is the

thing that you can see data completeness whatever data that you have colle collected for the analysis whether

it is complete or not data variation how much variation is there in the data data applicability is is there and missing

data information that is what you can check so these are the on the on these parameter you need to check your data

that you have collected so how reliable it is how accurate it is how complete it is how much applicable it is right so

these are the parameter on which you should check the data preparation right so if any one of the parameter that you

so that before the analysis itself you can correct that particular data and you can transform that data you can add

something you can check the sources you can do something but after the analysis report is prepared decision is taken it

may be difficult so before data collection and after the data collection these issues should be raised I always

suggest before the data collection check the reliability and validity of the questionnaire that you have developed

and reliability and validity of the source from which source that you are going to collect the data so if source

is reliable go ahead and take the data if you have a question about the source think again and again and think about

Alternatives next so once you have thought these all things then you have to think how to develop the HR metrix

matric every kpi so you would have seen kpi in the organization is having the number there is no kpi which is not

having the number all kpis are having the number right for example example engagement Employee Engagement you need

activities right so one activity for employee engagement that you have decided so when HR department has

decided for employee engagement one activity that you will give some information about the job Pro job

activities every day to the each employee so how many activities one related to one activity so that one

activity information is given to the employee or not so that is there so that one is the kpi in the same way for a

recruitment so recruitment let us assum 20 employees that you need to hire in next one year for senior level so you

have decided you will multiple activities like you have to do the job posting right advertisement and then

application screening that is what so for for first month you have decided the job postings on a multiple sources so

your your kpi is one activity on internal source and one activity for external so every month you have to do

two job posting one on internal and one on external so kpi is having to your kpi is one for internal and one for external

so both are number so these numbers are the matrick because they are measuring something right so that kpi that is what

you can see something they are measuring so these all kpi so whatever kpis that during the goal setting process some KRS

would be there and in order to measure that K progress towards that K some kpis are there so that those numbers that you

have deced so every number is a matric whatever the number that you see in a kpi that is a matrix so you think about

it now question comes how to develop the Matrix so I always say HR department if you are thinking about the HR department

so these are the six activities that HR department does what is what are those six activities

Recruitment and selection Learning and Development performance and compensation so these are the six activities you

think about these activities so in prev uh in upcoming sessions you will learn the process of each function right so

what is the recruitment process what is the process of selection what is the process of learning what is the process

of development what is the process of compensation what is the process of performance right so these all processes

you will learn in the upcoming sessions right so the moment you have understood the processes think about what are the

recruitment first challenge is employer branding how strong your employer brand is that you want to know right if you

want to know how strong your employer brand as right so what you can do how we measure the strength of employer

brand employer brand strength we measor through how many people have left your organization and how many people have

joined your organization from the competitor right how many have joined to the competition and how many have left

for the competition so right so that is how you can see so for example from your organization

10 people have joined your competitor and 12 people from the competitor have joined you so you can

see two members additional member have joined you so it indicates that your employer brand is more stronger than the

your competitor so first what you did first you identify the problem and then you thought which type of data will help

you to understand the extent of that problem so you decided the leaving people and coming people that you will

understand from the definition of that particular problem so you understand that particular concept then decide

which data will be more applicable collected data and calculate so related to each function that is what you have

to do related to each function you have to identify certain problems challenges that manager is facing and then you make

a list of variables based on that collect the data analyze it and uh make the decision so first you need to think

about the function under which function which problem is there and then collect the data why I am focusing on problem

because if you will collect the data without problem then what is the use of that data right so you have to make a

decision in order to solve some problem in the organization so that's why I'm saying you make a index Matrix first

related to those issues for which you your organization is facing the problem if attrition is not the problem in the

organization people are not leaving the organization then why why you want to waste your time to analyze the attrition

when Atri is zero in your organization people are not living at all then why you want to analyze some other problems

are there go ahead and analyze those problems rather than wasting your time in attration because that has to be a

problem a problem and problem is there then only you collect the data related to that develop the metrix analyze it

and make a decision don't waste your time on those issues for which there is no problem in the organization right so

that's the first aspect how you identified these all problems second step is divide these all problems as per

the functions so all HR related problem that are there that you divide into six under these six functions related to

various steps of these process so these steps and challenges that you that manager face and what are the steps

of each process that is what you will learn in coming so what I'm giving you whatever problem that manager is facing

be able to categorize this is the recruitment Matrix this is the selection Matrix this is the training Matrix this

is the development Matrix this is the performance Matrix and this is the compensation Matrix so I hope you would

have understood the categorization of data uh categorization of these Matrix also right so I hope it would have been

clear to you how to develop the Matrix related to the HR so few criteria that is what we can learn right that we

should uh keep in our mind while developing The Matrix so that we will be able to develop some good activity

are developing it should be documented right it should not just you have developed and then you forgot you

calculate and you have forgot no whatever Matrix that you are developing you document and that process of

documentation I already said you divide all metrics into six categories some matrics related to recruitment

can divide and then you can document it second Matrix should have the narrative definition so whatever that you are

going to capture let us assume the percentage of absentismo total employees absent number of

employees who are absent divided by total employees and multiply by 100 so on that particular day what is the

absent ISM rate right so that is what that is what any you can calculate so you have to Define these all things what

do you mean by this uh absentismo there has to be a mathematical formula even if it is Count also right number

that you are presenting that is also a formula so if it is even count also then also you have to have a mathematical

formula for every metric if matric is unable to measure something then that matric is not a matric right matric

should be able to measure something so for that you need to have a formula if counting also that is also giving you

some number and based on that number you can interpret something so that's why counting also in mathematical formula so

you should have the mathematical formula for every metric that you are developing related to these HR

functions right next criteria every metrix should provide the information as what to calculate and right what you

have to calculate that is what every metrix should provide and you should explain clearly every metrix should

include the description why it is being captured whatever information that you are capturing through the Matrix then

you should explain why you are doing that because I I already said if you are collecting the data which is related to

that there is no problem right and there is you don't have any plan to use that particular data in future in the

planning process or solving the or making the process better then why to collect that data so if you need that

data then only you go ahead and collect the data so that's why this this description is important related to each

it next criteria you should categorize these all metrics right so that I have already explained how you can categorize

in among all six categories every Matrix should indicate the official source of the row data

right so your mric will calculate something so that data from where that data will get it so what is the source

of the data that also should mention so that anyone who is using that matric that person should be able to understand

what is the source of data and what is the reliability of that analysis right so every metric should explain the

official source of the row data so thank you I hope you would have learned the basics of data preparation related to