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Insight into Data driven HRA - Module 4
Human Resource Analytics (MBAHR304)
Chhatrapati Shahu Ji Maharaj University
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UNIT 4: INSIGHT INTO DATA DRIVEN HRA
The Foundations of Data-Driven HR
It is required for examining the data cycle that starts with data entry, moves into analysis, and ends in action.
Each portion of the cycle is underpinned by a foundational capability needed to ensure that the data can be turned into insights that drive results. There are three prerequisites to ensure sufficient data quality:
✓ There is a need for proper data governance;
✓ Sufficient analytical capabilities are required to turn data into insight;
✓ The organization has to adopt data as part of their culture and decision making in order to ensure that those insights were actually used to make decisions.
TYPICAL DATA SOURCES
❖ Operations
❖ Compensation
❖ Customer service
❖ Human resources information systems (HRIS)
❖ Learning management systems (LMS)
❖ Social media and nontraditional learning systems
❖ Engagement Surveys
❖ Performance management systems
❖ Interviews and estimation by experts
❖ Public data from outside the organization
Typical questions faced (survey)
For each source of data, it is important to know
❖ Where the data are housed,
❖ Who the owner is,
❖ How you will collaborate with the owner,
❖ How you will obtain and integrate the information with other data, and
❖ Ultimately, how valid and reliable it is
Organizations that did the above well, created a data plan that started at the end of the data cycle and worked backward. This meant identifying
the most critical audience,
understanding what decisions or problems data needed to be applied to,
identifying the appropriate metrics or data points, and
then working backwards through the data collection process.
The Challenge
Over a third of HR departments struggle to move beyond basic measurement and metrics (McLean & Company, 2018 HR Trends Report).
HR has more data than ever before, but often does not know what to measure or what to do with the data.
Unreliable data and lack of analytic capability are significant barriers to moving forward.
DATA ISSUES:
❖ Imagine for a moment that the vice president (VP) of human resources (HR) schedules a meeting with you, the leader of a talent analytics team
❖ to learn about the effectiveness of an HR initiative called Retain & Grow.
❖ The initiative began soon after your technology company acquired a smaller, company for its innovative processes, patents, and deeply skilled engineers.
❖ The program is also an extension of a failed program that was designed to stanch the loss of highly talented young employees who come to the company for a couple years of experience and depart for more lucrative jobs with better work/life balance.
❖ Your company is facing a talent crisis, and
❖ the VP of HR is coming to you for solutions.
Techniques for establishing questions
Open and Closed Questions
Funnel Questions
Probing Questions
Leading Questions
Rhetorical Questions
DATA CLEANING:
Data cleaning is a key element in HR analytics. Before you can analyze your data, it needs to be ‘clean’.
Data cleaning, or cleansing, is the process of correcting and deleting inaccurate records from a database or table.
Broadly speaking data cleaning or cleansing consists of identifying and replacing incomplete, inaccurate, irrelevant, or otherwise problematic (‘dirty’) data and records.
A common saying in data analysis is: “garbage in, garbage out”.
This saying means that you can put a lot of thought and effort into your data analysis and come up with lots of results. However, these results will mean nothing if the input data is not accurate. In fact, the results may even be harmful as they can misrepresent reality.
Data Cleaning – Why is it this important?
when data cleaning is seen as an important organizational effort, it can lead to a wide range of benefits for all
Streamlined business practices: Imagine if there are no duplicates, errors, or inconsistencies in any of your records. How much more efficient would all of your key daily activities become?
Increased productivity: Being able to focus on key work task instead of finding the right data or having to make corrections because of incorrect data is essential. Having access to clean high quality data, with the help of effective knowledge management can be a game changer.
Faster sales cycle: Marketing decisions depend on data. Giving your marketing department the best quality data possible means better and more leads for your sales team to convert. The same concept applies to B2C relationships too!
Better decisions: We touched on this before, but it’s important enough that it’s worth repeating. Better data = better decisions.
WHY IT HAPPENS?
HR data is oftentimes dirty. Dirty data is any data record that contains errors. This can happen for different reasons.
Missing Data Different Labels for one & the same job functions Multiple records/ Duplicate records Non-Matching records in different systems
DATA CLEANING – PROCESS
When cleaning HR data there are two things you need to understand. The first is data validity and the second is data reliability.
Validity
Validity is whether you’re actually measuring what you need to measure. Does the appraisal system only measure individual performance, or does it (also) measure who is best liked by his/her manager? Is data collected evenly throughout the organization, or is it skewed in one way or another?
Questions you can ask yourself to check for Validity, are:
➢ Does the data represent what we want to measure?
➢ Are there any biases in the way we measured our data?
➢ Was the data collected in a clear and consistent way?
➢ Are there outliers in the data?
Reliability
Reliability is about measuring the same thing over and over again and achieving the same result.
When you measure someone’s engagement in the morning you want to have a similar result as when you measure it again in the afternoon. This is because engagement is a trait that is relatively stable over time.
Questions you should ask yourself in this context for Reliability are:
➢ Did we consistently produce the same results when the same thing was measured multiple times?
➢ Did we use clearly documented data collection methods?
➢ Were data collection instructions followed each time?
Completeness: How thorough or comprehensive the data and related measures are known
Consistency: The equivalency of measures across systems and subjects
Uniformity: Ensuring that the same units of measure are used in all systems
Traceability: Being able to find (and access) the source of the data
Timeliness: How quickly and recently the data has been updated
Insight into Data driven HRA - Module 4
Course: Human Resource Analytics (MBAHR304)
University: Chhatrapati Shahu Ji Maharaj University
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