Insight into Data Driven HRA: Typical data sources, Typical questions faced (survey), Typical data issues, Connecting HR Analytics to business benefit (case studies), Techniques for establishing questions, Building support and interest , Obtaining data, Cleaning data (exercise), Supplementing data

1. Module - 4
Prof. Vijay K S Bapuji B-Schools, Davangere

2. Typical data sources
• Operations
• Compensation
• Customer service
• Human resources information systems (HRIS)
• Learning management systems (LMS)
• Social media and non-traditional learning systems
• Engagement
• Surveys
• Performance management systems
• Interviews and estimation by experts
• Public data from outside the organizationProf. Vijay K S Bapuji B-Schools, Davangere

3. HR professionals gather data points across the
organization from sources like:
• Employee surveys
• Telemetric Data
• Attendance records
• Multi-rater reviews
• Salary and promotion history
• Employee work history
• Demographic data
• Personality/temperament data
• Recruitment process
• Employee databases
Prof. Vijay K S Bapuji B-Schools, Davangere

4. For each source of data, it is important to
know
• where the data are housed,
• who is the owner ,
• how you will collaborate with the owner,
• How you will obtain and integrate the information with other data,
and
• how valid and reliable it is.
Prof. Vijay K S Bapuji B-Schools, Davangere

5. Typical questions faced (survey)
• Consciousness towards the responses
• Raise the issue of confidentiality
• Objective behind the survey
• Detailed information gathering during the survey
• Revealing their personal information
• Jargons, Abbreviations and inner meanings associated with the survey
• Wording, Phrases and Options
Prof. Vijay K S Bapuji B-Schools, Davangere

6. Typical data issues
• Biggest Challenges of HR Analytics?
• Finding people with the right skillset to gather, manage,
and report on the data
• Data cleansing
• Data quality
• Too much data to parse or not knowing what data is most
important
• Data privacy and compliance
• Proving its worth to executive leadership
• Tying actions and insight to ROI
• Identifying the best HR technologies to keep track of the
data
Red Marked – Data Issues
Prof. Vijay K S Bapuji B-Schools, Davangere

7. Typical data issues
1) Poor Organization. If you're not able to easily search through
your data
2) Too Much Data. ...
3) Inconsistent Data. ...
4) Poor Data Security. ...
5) Poorly Defined Data. ...
6) Incorrect Data. ...
7) Poor Data Recovery.
Prof. Vijay K S Bapuji B-Schools, Davangere

8. Five Characteristics of Good data
1) Accuracy
2) Completeness
3) Consistency
4) Uniqueness
5) Timeliness
Prof. Vijay K S Bapuji B-Schools, Davangere

9. Techniques for establishing questions
Data Is Only As Good As The Questions You Ask
Few Questions need to be asked ….
1.What exactly do you want to find out?
2.What Data will you use that can help?
3.Where will your data come from?
4.How can you ensure data quality?
5.Which statistical analysis techniques do you want to apply?
6.What HR Analytics method need to be developed, if any?
7.Who are the final users of your analysis results?
8.What else do I need to know?
9.What data visualizations should you choose?
10.How can you create a data-driven culture?
Prof. Vijay K S Bapuji B-Schools, Davangere

10. Techniques for establishing questions
Few techniques
- Closed questions
- Open Questions
- Probing questions
- Leading questions – Leading to some desired
- Loaded Questions – Straight forward questions
- Funnel Questions – Started as broad and then narrow down
- Recall and process questions
- Rhetorical questions – Phrases
- A word on tone – Emojis and Images
Prof. Vijay K S Bapuji B-Schools, Davangere

11. Obtaining data, Cleaning data (exercise),
Supplementing data
Prof. Vijay K S Bapuji B-Schools, Davangere

12. Obtaining data
- This talks about the collected data from various sources
Prof. Vijay K S Bapuji B-Schools, Davangere

13. Cleaning data (exercise
- Remove Unwanted observations - This includes duplicate or irrelevant observations.
Duplicate observations
Duplicate observations most frequently arise during data collection, such as when you:
• Combine datasets from multiple places
• Scrape data
• Receive data from clients/other departments
Irrelevant observations
Irrelevant observations are those that don’t actually fit the specific problem that you’re
trying to solve.
• For example, if you were building a model for Single-Family homes only, you
wouldn't want observations for Apartments in there.
• This is also a great time to review your charts from Exploratory Analysis. You can look
at the distribution charts for categorical features to see if there are any classes that
shouldn’t be there.
• Checking for irrelevant observations before engineering features can save you many
headaches down the road.
Prof. Vijay K S Bapuji B-Schools, Davangere

14. Cleaning data (exercise
- Filter Unwanted Outliers-
• Outliers can cause problems with certain types of models. For example, linear
regression models are less robust to outliers than decision tree models.
• In general, if you have a legitimate reason to remove an outlier, it will help your
model’s performance.
• However, outliers are innocent until proven guilty. You should never remove an outlier
just because it’s a "big number." That big number could be very informative for your
model.
• We can’t stress this enough: you must have a good reason for removing an outlier,
such as suspicious measurements that are unlikely to be real data.
Prof. Vijay K S Bapuji B-Schools, Davangere

15. Cleaning data (exercise)
- Handle Missing Data - you cannot simply ignore missing values in your dataset.
Two ways to handle this
• Dropping observations that have missing values
• Imputing the missing values based on other observations
Prof. Vijay K S Bapuji B-Schools, Davangere

16. Cleaning data - Techniques
• Get Rid of Extra Spaces
• Select and Treat All Blank Cells
• Convert Numbers Stored as Text into Numbers
• Remove Duplicates
• Highlight Errors
• Change Text to Lower/Upper/Proper Case
• Spell Check
• Delete all Formatting
Prof. Vijay K S Bapuji B-Schools, Davangere

17. Supplementing data
• This basically sourcing data from various sources
• Something that completes or makes an addition
• Add something to something to make it larger or better
Here add from where? From various other sources viz. Social media,
Some secondary research data, research survey findings ……
Prof. Vijay K S Bapuji B-Schools, Davangere

18. Thank You
Prof. Vijay K S Bapuji B-Schools, Davangere