𝐇𝐑 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 ~ 𝐈𝐤𝐢𝐠𝐚𝐢
- 𝐓𝐞𝐚𝐦𝐐
- Jan 30, 2022
- 4 min read
𝐓𝐡𝐞 𝐏𝐫𝐢𝐦𝐞𝐫
Arguably the most practical tool and greatest potential for organisational management is the emergences of predictive analytics. Fitz – Enz and Mattox II
Analytics present a tremendous opportunity to help organisations understanding what they don’t you know... by identifying trends and patterns, HR Professionals and management teams can make better strategic decisions about the workforce challenge that they may soon face.–Huselid
We define ‘Predictive HR Analysis’ as the systematic application of predictive modelling using inferential statistics to existing HR people – related data in order to inform judgements about possible casual factors driving key HR – related performance indicators. The result of this modelling can be used [where appropriate] to make tangible predictions about particular results or people outcomes’
Put simply, we take the sophisticated statistics and quantitative analysis techniques that scientists use to predict things [such as what may cause heart disease or what might help to cure cancer] and apply them to information. We hold about people in organisation furthermore, where appropriate, we can also then apply these predictive models to make tangible predictions about particular results or outcomes [Eg employee or organisational behaviour], that we might expect to find, given certain conditions.
𝐇𝐮𝐦𝐚𝐧 𝐂𝐚𝐩𝐢𝐭𝐚𝐥 𝐃𝐚𝐭𝐚 𝐬𝐭𝐨𝐫𝐚𝐠𝐞 𝐚𝐧𝐝 ‘𝐁𝐢𝐠 [𝐇𝐑] 𝐝𝐚𝐭𝐚’ 𝐦𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧
To be able to realize the potential of predictive HR analytics all of us are reliant upon what current and historical data is available. Predictive HR analytics relies completely on good data; we cannot look for patterns in data when the available data is limited and sketchy. The success of HR analytics is completely reliant on the availability of good people – related information.
Increasingly HR functions are not necessarily faced with the problem of there being a lack of data availability – they are not necessarily faced with problem that there is too much data to know what to do with. Thus a challenge often faced by an HR analytics team is what to do with all the people – related data; one of the biggest challenges is getting that Data into the right format for analysis.
Skills and Qualifications
Measure of particular competencies
Training attended
Levels of employee engagement
Customer satisfaction data
Performance appraisal records
Pay, Bonus and remuneration data
𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Answerer the question: ‘What happened?’ this type of analysis summarises raw data to understand what has occurred, based on historical data, and helps to uncover patterns that can offers insights to explain the reasons for the occurrence. This allows learning from past behaviours and understanding how they might influence future outcome.
𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Answerer the question: ‘Why did it happen?’ In this type of analysis typically there are measures about the relationship between two variables, and motivation is to go beyond ‘what happened’ to understanding what the driver or explain for what happened? This knowledge can then allow us to take actions that enforce a designed outcome or militate against an undesired one, for instance, job satisfaction vs. retention; engagement vs. profitability; culture vs. Turnover; ethics vs. Profit; job satisfaction vs. Customer satisfaction.
𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Answer the question: what is likely to happen?’ uses a variety of statistical techniques to determine the probable future outcomes of an event, or likelihood of situation occurring. Note that, different than in the mentioned diagnosis analysis, there is a clear implication on the direction of causation. A causal relationship exists where the occurrence of one event is liked to another. For instance, recruiting source predicts retention; a change in professional Social network profile predicts absenteeism; training programmes predicts sales outcomes; projected return on investment [ROI] of new talent retention solution; forecast ROI of new compensation arrangements
𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨 𝐇𝐑 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬
Ü Define the Problems
Ü Format Hypotheses
Ü Collect Data
Ü Analysis Data
Ü Derive Insights
Ü Build Recommendations
Ü Visualize and Tell a Story
Ü Execute and Evaluate
𝐇𝐑 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 – 𝐈𝐤𝐢𝐠𝐚𝐢
On behavioural data requires a unique combination of experience across these four major domains
𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠: How hardware and software work together to ingest, store, manipulate and output Data.
𝐇𝐮𝐦𝐚𝐧 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐮𝐫: The science of behaviour is commonly known as psychology and has numerous subcategories and specialities. In HR Analytics, a subfield known as social psychology [and often one of its sub-disciplines, industrial organisational Psychology] is Primary branch of behavioural science which applies.
𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐌𝐞𝐭𝐡𝐨𝐝𝐬: Statistics is the group of mathematical principles concerned with analytical principles concerned with analysis and interpretation of data. Research methods are the techniques and strategies leveraged to gather Data for such analysis.
𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐜𝐮𝐦𝐞𝐧: Though Business Acumen is often a very generic term used to describe ambiguous competencies, in this application business acumen understands the practical business application of the insights gained and the context in which you are operating.
In the same way that Ikigai comes together to create balance across an individual’s life, these four areas of expertise hold their own critical worth when doing HR Analytics Well.
𝐏𝐚𝐫𝐞𝐧𝐭𝐡𝐞𝐭𝐢𝐜𝐚𝐥 𝐑𝐞𝐦𝐚𝐫𝐤𝐬
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