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People Analytics Series: Introduction (Part 1 of 4)

February 11, 2020

The introduction of analytics into Human Resources is not a new concept and has been gaining popularity over the last decade or so. A recent survey conducted by Oracle suggests that 94% of business leaders utilize data in some way to conduct predictive analysis.

 

Despite the companies who participated in Oracle’s survey all having annual revenue of $100 million or more, it is only a matter of time before the integration of data in HR decision-making permeates further into the small and medium sized business space.

 

While there are benefits to having a dedicated and robust data housing system, there are steps any organization can take in utilizing existing data to enhance their organization and its direction. In this 4-part series, I will introduce what is meant by people analytics, and provide insights on how to prepare, collect, and interpret data.

 

For the purposes of this article, people analytics means using data about people to develop insights in order to make data-driven decisions within a business. An example of this would be looking into employee demographics, qualitative feedback, and attrition trends to determine if a one-size-fits-all benefits plan is optimal.

Before one dives into preparing for any people data-related project, it is important to first think about where your organization is currently, where it wants to be (and how to close the gap) with regards to how resources are currently being used. To assist with this, below are tiers in helping to understand analytics maturity within an organization:

  • LEVEL 1: Descriptive (What happened?)

    • The first tier is essentially the ability to read reports generated by people systems.

  • LEVEL 2: Diagnostic (Why did this happen?)

    • This level is typically reached when one is able to not only read reports but find causal factors between multiple sets of data.

  • LEVEL 3: Predictive (What would happen if?)

    • Level 3 is where the approach becomes more proactive (as opposed to reactive). At this point, an analyst would be able to determine likelihood of what would happen (or not happen) based on causal factors identified.

  • LEVEL 4: Prescriptive (What should we do?)

    • At this level, algorithms within a system would analyze and forecast what actions should be taken.

 

Understanding where one’s organization is situated on this people analytics journey will be instrumental in gathering and utilizing the tools available before one begins.

 

Stay tuned for next month’s installment where I will discuss how to prepare when taking a data driven approach. If you have any questions in the meantime, feel free to drop a line.

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