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Six Reasons People Analytics Functions (and Leaders) Fail

By | Dirk Petersen |

People analytics has seen dizzying growth over the last few years and that growth is still accelerating. Almost every CHRO we have spoken to over the past year has shared that they will increase their investment in building out their people analytics capabilities. Research conducted by Insight222 in 2020 revealed that people analytics teams had grown in size in absolute terms, with 60% of companies surveyed intending to grow their people analytics teams further.

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In a sneak preview of this year’s People Analytics research from Insight222, due to be published next month, we can reveal that the growth of People Analytics teams has accelerated even further. In 2021, 75% of companies indicated that they are planning to increase their team size.

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Source: Delivering Value at Scale: A New Operating Model for People Analytics, Insight222 2020

Source: Delivering Value at Scale: A New Operating Model for People Analytics, Insight222 2020

But with increased resource comes increased expectation. This blog summarises the most common failure points of people analytics functions. Its purpose is to help CHROs and people analytics leaders avoid a few critical mistakes, make investment decisions more confidently, and achieve the expected business outcomes.

Failure Risk One: Pursuing Quality Data

The first failure risk that people analytics professionals commonly point to is data: specifically, pursuing quality data. We have seen incredible impact created from rather rudimentary data. This opportunity to create impact is missed when people analytics teams become obsessed with building the perfect data set. Teams get stuck in their pursuit of the perfect data set and neglect to deliver meaningful insights with the data already at their disposal. They subsequently miss the opportunity to gain early investment and buy-in from business stakeholders.

How to avoid the risk: Emulate the finance function’s approach to imperfect data. The finance function makes assumptions, clearly labels these, and assigns confidence levels to scenarios.  They don’t wait for the perfect data but deliver with the data that is available and the caveats that are necessary. They use the caveats as opportunities to make their case for investment in improving data over time.

Failure Risk Two: Growing Pains

The second failure point occurs when people analytics teams don’t plan ahead as they experience rapid growth. Many of our clients report that they experienced a tipping point where seemingly overnight the function shifts from trying to find value-adding projects to being inundated by requests. This is a critical career defining moment for every people analytics leader. Teams that are not ready for that moment fail, because the business will turn elsewhere. And you know you are failing when you hear statements from business leaders such as “oh, we don’t ask them, they are so busy.”

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