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What I Wish I Knew When I Was Building a People Analytics Tool

Building a people analytics tool in-house is a transformative journey. Here we bring the insider knowledge that will prepare you for the road ahead, knowing precisely what to expect. 

Are you curious about what it takes to build your own people analytics tool?

In this article, we’re bringing you first-hand experiences and expert advice, so get ready to gain a deeper understanding and arm yourself with the needed knowledge! 

Professionals who’ve built people analytics tools, shared their experiences and insights, providing valuable answers to a question that resonates with anyone venturing into this field: What I wish I knew? 

This journey takes years 

“The maintenance on the following is enough to make me pull my hair out… APIs, data models, role-based security, UX enhancements/preferences, stakeholder speed expectations, data augmentation… You can spend years getting that right when there are already vendors that do all that. Spend your time doing the higher value analyses on initiatives that leaders need.” — Dan George, Founder and CEO at Piper Key 

Make sure the tool is the right move

“Think critically if a tool is even the right way to do things. Sometimes you aren’t in a position of readiness as an organization to turn something into a tool because you haven’t actually settled on a process or way of working yet.” — Nicholas Bremner, Ph.D., People Decision Science at Uber

It gets more costly than you’d expect

“People analytics products/tools are only one-third the value of people analytics yet tend to absorb two-thirds the budget or more if you try and build in-house” — Ryan Hammond, Analytics-driven HR leader at Datavant

Do you have data engineering skills? 

“Data engineering is key. Ensure that you have the necessary infrastructure and skills to design and maintain the data flow. Don’t cut corners, you don’t want to build something unmanageable. If it’s not a skill you have internally in HR (and it may make sense not to have it), make sure the data team has resources allocated to your project (in our case, we insisted on having 1 FTE fully allocated to our budget).” — Ricardo Tavares, Head of HR Analytics & Transformation at CUF

Unicorn data engineers are rare and expensive 

 “Good data engineers are expensive and the ones that really know HR and want to stay there are unicorns.” — Ryan Hammond, Analytics-driven HR leader at Datavant

Focus on analyses and data consultations first 

“Challenges of navigating internally approved infrastructure tools cost accrued by switching HR tech stack, etc. Would definitely encourage People Analytics teams to focus more on quick analyses and data consultations as a basis to understand what’s needed before embarking on the build journey, even if that’s the final destination. Time to value takes so much longer with build and teams need to keep delivering value in the meantime regardless!” — Patty Smith, People Analytics Manager at Cruise

The importance of synergy of skills in PA 

“I’ve worked on internal people analytics tools in different types of scenarios. What’s been true of these experiences is that impact is a function of having the capability to run a small multidisciplinary shop in-house. Having impact requires engineering, project management, a dash of user research, product management, and more. I wish I knew (and leaders did too) what it took before I jumped into building (and maintaining) PA tools” — Jason Eton Scott, People Analytics Advisor at Orgnostic

Prioritize proactive planning

“Once you turn something on, you can’t just walk away from it and expect it to run itself. Have a clear plan for maintenance and support that aligns with your user group(s). This means you’ll have to earmark time/resources to maintenance/updates, which could slow you down on future builds” — Nicholas Bremner, Ph.D., People Decision Science at Uber

In conclusion, insights from experts stress the need to carefully evaluate the decision, consider costs, prioritize data engineering, and focus on the importance of a multidisciplinary approach and proactive planning for maintenance and support. 

Armed with this valuable knowledge, take the next step by exploring the aspects of the building, buying, or blending approaches, where you can look at the pros and cons of different approaches and discover the perfect fit for your data-driven success!

This piece has been co-written with members of the People Analytics Lounge, an online community of People Analytics and HR leaders, professionals, practitioners, and enthusiasts. We want to thank everyone who took part in sharing their valuable insights and experiences! 

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