May 30, 2019

The Process of Determining the Right HR Data Science Delivery Model

Jason Ezratty
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Uncovering the right HR data science delivery vehicle isn’t always straightforward. For people analytics teams, the road may be rocky if they can’t illustrate the value of their findings to executives or lack the experience to drive solutions based on the data insights.

A people analytics team, for example, found that “distance between home and work” is a top predictor for turnover of retail workers. So what are the next steps?  If the team is unsure, they should call in subject matter experts from talent acquisition, legal, and other areas to identify what matters and develop appropriate recommendations.

Tips on Creating the Best People Analytic Teams

If you don’t have an in-house team that is sufficiently large or experienced with the issues at hand, there are alternatives. Many organizations start with a small internal team and grow organically over time. Bringing in the expertise of trusted individuals outside of HR can offer tremendous value. Team members from other departments also benefit by working on a new project that may have significant business impact.

Most organizations have co-sourcing in place, such as an outsourced engagement survey that delivers  drivers of engagement results. Outsourcing other use cases might include matching internal and external talent to work on the project, incorporating contingent workers into workforce planning, or simplifying manager experience with bots.

5-Steps to Identifying the Optimal Service Model

Every service delivery model has the potential to be successful. Here are steps to help you figure out the evolution of your service delivery:

  1. Prioritize business needs. If clients are approaching you with questions, ask: what has the most business value and is the client willing to change? Don’t be shy about probing clients to clarify that you are solving the right problem. For clients without questions, Patrick Coolen’s article 10 Golden Rules of HR Analytics is an excellent resource to kick start the discussion.
  2. Determine what you can outsource. Part of being a business leader is making rational decisions about what work is ‘mission critical’ and what can be done by others (i.e. extended team members, outsourced, etc.) and managed by you. This creates focus for the people analytics team and avoids spending efforts on projects that are time consuming and/or expensive.
  3. Take stock. To understand your company’s readiness for data-based decision making, ask: how strong is your people analytics team; what delivery challenges are most important to solve, and are there existing leadership biases for service delivery alternatives?
  4. Do some homework. Once the process is started, think about how you can help clients answer questions to derive the most value from ongoing machine learning or to refresh the analysis? What capabilities exist internally and how easily can you tap into them? What providers offer faster approaches and needed expertise as an alternative to performing the data science work from scratch?
  5. Create the case for change. What is the collective business value for being able to support client needs? At current capacity and expertise, when can you deliver? What alternatives are there for different business needs (build, buy, rent, outsource capability) and how does that change speed and likelihood of execution?

Understanding your organization’s maturity and readiness for analytics, how to respond to current and future demands, and having a business leader’s mindset of the analytics function are critical for delivering anticipated value in people analytics.

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