People Analytics Strategy: Building the Right Foundation

When it comes to leading an organization, 2020 was a year unlike any other. The shifting demands of a global pandemic, mass migrations to remote work, not to mention political and social unrest, all challenged even the strongest of organizations. Critical decisions had to be made almost daily to keep up with new regulations, employee expectations, and harsh new business realities.

So, how did some of the best companies make leadership decisions during these trying times? Increasingly, they looked for support through people analytics.

At its simplest, people analytics involves using data to better understand your workforce and to improve the quality (and speed) of organizational decisions. A strong people analytics strategy organizes and leverages data to reduce uncertainty and help decisionmakers select the best paths forward. The ultimate value from people analytics is when it is used to generate reasonable predictions about the future.

A robust People Analytics program relies on three core elements:

  1. A multi-disciplinary team to lead and manage the process and to oversee the strategy (the “who” and “why” questions);
  2. Constructing and maintaining the technical details of gathering, storing, securing, cleaning, and accessing the data (the “what” and “when” questions);
  3. A well-designed people analytics strategy to analyze and use the data to derive maximum value for the organization (the “how” question). 

This article will focus on this third element of “how” to build the right people analytics strategy.

Embracing People Analytics

While the potential benefits of people analytics are numerous, the ultimate value can only be as good as the quality of the data sources used as inputs. The model below outlines the key data categories to be considered when creating a robust people analytics strategy. To unlock powerful predictive insights, a strategy should be built on a solid foundation of employee demographic data, employee experience data, and operational outcome data.

Demographic Data

Employee demographic data refers to descriptive information about your employee population. What do you already know? Age, gender, tenure, manager status, educational background, performance ratings, exemption status, etc. Most demographic items are best captured when an employee is first hired and onboarded, though employee profile surveys can also be used to help update and expand demographic fields. The potential fields are virtually limitless.

During 2020, with the growing national conversation on diversity, equity, and inclusion, we saw an increase in organizations capturing ethnicity data and an expansion of gender categories to better support DEI efforts. Robust employee demographic data helps create a broad foundation for deeper analysis when combined with other datasets.

Employee Experience (EX) Data

Employee experience (or EX) data refers to the perceptions, attitudes, and beliefs that an employee forms directly from their experience working for an organization. This data is constantly changing and requires a measurement strategy that captures perceptions frequently and appropriately across the employee lifecycle.

DecisionWise helps collect EX data via employee lifecycle surveys, annual anchor surveys (engagement surveys), anniversary surveys, and 360 feedback assessments. EX data helps an organization understand the experience they are creating for employees across a myriad of planned and unplanned moments.

Outcome Data

This category includes anything that is tracked as part of your organization’s operational metrics. This includes traditional HR operational data such as attrition numbers, as well as non-HR data like sales or production metrics. Often these outcome items are the areas that you will be using people analytics to solve for later.

Interestingly, outcome data is often a limiting factor in a people analytics strategy. Why? Because human resource teams frequently do not have access to (or do not seek out) data sets outside of HR. I have worked with hundreds of HR teams over the last ten years and I am continually surprised how many organizations keep their “HR data” and their “operational data” in separate silos. This is why we strongly encourage HR leaders to help build a multi-disciplinary team to work on their people analytics initiatives.  All outcome metrics (and corresponding human custodians) should be sought out as potential inputs for people analytics and the team. 

Insights & Predictions

The ultimate goal of a people analytics strategy is to unlock insights and predictions that are useful to the organization. These insights are derived by combining and analyzing an organization’s demographic, EX, and outcome data. Some insights may become apparent simply by slicing EX data by a few demographics. For example, an engagement survey with demographic detail might reveal that female managers in a company’s Northeast region are feeling overworked. Other insights and predictions may require advanced statistical analysis to reveal. For example, correlating the impact manager 1:1’s is having on customer churn, or how a training program is impacting clinical outcomes within a healthcare organization.

Leadership workshop in the office

In many ways, the Human Resources function is still seeking to find its strategic voice within the organization. A strong people analytics strategy that leverages robust demographic, EX, and outcome data not only gives HR the tools to create a world class experience for employees, it also provides the insights and predictions to help HR inform business outcomes throughout the organization. The case is becoming increasingly strong for companies to organize and analyze their data to increase their understanding, to make data-driven decisions, and to leverage people analytics to improve the quality of their thinking.

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