Posted: 24 Feb. 2022 12 min. read

What Holds Your People Analytics Strategy Back?

Four Common Challenges and Practical People Analytics Solutions

By Eric Lesser and Peter DeBellis

Over the last several years, companies have spun up a flurry of activity, establishing or attempting to advance their people analytics strategy. Whether to gain insight into the productivity of a more distributed workforce, understand and address retention challenges, or inform more sophisticated workforce planning, companies have been investing in a range of people analytics solutions and technologies. By doing so, they look to have a direct impact on key cost and revenue drivers, improve organizational decision making, and more effectively apply their human capital analytics to differentiate themselves from competitors in the market.

However, many companies struggle to progress beyond the initial stages of building their people analytics strategy and capability. Specifically, Deloitte’s 2020 High Impact People Analytics Study found that over three quarters of organizations, while perhaps possessing a compelling future vision for better people analytics, are currently focusing on providing insights primarily to the HR organization, tackling basic data quality issues, and establishing fundamental analytic capabilities.

Only 18% of companies have moved to a higher maturity level at which they are applying broader approaches and advanced tools, along with increased consultative skills and customer centricity in support of critical business challenges. This bottom-heavy distribution of people analytics maturity is very similar to what we found in our 2017 High Impact People Analytics Study, indicating limited progress across the domain in the three years between our studies.i

What makes it so difficult for people analytics teams to move to higher levels of maturity? Four common challenges hold organizations back from progressing to the next level:

1. Lack of focus on pressing issues

Initial pilot projects often fall into a “safe” zone where the team and analytics leaders are comfortable with the quality of the data as well as the depth of their related analytic capabilities. Often, these projects rely on data sources directly within the control of the HR organization and involve tried and true analytic techniques and visualizations. On the surface, this approach makes sense: no one wants to risk their initial forays into a new discipline and engage with key stakeholders if they are unsure of the accuracy of data they are using or the quality of the analyses.

But one important dimension often missing from these early efforts is a clear connection to strategic value. Without such a linkage, people analytics teams can spend valuable time and resources working on projects with limited potential impact. Further, lacking a mandate tied to a relevant challenge facing the organization, it is unlikely that the project will garner the time, attention, and, perhaps most importantly, the resources needed to drive actionable solutions.

Identifying use cases that address key concerns of HR and business leaders is central to overcoming this hurdle, increasing the likelihood of success of early people analytics efforts, and maintaining momentum further down the road. Although potentially impactful projects may involve data that is less than perfect (almost all projects face this challenge in one form or another) or require more challenging analytic approaches, it is imperative that analytic leaders avoid the temptation to only take on early-stage projects that seem simpler or involve familiar approaches.

2.  Difficulty communicating insights

A disconnect between the people analytics team and its audience can bring early workforce analytics projects to a grinding halt. Analytic practitioners often are well versed in the language of statistics, artificial intelligence, and machine learning, but may be less experienced in crafting a message that is easy for non-statisticians to digest.

Consider the following scenario:

A company is interested in understanding the factors that influence the retention of top salespeople. The people analytics team develops a predictive model and identifies a set of predictive factors and other statistical insights. However, the output presented to senior leadership is full of difficult to understand graphics and statistical language that make it challenging to discern the key points and turn the insights into action. At the end of the meeting, both senior management and the people analytics team are frustrated by the inability to get across their messages and leave without a real mandate to move forward.

One approach that can help bridge this divide involves leveraging “translators,” individuals experienced in taking complex ideas and transforming them into clear and accurate visuals and graphs to help make it easier for stakeholders to digest analytic findings. Translators often possess consultative skills they apply to both help develop compelling visuals and tell stories shaped by an organized and engaging verbal and written narrative.

In addition to translating data that has already been manipulated, HR analytics functions can also deploy interactive dashboards that allow stakeholders to work with data by themselves and test out different scenarios and assumptions. The visceral activity of manipulating the data can often make it easier for stakeholders to comprehend analyses and make connections between different data points. This, coupled with guidance from the people analytics team, can help executives understand how to effectively visualize and interpret the data in support of better decisions.

3Overcoming executive bias

Even if organizations focus people analytics projects on impactful issues and effectively communicate the outcomes of analyses, the simple fact that people analytics is a new capability can raise concerns among senior stakeholders. And conflicts with generally accepted data sources from areas such as Finance and Operations (e.g., disparate attrition rates being reported based on differing underlying data or calculation approaches) can also reduce executives’ level of comfort with potential findings. But perhaps the most challenging source of skepticism is when findings defy conventional wisdom and challenge the mindset of senior leaders who pride themselves on “knowing their people” and “trusting their instincts.”

One example where this often plays out is in the world of talent acquisition. Leaders, who have recruited from the same universities for many years (sometimes including their alma maters) may be skeptical when presented with findings suggesting that candidates hired from these schools are similar, or even inferior in terms of retention, promotion, or other measures of success when compared to hires from other institutions. These and many other individual biases—both conscious and unconscious—can challenge the adoption of people analytics at the executive level if left unaddressed.

Early engagement of leaders across multiple fronts can help to raise the visibility of and overcome traditional misconceptions and common biases. Indeed, our research on people analytics found that high performing companies invest exponentially more time in stakeholder management at the “bookends” of the analytics process compared to their lower performing peers.ii This means they spend more time with customers up front understanding challenges and defining needs, as well as on the back end of the process when sharing insights and action planning.

One additional strategy to help overcome executive bias is to reach out to informal influence leaders within the organization to socialize findings before meeting with senior stakeholders. Gaining support and validation from individuals who leaders trust can help analytics practitioners gain credibility, which can be especially useful when findings may run counter to an executive’s preconceived notions.

4. Scaling existing technology

The start-up of a people analytics function is often on a shoestring budget, leveraging existing technologies within the organization. While these tools may be sufficient for initial pilot efforts, many analytic teams soon realize it is difficult to take on a greater number, or complexity, of projects for a variety of reasons, including:

  • Data quality issues, resulting from challenges such as multiple source systems, inconsistent governance, and organizational silos, often need to be reconciled before extracting and analyzing data
  • Manually extracting data from multiple tables and databases requires significant amounts of time and effort to cleanse and normalize the data
  • Existing tools have different security levels and approaches, making ongoing access management tedious and time consuming
  • Underlying databases and data warehouses can change as result of technical or organizational modifications, requiring ongoing manual updates to dashboards and production models

To increase the capacity of your people analytics function, consider investing in analytics technology that facilitates data access, extraction/translation and ingestion, analysis, and visualization. The market for these types of tools continues to grow, with more than 50% of companies planning to invest in people analytics-related technologies over the next 12 months at the time of our last study. More mature organizations are twice as likely to use dedicated people analytics solutions and software compared to their peers.iii

Taking the next great leap forward

For excellence in people analytics, taking the next step in their evolution requires more than just clean data and analytic prowess. Successfully tackling impactful business issues requires analytic teams to advance their skills in areas such as business acumen, information design and storytelling, and stakeholder management. And, technology can help to reduce complexity, increase the capacity to take on more, and more advanced, analytic projects, and foster trust among stakeholders. By applying practical solutions to common challenges, people analytics leaders can increase the likelihood that their functions will advance beyond the early stages of maturity to deliver insights that spur action, improve decision quality, and drive greater business impact.

iSeven Top Findings for Driving High-Impact People Analytics, Deloitte Consulting LLP / Madhura Chakrabarti, PhD, 2017.
iiSeven Top Findings Making Greater Business Impact Through People Analytics, Deloitte Consulting LLP / Peter DeBellis and Zachary Toof, 2020.


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