Data-Driven Decision Making: People Analytics Guide

What is data-driven decision-making?

Data-driven decision-making in business is a process that allows organizations to chart their course and strategy using data to be reasonably certain that their decisions make sense, and to evaluate in retrospect whether those decisions were the right ones. Organizations that rely on intuition for workforce decisions face 23% higher turnover costs than those using people analytics, according to recent benchmarking data. Why does that matter?

• Improved customer satisfaction and retention

• Sharper strategic planning grounded in facts

• Faster, more confident pivots when markets change

Most HR teams now collect data from HRIS systems, engagement surveys, and performance reviews. HR leaders now manage data from 15+ systems on average, making it harder to identify which metrics actually predict turnover or engagement. Organizations struggle to identify actionable insights when 60-70% of collected data remains unused or unanalyzed. The challenge: separating signal from noise to focus on metrics that drive business outcomes. Organizations waste an average of 40 hours per quarter analyzing metrics that don't predict business outcomes.

What is data-driven decision making?

Data-driven decisions in HR use employee feedback, HRIS metrics, and performance data to predict outcomes before implementing changes. Data-driven decisions allow HR leaders to predict outcomes. For example, analyzing survey data shows that improving manager training scores by 10 points typically reduces voluntary turnover by 3-5%. Leaders can forecast results and avoid costly mistakes.

What are the 6 steps in data-driven decision making?

Most HR teams struggle to move from data collection to action. The following six steps create a repeatable process. In addition to accurately defining the challenge, it’s necessary to identify the data that can offer insight into the problem and how it relates to other data, determine how or where to get the needed data, and determine how that data is used to make decisions.

Here are the key steps and considerations:

1. Clearly define the business problem you want to solve.

   • Identify what is happening — e.g., 90-day turnover spikes.

   • Pinpoint when it occurs by reviewing exit dates.

   • Uncover why by matching exit data with onboarding survey feedback.

   • Address root causes to lower turnover in that critical window.

2. Identify which data sources answer your question. For retention issues, combine HRIS turnover data with lifecycle survey responses and manager effectiveness scores. Target your listening strategy to the population experiencing the problem. If turnover concentrates in frontline roles, pulse surveys of those employees combined with manager interviews provide faster insights than company-wide engagement surveys. What other data does your organization collect that you will want to utilize?

3. Design the study or survey. Poor survey design produces unreliable data. Use validated questions and consistent response scales to ensure results drive action. Test survey questions with a pilot group before full deployment. Inconsistent wording across survey waves makes trend analysis impossible.

4. Determine the partnerships or collaborations needed to complete the study. Most HR teams lack dedicated people analytics resources. Determine whether you need external expertise for survey design, statistical analysis, or action planning support. Look for partners with expertise in predictive analytics, benchmark databases, and action planning frameworks—not just survey administration.

5. Make decisions in leadership team meetings. Using the data collected and analyzed in your survey or study, determine the actions to take to address your business problem.

6. Assess the effectiveness of your decisions/solutions. Follow-up surveys measure whether your interventions worked. If manager training aimed to improve engagement, pulse surveys 60 days post-training show the actual impact. Measuring results completes the cycle. Organizations that track post-intervention metrics identify new problems faster and build continuous improvement into their culture.

Organizations with mature listening programs solve workforce problems 40% faster than those conducting annual surveys only.

Regular pulse surveys identify emerging issues before they affect retention. Early detection allows HR to intervene when problems are still manageable.

Most teams focus on data collection and analysis. But problem definition and post-action measurement matter more. Without clear problem statements, even perfect data leads nowhere. Data should answer specific business questions. If you can't connect a metric to turnover, engagement, or productivity, question whether you need it. HR teams tracking 50+ metrics often lose focus. Start with three key indicators tied directly to your business problem, then expand only if needed.

What People Data Do You Need?

HR teams now access data from HRIS systems, engagement surveys, performance reviews, learning platforms, and exit interviews. Most struggle to connect these sources. HRIS systems track promotions, turnover, transfers, and development activities. HRIS data is easy to access but hard to connect to business outcomes. The real question: which workforce metrics actually predict revenue growth or customer satisfaction?

• Pulse surveys – quick, frequent check-ins that track sentiment changes over time

• Collaboration-tool metadata – connection patterns that show how teams work together

• External labor trends – benchmarks on pay, skills demand, and turnover in the market

To drive profitability through people, consider these questions:

• How can we empower employees closest to the work to make efficiency-based decisions?

• What is the optimal maximum size for team leadership training?

• How does daily employee work correlate with expected customer outcomes?

Lifecycle surveys capture employee perceptions that HRIS systems miss—like manager quality, role clarity, and cultural fit.

Organizations using onboarding, pulse, and exit surveys together identify retention risks 60 days earlier than those relying on annual engagement surveys alone.

If 30-day survey data shows new hires lack manager connection, implement weekly one-on-ones and assign peer mentors. Track 60-day pulse scores to measure impact.

Survey data reveals patterns HRIS systems can't show. For example, employees who rate manager quality below 3.5 are 4x more likely to leave within six months.

Do you have the expertise to find the right data?

Most HR teams collect more data than they analyze. The challenge: connecting workforce metrics to business outcomes like retention, productivity, and revenue.

Partners with people analytics expertise identify which metrics predict turnover, engagement, and performance—saving HR teams months of trial and error.

Without dedicated analytics resources, most HR teams can't run regression analyses, build predictive models, or conduct cross-survey comparisons.

Without a dedicated internal analytics team, organizations often face three primary hurdles:

• Relevance: Determining if the data will actually impact specific business goals.

• Synthesis: Missing the "big picture" by failing to look at disparate data points together.

• Integration: Having the data but lacking the tools to compare and analyze it effectively.

Analytics experts identify which metrics predict your target outcomes. They separate signal from noise and build models that forecast retention, engagement, and performance.

The right partner integrates HRIS data, survey responses, and performance metrics to identify the three to five factors that most strongly predict your target outcome.

Frequently Asked Questions

What is a data-driven decision?

A data-driven decision relies on facts, not guesses. Leaders study trends and correlations to predict what will happen if they act, choose the best option, and then check results against the data. This cycle lowers risk and shows quickly whether the action worked.

What data should you collect for people analytics?

Collect data that links employee experience to business results:

• HRIS records – hiring, promotions, performance, turnover

• Employee listening – onboarding, engagement, pulse, and exit surveys

• Operational metrics – productivity, customer satisfaction, safety, quality

Combine these sources to spot patterns, test solutions, and track change over time.

Why does analytics expertise matter in data-driven decision making?

Finding the right signals in a flood of information takes analytic skill. If you don’t have an internal data team, work with specialists who can merge data sets, run predictive models, and surface the few metrics that explain most of the problem. Acting without this expertise risks chasing noise instead of real issues.

Can you give an example of a data-driven decision in action?

A call center tracked ticket volume by hour and matched it with staffing records. Data showed the longest wait times on Monday mornings. Managers shifted schedules to add agents at that time and cut average wait time by 35 percent in one month—an improvement driven entirely by data analysis.

How do you make a data-driven decision step by step?

Follow six clear steps:

1. Define the business problem.

2. List the data you need.

3. Design the survey or study to gather that data.

4. Secure the right partners and tools for analysis.

5. Review findings with the leadership team and choose actions.

6. Measure results and adjust.

What’s another term for data-driven decision making?

Many organizations also use the phrase “data-informed decision making,” which stresses using data alongside business judgment instead of relying on numbers alone.

Need help finding the right data to solve business problems?

Perceptyx helps companies identify barriers to improvement through custom surveys, advanced analytics, and expert guidance. With custom surveys, an advanced people analytics platform, and expertise in all aspects of survey design, strategy, and communication, we can help you identify and collect the right data to solve your business problems—and help you support a cycle of continuous improvement in your company. Get in touch and let us show you how.