Using People Analytics For Data-Driven Decision Making | Perceptyx

Using People Analytics For Data-Driven Decision Making

By Emily Killham - July 14, 2021

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. While gut feelings or inspiration may have been the initial catalyst for many successful companies, it's nearly impossible to sustain business success over the long term relying solely on intuition.

These days, having access to good data is not an issue for most organizations. To the contrary, as systems have become more sophisticated, in some ways the data-driven approach to decision making has become more difficult, as there is more information to sift through. While this wealth of data can make data-driven decision-making easier (because finding correlations between data points can offer additional insights), much of the larger volume of data constitutes background noise which can obscure what matters most. The challenge for many organizations today is how to synthesize what’s important while screening out the noise.

In this article, we’ll examine the advantages of data-based decision-making in business, how data is used to make decisions, and the most important factors to consider in making data-driven decisions. Our specific focus will be on using people analytics with data gathered through employee listening combined with other important people data organizations collect and use for making data-driven decisions.

What does it mean to make data-driven decisions?

A data-driven decision is simply one that is based on data rather than a hunch. Data-driven decisions have the advantage of being “pre-tested”; to a certain degree, analyzing data and correlations can show leaders how pulling lever A will likely impact metric B. This gives leaders some predictive power regarding the impact of the decisions they make, and just as importantly, reduces the chances of unanticipated negative impacts.

In the simplest terms, making data-driven decisions gives leaders a map to follow— versus the uncharted territory of decisions made without the benefit of data.

Having the right data is crucial for data-based decision-making. Download our free People Analytics Playbook to learn what you should be asking your employees—and how.

The Data-Driven Decision-Making Process: 6 Steps

Though the data-driven approach to decision-making seems straightforward, using it to successfully address business challenges requires a thoughtful 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. Sometimes an organization may know it has a problem, but hasn’t clearly defined what it is or what is causing it. For example, consistent turnover at the 90-day mark is clearly a problem, but to determine the cause we need data. By looking at exit data, we can define when the problem is occurring, and by correlating that data to employee survey responses, we can pinpoint why it is happening. By addressing the root causes of the problem, we can reduce the turnover rate in this critical period.
  2. Determine the type of data needed to solve the problem and the type of survey or study needed to obtain that data. Do you need to survey all employees, or only those in specific jobs? Will it be easier to get to data you need from a survey or a focus group? What other data does your organization collect that you will want to utilize?
  3. Design the study or survey. Determining what data to collect and ingest into the study—and how to report it—will ultimately shape the quality of data leaders have for decision-making. Ensure that the data is consistent and reliable—the quality of the output will only be as good as the quality of the input.
  4. Determine the partnerships or collaborations needed to complete the study. Does the organization have the internal manpower and expertise to design the study and analyze the results? If not, decide on the type of help the organization will need, who the best partner will be, and each party’s role in the collaboration.
  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. One of the main advantages of data-driven decision-making is that the impact of decisions can be evaluated through analysis of data collected subsequently. In many ways, the assessment of the effectiveness of actions is the most important part of the process, as it informs the next business problem an organization is ready to solve.

The power of data-based decision-making in business is that it creates a cycle of problem-solving and continuous improvement. Data-driven decisions keep the organization constantly moving forward and tackling new problems as they emerge.

While often much of the effort and focus in the data-driven decision-making process is directed to the steps in the middle, the most important parts of the process are clearly identifying the problem that you want to solve, the actions you take to solve that problem, and assessing the effectiveness of the actions you take. Decisions and actions are driven by data, but it’s important not to lose sight of the business problem you’re trying to solve. This has become a problem for some organizations as more data has become available; it’s important not to get so overwhelmed with data that you forget about the problem you started out trying to solve.

What people data do you need?

With all the potential sources of data available to organizations today, zeroing in on the right data for your purposes is a challenge. There’s a wealth of data in HRIS systems regarding promotions, turnover, transfers, and development. While this data is easy to access, as organizations become more sophisticated in their use of data, they need to think about how data is used to make decisions that drive profitability through their people.

How, for example, can we best empower the people closest to the work—who are in the best position to know—to make decisions on efficiency? What is the optimal maximum number for team leadership training? How does the work employees are doing lead to the outcomes you expect for customers?

To get this type of data, surveys are critical. Asking for employee feedback throughout the lifecycle and cross-referencing and connecting that data can answer these questions and others.

If, for example, you learn that new employees who left the organization on or before the 90-day mark did not feel connected to an internal network during onboarding, you can evaluate your current processes and find ways to solidify the manager relationship and connection to the organization. While survey data adds considerably to the wealth of data you already have (all of which may already feel overwhelming), taking all of those pieces of data and looking for patterns allows you to home in on changes that will have the biggest impact.

Do you have the expertise to identify the data you need?

Having piles of data is not the problem; ultimately the issue is in identifying the right data to use for making the decisions that will solve business problems.

That’s where having a partner with analytics expertise becomes very important. Without your own internal data analytics team, your options are limited in terms of what you can do with your data.

Data can suggest a lot of things—but will it have an impact on what you need to accomplish? Is it important? If assembling and analyzing data isn’t something you do all the time, it can be easy to miss what’s most important. You might not see what really matters most if you aren’t synthesizing data and looking at it all together. Or, you may have the ability to gather all the data but don’t have a way to put it all together and compare it.

To determine what data is most important, how it fits together, and which patterns constitute insights that are useful for addressing your specific business problems, you need an expert. A good partner can take data from a variety of sources, cull it, and zero in on the pieces that are really important for the decisions you need to make.

This is what Perceptyx brings to the table. We can help you collect more data in a targeted, sensible way that helps to verify that your approach to solving problems is working. We have the expertise to do cross-survey and predictive analytics, to tease out the important data points that might otherwise be obscured. While there are many survey providers who can help you collect a lot of data, we partner with you and empower you to truly solve your business problems. We offer a flexible, customized approach that allows us to provide solutions to the problems you need to solve—not just more data.

We can be your people analytics team or enable your team to be more effective.

Choosing a partner like Perceptyx with expertise in data analysis can help your organization not only pinpoint the right data for solving your business problems, but also guide you through the process of planning and taking action—to help start a cycle of continuous improvement that will pay dividends far into the future.

Need help identifying the right data for solving your business problems?

At Perceptyx, helping companies identify barriers to improvement is our goal. 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.

Download Now: The People Analytics Playbook

Comments

We promise that we won't SPAM you.