How to Get More Out of Your Organization’s Data

Data drives every decision a business makes. The right data provides insight into issues as diverse as retention rates, project status, and employee engagement. Data is king in the modern business world, but only when the correct data collection process steps are employed.

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Poor quality data, incorrect analytical approaches, and even asking the wrong question can throw data results into disarray and skew results. Following these data collection process steps reduces the risk of such problems while yielding the most accurate results.

Step 1: Identify Issues

Even the largest companies struggle to locate, detect, and analyze all available data. The scope and capabilities of your data analytics team are probably limited, which means you have to make the best use of the resources you have. Which issues have priority–equity-inclusion, employee engagement, performance review data, or any of the thousands of other issues that may benefit from data analysis? Decide which issues have priority and set goals.

Step 2: Improve Data Quality

What kind of data do you need? Surveys, annual performance reviews, promotion tracking, exit interviews, and more can all yield insightful information, but only if the data collected is of good quality. For instance, if only half of your managers are submitting performance reviews, the data collected will be incomplete. Exit interviews are notoriously unreliable, as the reasons employees give for leaving a company often don’t match up with the real reasons for turnover rates. Choose what kind of data to collect, then create a systematic collection method that ensures high-quality data.

Step 3: Linking Data

To gain a complete answer to any data-driven question, you need access to all relevant information. This can prove difficult. Different teams collect and work with different data, and different departments may not know which departments hold which data. Again, this can lead to inaccurate analytic results–to fully understand employee engagement issues, for instance, you need to understand both retention and turnover rates, employee surveys, and performance reviews–all of which may be scattered across departments.

One of the most important data collection process steps is the design of processes that make it easy to link diverse points of data. The more data available for each employee, department, or project, the more accurate your analytical results.

Step 4: Analyzing Data

Which data analysis techniques do you plan to use, and which software will you employ? Do you have data analysts available on-site, or will you need to contract with outside experts? Data analytics require complex processes to which your company may not have in-house access, so it’s wise to plan in advance who will analyze data, where, and how. Staying on top of the latest analytic and HR trends helps guide your decisions.

Step 5: Theory and Action

Once the numbers have been crunched, it’s time to develop and test theories. This involves investigating not only what the data means, but what it predicts for the future. From theories, good data collection process steps move into action. Roll out new processes based on your theories, and track outcomes. It can take several attempts to achieve the desired results of any new process, as new data informs and changes the answer to your question–or even what question you need to ask.

DOWNLOAD THE E-BOOK: The Ultimate Guide to Employee Engagement Surveys