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Can You Ensure Data Quality In Your Reporting Process?
Many organizations claim to be data-driven, but it’s a lot easier said than done. Use these five steps to consistently get clean, accurate data for your reports.
It only takes looking at your data in one software system to realize that you have data quality issues. Maybe you are using Sales in one report and Net Sales in a different report. Maybe you are tracking full-time employees in one place and full-time equivalents (counting part-time and contractors) in another. Once you try to have a review meeting using different data, questions begin to swirl about the accuracy of data. Whether you’re just tracking KPIs for the first time or finding quality issues in your current reports, you should know that it takes a concerted effort to define data quality for your organization, and then live by that definition.
Whether you’re just tracking KPIs for the first time or finding quality issues in your current reports, you should know that it takes a concerted effort to define data quality for your organization, and then live by that definition. Click To Tweet
Not to worry—we’ve learned some best practices along the way to guide you. Use these five steps to become a data-driven organization with accurate reporting information:
1. Get leadership buy-in.
Data quality standards have to come from the top—that’s the only way to build a data-driven culture in your organization. If employees are asked to submit data that’s never reviewed or used, quality standards drop and errors are inevitable. But when department and executive leadership teams regularly review, ask questions about, and make decisions based on reporting information, you’ll get precise, actionable data. For example, holding monthly leadership meetings where different departments are invited to present their data will prove the executive team is interested and invested in data quality. Expect a lot of questions early, and then standards for data will be derived from these conversations.
2. Commit to the evolution.
There’s still work to be done once you’ve got a strategy and leadership buy-in on data quality. Now you have to figure out how to get accurate, timely information. Go into each department and look at its data. What metrics are important to that department? What should you be tracking? Where is the data sourced from?
The data will likely be rough to start, but once you determine what is needed, you can begin to refine and clean it. For example, keep in mind your measure format—such as an actual, year-to-date sum or average trend data—will change as you report on them. As part of your initial strategic reporting process, you may start by labeling measures as “prototype.” This clearly communicates you’re still trying to figure out the measure and data involved. Once the kinks are ironed out, change the label to “manual entry” or the name of the data source, as appropriate.
It’s an evolution to get really high-quality data. It could take months or even years, but it’s worth the wait.
Evaluating your measures and goals might be part of the data-quality evolution. Learn how to avoid errors in this step-by-step guide.
3. Document the process.
If you don’t write it down, you won’t remember it. Ensuring data quality means you need to document how the data is being sourced (e.g. manual entry or API), and how and when it’s being reported. Plus, it helps with communication and staff buy-in. This includes everything from outlining agendas for department reporting meetings to determining which databases will automatically feed data into reports. (It’s important to automate data feeds from sources when possible—it’s less work for staff and contains fewer errors.) With documentation, you may learn that you are sourcing a revenue number from two different systems, and thus you can evolve your measures (see earlier step) to start using the same source for the same data.
4. Don’t be complacent.
At some point, you’ll realize you’re getting good-quality information. Don’t take it for granted and get complacent! Continually vet your data sources and process to avoid potential problems. Was there a database update that might cause incorrect information to appear? Is a new hire unsure which numbers to report on? Are your data quality metrics still relevant? Scheduling regular maintenance on everything will help you catch and fix issues early.
Most organizations are diligent about establishing a good reporting process with clean data for the first year or two. Over time, bad habits creep in. You won’t know you’re pulling incorrect or incomplete data unless you have a ongoing practice of checks and balances. Intervene quickly, even for small issues—it’s easy to lose control of a rock-solid reporting process. Some organizations have a system of approvals: an analyst enters data and then a manager reviews and approves it. This helps with accountability and makes sure someone with fresh eyes double checks the data.
If you are using software like ClearPoint, you can set an alert to trigger when you see data that is well outside the range of what you’re expecting. This can be your first flag that something may be awry.
5. Hire a pro.
Depending on the size of your organization and how many metrics you’re tracking, you might need a full-time data analyst. This person helps with the entire reporting process and attends leadership meetings, but likely won’t present the data (that’s typically handled by the strategy manager or department head). The analyst is responsible for data quality management. If strategic planning and performance management become really important to your organization, this role will get funded, and it will produce a tremendous amount of value.
Of the five steps, leadership buy-in and culture change is the most important thing. (It’s #1 for a reason.) When trying to determine what data quality is and how you can push your organization to be data-driven, these two factors matter more than anything else. You’ll also realize that standardizing your data presentation can be easily accomplished in reporting software, like ClearPoint. Software is one more way to ensure data quality in your reporting process.