Are your reports delivering the right information for your business? 

While we work hard to produce important and useful data analytics reports, we know that not all of the data we present is being used to its fullest extent. The question to ask yourself is: How much of the data that you analyze and report on is being actively used?

To prevent wasted efforts in dashboard and analytics report development, here are best practices to follow:

  1. Stay tuned with the business: How many times does IT meet with users about a report design, and then go away to develop something else? More often than you think. What happens is that IT, as it works on the report back in the office, thinks of new ways to slice and dice the data and decides to embellish the original request with additional functions and features. This is a great practice – and can pay off “big” for users – as long as the embellishments don’t create so much report drift that the original business request is missed. Know what you’re asking for when you’re asking for data; otherwise, you’ll get overwhelmed by the numbers that aren’t showing you relevant dollar impact or ROI. 
  2. Visualize dashboards and enable easy drill-down: Financial departments are comfortable working with spreadsheets and figures, while sales might prefer a pie chart, manufacturing might prefer bar charts. Finding the optimal visualization of summary-level data for each user is a major victory in itself. It immediately creates a level of comfort for the user. Personalization is key! 
  3. Ask next-generation report questions: Today, your user might be asking for a report that tells him how much product flows through each of his production lines hourly, daily, and monthly. Next year, he might want to know how much product was returned for defects and which production lines produced it. From a data standpoint, it’s always a good idea to ask the user what he might want to see from a given report in the future so you can easily scale to that and keep the report relevant.
  4. Enable multi-level usage clearance and universal access: Analytics report designs should clearly designate security access levels, and who should control and authorize them. These reports should also have the technical flexibility to be accessed by anyone in the enterprise who is cleared for use.
  5. Verify data integrity: Before any analytics report or dashboard is cleared for use and moved to production, the data that it uses and reports should be cleaned and verified for accuracy. If your numbers are inaccurate and there are errors in data collection, the reports won’t mean anything. 
  6. Synchronize data: Data synchronization is done in the database area of IT. It’s important because information discrepancies and internal disagreements can arise when two different departments think they are talking about the same thing but they aren’t. You want to make sure that you have a single source of truth when it comes to all your data. 
  7. Standardize report development and formats: Standardize the report-producing tools that you use and also the formats that various reports use. This helps ensure uniformity across the enterprise and lessens confusion for users.
  8. Measure for use and perform post-mortems: Annually, IT should review analytics reports for the amount of use they’re getting. If a report hasn’t been used, IT should check with end users to see if the report is still relevant. It’s equally valuable to conduct a post-mortem evaluation. Which content, feature, and function characteristics of the reports were most widely used? What did you learn about the reports that weren’t used? What can you take away from the evaluation to improve the quality of analytics reports? These are all important questions to ask to ensure the reports meet the end users’ needs.

If you need further assistance with cleaning up your data or optimizing your reports, reach out to [email protected] or visit our website: to learn more about what we can do for you!