Learn how to optimize your analytics reports with these 10 best practices, including data integrity, visualization, storytelling, and more.
While we work hard to produce important and useful data analysis reports, we know that not all of the data we present is used to its full extent. In August 2023, IDC reported that unstructured data is underutilized, undervalued, and underfunded. Their survey of 400 IT leaders revealed that most companies’ unstructured data remains siled, and only a fraction of it is analyzed and acted upon, resulting in missed opportunities for valuable insights and actionable recommendations.
So now the question is: how much of the data we analyze and report on is actively used?
The issue of using reports has plagued IT from the beginning. Most IT staff know that the 80:20 rule applies: 20% of the reports produced for the business represent 80% of the information. Meanwhile, unused and rarely used reports accumulate on the servers.
To avoid wasted effort in developing analytics dashboards and reports, here are eight best practices to follow:
- Stay on top of the business
- View dashboards and allow easy drill-down
- Ask questions about next generation reporting
- Enable multi-level usage authorization and universal access
- Verify data integrity
- Synchronize data with similar data in the company
- Standardize development and reporting formats.
- Measure for use and perform autopsies.
- Incorporate data storytelling techniques.
- Select the right data analysis tool
1. Keep an eye on the business
How many times does IT meet with users to discuss the design of a report and then go on to develop something else? More often than you think.
What happens is that IT, while working on the report in the office, thinks of new ways to slice the data and decides to embellish the original request with additional functions and features.
This is a great practice, and can be “great” for users, as long as the embellishments do not cause so much deviation in the report that the original business request is overlooked.
2. View dashboards and allow easy drill-down
Interactive features such as filters, drill-through capabilities, and tooltips can improve the usability of the report, allowing users to explore the data from different perspectives and get the information they need.
Finance departments are comfortable working with spreadsheets and figures, while sales may prefer a pie chart, manufacturing may prefer bar charts, and logistics may prefer a world map (Figure A).
Finding the optimal summary-level data display for each user is a big win in itself. It immediately creates a level of comfort for the user.
Another usability factor is the ease of drilling down into more detailed analytical data. For example, if a user is working with a map summary visualization and wants to know more about his truck fleet in Atlanta, he should be able to click on Atlanta to access the details.
3. Ask questions about next-generation reporting
Today, a user might request a report that tells them how much product flows through each of their production lines per hour, day, and month. Next year, they may want to know how much product was returned for defects and which production lines produced it.
From a data point of view and from a report data field definition point of view, it is always a good idea to ask the user what they would like to see in a given report in the future, so that companies can scale easily to that and keep the report relevant.
4. Enable multi-level usage authorization and universal access
At any time, a new user in a new business area can request access to a report. At any given time, the user controlling a given report will also want to grant security clearances at different levels to people, such as a vice president of manufacturing can see all manufacturing activity, but the manager of Plant B can only see all manufacturing activity. be able to see information about Plant B.
SEE: Creating access paths to analytical data for better results.
Analytical report designs should clearly designate security access levels and who should control and authorize them (Figure B). These reports must also have the technical flexibility so that anyone in the company who is authorized to use them can access them.
5. Verify data integrity
Before authorizing and moving any analytics report or dashboard into production, the data you use and the reports must be cleaned and verified for accuracy. This involves performing data preparation and validation processes, such as data deduplication, outlier detection, and checking for missing or inconsistent values.
Rigorously verifying data integrity before deploying analytical reports into production reduces the risk of making decisions based on inaccurate or incomplete information. This, in turn, improves the credibility of the reports.
6. Synchronize data with similar data in the company
If sales reports use the “customer” data field, which refers to individual buyers, and manufacturing systems use the term “customer,” which refers to individual buyers but also a rework shop within the company, this data must be synchronized so that there is no common definition that allows sales and manufacturing to talk about the same thing.
Data synchronization is performed in the IT database area (Figure C). It is important because information discrepancies and internal disagreements can arise when two different departments think they are talking about the same thing but they are not.
7. Standardize development and reporting formats.
Standardizing the reporting tools used, as well as the formats of the various reports used, ensures consistency across the enterprise and reduces confusion for users. This includes standardized templates, data definitions, naming conventions, report layout, and design principles.
8. Measure for use and perform autopsies.
Annually, IT should review analytics reports to determine how much usage they are getting. If a report has not been used or was used infrequently, IT should check with end users to see if the report is still relevant.
It is equally valuable to perform a postmortem evaluation. What content features, features and functions of the reports were used the most? What was not used in the reports? What can be learned from the evaluation to improve the quality of analytical reports? These are all important questions to ask to ensure that your reports meet the needs of your end users.
9. Incorporate data storytelling techniques.
Data storytelling is the art of building a compelling narrative based on complex data and analysis to communicate insights effectively and make them more memorable (Figure D).
Data analysts or business leaders looking to inspire action by their teams should include the four elements of data storytelling: character, setting, conflict, and resolution. By adding these components, organizations can increase the impact of analytical reports and make them more engaging and persuasive.
10. Select the right data analysis tool
Choosing the right data analysis tool is important to the effectiveness of your analysis efforts. The best tools are usually easy to use and scalable. They also include data integration capabilities and visualization options, allowing businesses to connect to multiple data sources and easily create interactive reports.
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