Effective dashboards provide the key indicators and critical metrics clients need to monitor so they can quickly see when things are out of line. But no matter how hard you plan or how closely you follow the business requirements, clients who are trying to interpret the dashboard are inevitably going to have questions.
Build enough dashboards and you’ll soon see that the same kinds of questions keep coming up. Armed with that knowledge, analysts can save a lot of time and trouble by anticipating and answering those questions in the dashboard itself. Here are five questions clients commonly ask, along with tips on using the dashboard to address them proactively.
1. When was the data last updated?
A refresh date may not be a metric the client thinks to include in the project specs, but it will be one of the first questions they ask when they look at a dashboard. Is it updating daily, weekly, monthly, or at some other cadence? This context is important for them to understand the numbers they are seeing. If a client is monitoring KPIs and they make assumptions based on when they think the data was refreshed, instead of when it actually was, they might draw incorrect conclusions and make detrimental decisions.
Analysts can address this question in one of two ways:
- Add in an extract refresh date to your header. This is the quickest way to add something simple to your dashboard, and Tableau has written a knowledge base article to get you started. But this approach has some caveats. The date is dependent on what kind of connection you have and is based on when the data or extract was last updated in Tableau. If your data source doesn’t update correctly, the date will still show when Tableau refreshes that connection, leading to a data refresh date that isn’t accurate.
- Add a data refresh date in your data prep workflow and add that field to your dashboard. The field would be today’s date, and then once that workflow runs, the updated extract will have today’s date as a column. This field will help catch if there are errors in the workflow since it comes from the data workflow itself. However, it won’t work if you have a live connection to a data warehouse that doesn’t include an update date.
As an analyst, you put a lot of time and thought into building dashboards that will help your clients meet their goals. We must also be sure to give them all the context they need to correctly understand and act on the data they are seeing, including an accurate representation of when the data was last updated.
2. When was the report last changed?
With analytics teams working together on reports and web authoring becoming more prevalent, understanding when and how reports change is critical. But it can be hard to track in Tableau. Here are some options to consider besides manually adding a “Report Updates” note to your dashboard:
- Tableau Server has some built-in version control capabilities. They are helpful when changes are made during web authoring or if all reports are consistently published to the server. However, this feature lets you see only the previous version, and it doesn’t allow you to see the actual changes made from version to version.
- You can track changes in the Tableau XML from version to version, but this requires the technical knowledge and ability to correctly save and track changes using version control software. You can learn more in this article from Mighty Canary.
Collaborating on reports is a critical part of working on an analytics team, but it can cause issues when it comes to report updates and understanding what was updated between analysts. Clients need to understand report changes as well to know where to find the information they need. Keeping documentation is critical to ensuring that all analysts can understand report changes and communicate those changes to the clients.
3. How was this field calculated?
As analysts, we know that a simple question such as “What is our student enrollment?” can be anything but simple. Enrollment can mean full-time equivalent of students, full-time only students, paying-only students, degree-seeking only, and more. Because one word can mean different things to different people in the same company, clients may need to know how certain numbers are calculated to ensure they are interpreting the report correctly. As analysts, we need to be transparent with how we calculate different fields.
Here are three ways to answer that question:
- Create a glossary dashboard and ensure that links to the glossary page appear on each subsequent dashboard. This approach ensures that the information is readily available to the clients. However, it’s easy for clients to overlook this page and end up reaching out to the analyst directly with questions. You may have to spend some time training clients to look at the glossary for faster answers.
- Create an internal data dictionary as a data source and integrate it into your reports. You can implement this dictionary in a variety of ways, the easiest being using tooltips. If you have a data source that has the calculation names and the definitions, you can do a join between the original data source and the data dictionary so that the definitions are available. Then these definitions can be brought into tooltips. However, this approach can require a heavy lift on the back end to create the data dictionary and to keep it updated so that it works across many reports.
- Another option is to manually add the field definitions into the tooltips. This approach requires less effort on the back end to create a data dictionary, but, as definitions change, analysts will need to update each report manually instead of being able to update one central data dictionary.
It’s critical that clients understand the data they are consuming so they can accurately interpret the data and make sound decisions. As analysts we need to not only include data definitions in our reports, but write them in a way that makes sense to the client, such as by avoiding jargon and not including the syntax that created the calculation.
4. What data source(s) are included in this report?
Understanding where the data in different reports comes from will help clients understand the context around the data. For example, if one data source is a snapshot that doesn’t change and the other data source is a live connection, the two sources may not match if transactions are backdated. Document where the data comes from on each dashboard (or even each sheet if necessary). If there are nuances about the data source that clients need to know, be sure to add those definitions as well, either to a glossary page or to the dashboard itself.
5. What filters are on in the background?
While clients can generally see the filters they have selected, they cannot see what data source filters you have added or any data you have hidden. Not only do clients need to understand what data sources are included in the report, but they also need to know if there are certain aspects of the data that are excluded. For example, if your company has paid employees and volunteers, you may decide to filter out all volunteer data at the source. If so, it would be important for clients to know that the data they are seeing is only for paid employees. Documentation is key to addressing this question as well. In the same note about what data sources are included, be sure to also include any key filters or hidden data on the dashboard.
Taking the time to think about questions the client may have around the report and addressing those questions proactively will help elevate your dashboards, making them a critical part of the company toolkit. By providing clarity around key definitions and sourcing, your clients will be comfortable using the data you provide to make informed decisions based on accurate information.
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