The Insights Formula
Insight Formular
Intro
When looking at data, numbers alone rarely tell the full story. Patterns can hide in endless rows of tables, and trends may remain invisible in raw figures.
Dashboards offers a way to uncover what would otherwise remain unnoticed – but only if it is done with care.
The choice of design, the quality of the data, and the way information is presented all decide whether a dashboard truly informs or simply decorates. A good dashboard doesn’t just show data – it reveals meaning.
The Insights Formula
We know, data visualization is about translating numbers into visual representations so that they can be easily compared, and patterns, trends or other anomalies can be quickly identified. However, this is only one part of the process. To truly arrive at high-quality insights in the end, the following additional components are essential.
1. Question Framing
Every meaningful visualization starts with clarity of purpose. This includes a precise definition of the target audience and a clear understanding of which questions the future dashboard is meant to answer. To achieve this, user research methods such as end-user interviews, focus groups and expert reviews are applied to gain a well-defined picture. Ask your UX Designer or User Researcher.
2. Reliable Data
In contrast to many polished examples of data visualization, where “optimized” datasets fit perfectly to the message, reality is different. Preparing data so that it is clean, consistent, and ready for analysis is often the most time-consuming part of the process. There are many reasons for this, which we will not go into here, as this type of preparation is typically carried out by personas like Data Modeler or SAP Developers.
For us, however, the rule is simple:
“Garbage in, garbage out” – bad data leads to bad results.
This is why data quality must be a central focus when designing dashboards.
Key questions to ask include:
- Are the required data available?
- Are they relevant and complete?
- Are they up to date?
- Are they representative?
- Are the formats consistent?
To ensure reliability, validating the plausibility of data values should be an integral part of the dashboard development process.
3. Clear Visualizations
The following principles provide orientation for designing clear visualizations.
- Show the necessary information comprehensively.
- Provide clear support for the information hierarchy.
- Clarity over beauty
- Everything on the screen has to make sense.
- Everything that belongs together is grouped visually.
- Support data comparison capabilities (i.a. variances)
- Choose the right chart type
- Use consistent terms.
- Group with empty space.
- Place time-series on the x-axis (IBCS)
- Visually highlight the most important information.
- Provide clear info about where the action is needed.
- Avoid abbreviations (unless these are colloquial).
- Do not use semantic colors in the design, e.g., green, yellow, and red (unless it's company branding).
- Lead with purpose (guide the user to the appropriate actions).
4. In-depth Analysis
Once the key questions have been defined, the right data connected, and the appropriate visualizations chosen, what remains is the interpretation – assessing the relevance and reliability of the information being presented.
As an example, a year-over-year deviation that appears dramatic at first glance does not necessarily mean that there lies the actual problem. The real issue may lie in a much smaller deviation elsewhere – one that is not visually noticeable at first sight.
To correctly interpret these visualizations, in addition to verifying the information using statistical methods by business analysts and domain experts, the support of artificial intelligence will play a central role in the interpretation of the information.