How To Build An Effective Business Intelligence Dashboard
With 2020 approaching us, we are witnessing most of the leading organizations today have become or are in the process of becoming what is defined as a “learning organization.” According to Forbes Insights Survey (done in collaboration with Qlik, a leading enterprise analytics solution provider), 71% of business leaders strongly believe that self-service business intelligence can lead to accelerated learning and responsiveness in their organizations.
In learning organizations, decisions can no longer be driven by gut feelings alone. Instead, the decisions at every level must be backed by what the data says. To foster this “data-democracy” within enterprises, there is a need for intuitive & actionable reporting and analytics.
A well-designed dashboard enables everyone across functional areas to be able to derive insights for themselves, even if they are not statisticians or data experts.
A well-designed business intelligence dashboard acts as a snapshot of the management, providing a high-level view of the business, department, or a specific process without entangling the user in a mesh of numbers and figures. By engaging the full power of visual perception, they communicate a dense collection of facts efficiently and with exceptional clarity.
Well-designed business intelligence dashboards lead to fast adoption of analytics, setting up a paradigm of data democracy within the organization.
However, developing the best analytics dashboards is as much art as it is science and the design can easily go awry, failing to deliver what the user would expect. When this happens, it can limit the value of analytics to organizations.
Below we will take a look at the common mistakes that can occur while designing an analytics dashboard.
Common Mistakes When Developing A Business Intelligence Dashboard
With business intelligence dashboards, one can be tempted to present an exhaustive list of information. While it could ensure that the analytics dashboard is not missing out on any useful metric that the user might consider necessary, this will probably lead to cluttered user experience. It can get confusing for the user when the dashboard is consists of too many widgets and densely presented information.
Another common mistake with analytics dashboards is when they include over the top visualization to deliver information in new and advanced ways.
Unless the users are trained, they may regard it as a glitzy interface with little value. In such a case, they will revert to other more reliable, perhaps siloed reporting frameworks.
A common symptom of poorly adopted dashboards is that it is not aligned with the user persona. Such dashboards are developed without thoroughly understanding the user requirements, expectations, and skills. Users are likely to deem such reports irrelevant.
Such mistakes are recipes for failure, and unless they are carefully avoided, analytics dashboards will quickly become another not-so-useful piece of an upgrade.
What Makes A Business Intelligence Dashboard Effective & Useful?
So, how to build that perfect analytics report?
A poorly designed business intelligence dashboard has the potential to undermine the value of your enterprise analytics project.
There are a few business intelligence dashboards best practices that I will cover below. Well-designed analytics dashboards serve the purpose of an analytics framework — they will act as a single source of truth for relevant information across departments and processes.
1) Identify The Dashboard User Persona
The DAR principle helps us to understand that different users consume analytics in different ways. Hence, to build a useful dashboard, you need first to recognize the user expectations from the solution. When you do this well, you will understand what information needs to be presented and how complex the solution can be.
What DAR essentially says is that some users, such as the CXOs, prefer the dashboard to display ready to consume information with KPIs and basic visualizations. Any extraneous information is going to be considered unnecessary.
However, others, such as managers, will require a much more detailed summary that provides data filters, slice & dice, and drill-down capability built into the solution. It is because the managers want to understand their metrics in greater detail.
Power users would like to have an analytics dashboard that allows them to make their data discovery, and hence, such dashboards should be highly customizable.
It would be best if you undertook extensive user research in the beginning- and identify your user goals within the different hierarchies, departments, and Line of Business. Understand their KPIs and what they want to achieve with analytics.
After that, you should conceptualize the most consistent visualization style that will best convey the information. Whatever the goal of analytics is, there are various chart types most suited to display each.
2) An Excellent Dashboard Tells The User A Story
A dashboard is often the face of an analytics application. It is what the user interacts. To convey maximum impact, knit the numbers & graphs into a fascinating story.
When dashboards inform with a story, they not only make the whole information more seamless but also encourage conversations.
For example, the inverted pyramid structure of information provides a useful framework that can be applied here — include the most critical information at the top. The user should be presented with an increasing level of details as they scroll go below.
The RAG framework includes readily comprehensible colour codes to display red flags and successes.
Remember that the dashboard must answer “why” something occurred and not just “what” is happening.
3) The Perfect Dashboard Has A Minimalistic Design
A well-designed dashboard captures the essence of data without any extra charts and widgets. Information that is more than necessary might divert the user attention from the vital piece of information. There should be at max about 4–6 widgets in a single page depending upon the size & purpose.
Keep the widget sizes in the correct proportion & group charts that represent similar trends together. The dashboard should present information neatly, be easy on the eye and ensure that it can be read at a glance.
“The best software for data analysis is the software you forget you’re using. It’s such a natural extension of your thinking process that you can use it without thinking about the mechanics.” — Stephen Few
4) Keep Evaluating The Evolving User Expectations To Improve
Enterprise Analytics can never be finished in one phase. With time, the scope only increases as more processes, and people begin to realize its usefulness. Feedbacks will be regular and essential, and it is highly likely that the prefect report will be after cycles of improvements.
Therefore, you need to remain agile to spot red flags (if any). Even after the implementation, have regular discussions with your users to understand & incorporate their concerns and priorities in a structured manner.
Effective enterprise-wide analytics adoption is critically dependent on the quality of analytics dashboards & reports.
Meaningful user experience with your dashboard can boost analytics adoption, yielding significant ROI. By taking your users on a journey to making proactive decisions with effective dashboards, you will gain their confidence and help them improve.
While the quality of analytics dashboards is highly dependent on its careful implementation, you also need to choose the right technology so that it does not restrict you from a capability perspective.
Do your research thoroughly basis your requirements and make winning bets.
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