Henrik Ibsen is believed to have first said, “A thousand words leave not the same deep impression as does a single deed.” And it was this phrase that later changed to “A Picture is worth a thousand words” and later still to “One look is worth a thousand words.” No wonder the picture/hieroglyphs script was developed earlier than alphabetical elements.
In the modern business context, with the executive leadership facing challenges such as time constraints, multiple and competing priorities requiring their attention, and the quantum of data generated increasing at never-before-seen speeds, the above-mentioned adage has a very important lesson for business functions. This is where organizations can leverage appropriate dashboards to enable quick and efficient strategic decision-making for their executive leadership.
The Evolution of Reporting Leading to the Requirement of Powerful Dashboards
If we were to see the evolution of executive reporting, it’s clear that there would be a shift from lengthy reports and memos to powerful dashboards and other data visualization outputs. The advancement made on the technology side and the proliferation of multiple Data Visualization tools like PowerBI, Tableau, TIBCO Spotfire, etc., which are super convenient and easy to use, have made this shift easier. Furthermore, the costs associated with these Data Visualization Tools have also come down substantially, and there are even free, open-source tools available.
And that’s where lies the catch for the Enterprise Organizations. In the race to acquire the latest and most comprehensive tools, sometimes the most important aspect is missed – “What are my current and future Business needs for which I am choosing a particular Data Visualization Tool?”. A very simple example will be if a particular dashboard can be designed through MS Excel, and especially if the team is very comfortable with it and, for the foreseeable future, the team does not need more complex tools, then there is no reason to acquire another data visualization tool. Chances are, even if this is acquired, user adoption will be low, and there will be under-utilization. Both these factors increase the cost of operations and reduce operational efficiency as well. Understandably the example is never that black and white for organizations; however, the important point is to understand the Why.
Common Issues in Designing Dashboards and the User-focused Approach
Another common and recurring issue has been the urge of analytics developers to create fancy but uninformative graphics. Often, the development teams get busy deciding which graphs look prettier and different rather than which is most informative and allow the users to get answers to their questions. It is common to see dashboards crammed with multiple variables and graphics to make them comprehensive but often, it beats the very essence of the dashboard. This is commonly called Information Overload.
Another common mistake is to ignore the brand guidelines and design norms to make the dashboard look prettier. These examples are indicative, and there are various other factors to keep in mind while designing dashboards and optimizing the use of Data Visualization Tools. In a nutshell, the idea is to showcase more with less.
Process of Data Visualization at DefineRight
At DefineRight, we have adopted and detailed an in-house process of Data Visualization wherein we focus on four broad areas with a constant bi-directional feedback loop between them.
- Discovery Phase – This is generally the step where maximum time is spent. This phase is all about getting familiar with the metadata and the purpose of the dashboard. Both Business Analysts and Technology Analysts get together to deep-dive into understanding the metadata, process mapping, and documenting the discovery insights. Data Quality checking and choosing appropriate tools through consultation with business is a major activity in this phase. A few benefits of spending extra time in this phase are risk mitigation, improved scalability, and long-term time savings when handing over the activity to a knowledge partner. Some important aspects to keep in mind are who is going to consume the analytics, what they want to see, how they will integrate the insight into their business process, and what decision they will take.
- Wide Canvas Phase – While it can be argued that this phase is part of discovery, we believe that this should be taken as a separate phase. This phase is all about knowing the nuances and inter-dependability of the business operations and, accordingly, the application of the same towards achieving strategic objectives through tactical execution. This phase focuses on figuring out how any piece of data ties up with other macro and micro-level details in a strategic manner. This involves aspects of the data harmonization process as data is often combined from multiple sources, and hence it’s important for the end users to see the combined data in an easy-to-use format. This also is a checking mechanism for any redundancy and duplicity issues. Taking a specific example, often times users of dashboards need to move between dashboards or from a dashboard to a source system for additional analysis or take action. That is why it is important to understand the data insight value chain wrt that particular business process so that the dashboard becomes the one place from data to insights to analysis to actions.
- Design Phase – Once the activities of the two preceding phases have been completed, and this phase becomes comparatively simpler to execute. The underlying principle of this phase is to keep it simple. Even though this is a less complicated phase, care needs to be provided to use best Design Practices and User Requirements. Both aesthetics and functionality are equally important, and keeping the design consistent makes it easier to adopt. Another important component of this is testing. Through Development team-led testing and another check through business teams, quality and scalability issues can be tackled before circulation to a wider audience.
- Adoption Phase – In this phase, Change Management comes into focus. Even the most well-designed dashboards are of no use if there is low or little user adoption. Comprehensive FAQs, Pre- and Post-Circulation Query Response Management, and consistent hand-holding go a long way in increasing user adoption. It is equally important that dashboards are accessible and available in time to all relevant business users. But more importantly, if Analytics Metrics can be defined during the discovery phase and monitored post-circulation, corrective action can be taken before problems become acute. At the same time, the voice of end users needs to be monitored, and necessary feedback is given to the team to make improvisations, both in design and functionality.