Wednesday, February 26, 2014

Representing data in a dashboard

I have been working on a dashboard lately and I feel that the choice of tools and diagrams that are used in building the dashboard are extremely crucial to effectively deliver the dashboard's message to the end user. Since I'm most familiar with excel, it naturally became my tool of choice for building the dashboard. Other analysts or data scientists might be more familiar with other tools such as Tableau, R, visual.ly etc. but the principles are the same in creating an effective dashboard.

Wikipedia describes a dashboard as "an easy to read, often single page, real-time user interface, showing a graphical presentation of the current status (snapshot) and historical trends of an organization's key performance indicators to enable instantaneous and informed decisions to be made at a glance". 

As per the Wikipedia description above - many businesses try to compact all their data into a single page dashboard, and a clear disadvantage is that the dashboard might become too abstract that it makes it difficult for the end user to interpret the data. However, if a single page dashboard is still required, it might be useful to add in appendices to support and explain complex representations.

I'm currently working on a dashboard to address a need to establish a macro-level view of the health of a particular agency's campaigns. We would frequently choose to ignore the use of tools such as dashboards and continue micro-managing each campaign - which isn't particularly efficient especially when you have a ton of campaigns to manage. For an analyst, we often have to take charge of a particular group of clients - whether they are grouped into a particular geographic region, type of industry or type of channel. By having a macro-level view of all the campaigns you are managing, it will eliminate the resources wasted on micro-managing campaigns and allow for better management of campaigns on a daily basis.

The dashboard that I have created focuses on 3 main variables - performance, client happiness and advertising campaign spend. To visualize all these variables into a single diagram - I used the donut graph functionality available in Excel. And on the left side of the graph, I included a raw performance stats table that was sorted by highest performing to the lowest and conditionally formatted to make the table easier to understand. I also included speed meter gauges to help summarize performance and client happiness findings.

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I felt that the main dashboard wasn't sufficient to explain my message to the intended user, and thus I created 2 other pages in the appendix. The 1st appendix page has sparkline charts to show in detail trend data alongside the data set that was used to populate these charts since the intended users were technical and more data focused.

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The 2nd page in the appendix focuses mainly on underperforming vs performing campaigns and also includes the data set and sparkline charts highlighting the highest and lowest performance within the 3 month period. Another important aspect was the inclusion of a table illustrating the campaign's goals - which are used as a benchmark to determine performance numbers.

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Visually the dashboard looks more or less pleasant, but it can definitely be worked on further to remove clutter and repetition. Also, if the agency finds this prototype to be really useful and valuable for their business we can consider putting together a team of engineers to build a BI tool having similar functions.

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