Five-step process to creating effective dashboards that deliver the right data to the right people at the right time. Part two in a two-part series.
Having all the data is great, but it's of no value if nobody does anything with it. As Hal Varian, Google's chief economist said in the context of the rising importance of analysts and statisticians in business, "The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it's going to be a hugely important skill in the next decades…" In my view, the visualization and communication of data is as important as the processing and extracting of value from it.
Dashboards are all the rage these days, and a well-designed dashboard is a great way of focusing people on the data and what needs to be done about it. However, creating a good dashboard is not as straightforward as shoving a load of tables and charts into an Excel spreadsheet or Word document and sending it out via email. There's a lot more to it than that. Here's the approach I use to create dashboards.
Business Requirements Gathering
In order to develop a good dashboard solution, you need to have a good understanding of what the business needs to know. What's the business trying to achieve and what does "good" look like? That way you can work out what the right key performance indicators (KPIs) are that need to be tracked and all the supporting diagnostic metrics that are needed as well. This means getting people into workshops or carrying out one-on-one interviews to understand the real issues that the business is addressing and the data that's needed to support them.
Data and Systems Audit
Next, you need to understand what data is available and where it's stored. One of the challenges is that often data is stored in silos and in various different types of systems. The data audit may also reveal that data that's needed in the business is missing and that new information sources need to be created or bought in. In the audit, you also need to understand how easy it is to get at the data you need from the various systems. Is there an API available that allows you to pull the data automatically or do you need to set up some kind of scheduled export of the data?
This phase is about creating the right conceptual model for your dashboards. The design here has two elements: designing the overall dashboard information architecture and designing the actual dashboards themselves. Designing the dashboard information architecture is about defining which person/department gets which data, at what level of granularity and at what frequency. This is the result from the business requirements gathering that was done upfront in the process. The second stage is the design of the dashboards themselves in terms of how they are going to look. One approach is to create wireframes of the dashboards to show the future end users what they are going to receive and get their input during the process.
Prototype and Test
Once the end users are agreed on the design, the next stage is to build prototypes of the dashboards in the chosen delivery system and test the methods that will be used for pulling the data from the various systems. The prototype should use real data, but at this stage the production of the dashboards will still be relatively manual. No matter how much thinking you do at the design stage, there will always be changes to be made to the dashboards at this part of the process. People can find it difficult to visualize how they want something to look until they actually see an example of it, or once they have an initial version they want to add in some additional data that they didn't feel they needed earlier. Be careful, though, at this stage about adding too many additional data elements; otherwise the dashboard can become cumbersome and difficult to use. Often, less is more!
Once the prototypes have been agreed on and signed off, it's time to automate as much of the process as possible. All too often, too much time is spent in the production of dashboards and not enough time is spent analyzing and understanding them. Depending on the various systems involved, some parts of a dashboard may be easier to automate than others, but the goal should be to have the production as automated as possible.
Good dashboard design is more than having some pretty charts and tables. It's about understanding what's important in the business and creating effective ways of delivering the right data to the right people at the right time. This simple five-step process should help you on your way.
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Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
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