Dashboards, Infographics, and Visualizations - Best Practices

• Tweet
• Buffer

Dashboards, infographics, and visualizations have been on my mind a lot this year. I've always loved turning data into pretty colors and patterns, which is great as the number of dashboard projects for consultants like me has gone through the roof in the past couple of years.

The projects I've worked on, even just in 2012, have been at times affirming as an analyst, have driven some key business relationships, but sometimes have been very frustrating. I've been made to feel like Leonardo da Vinci when in one project I simply merged two data sets with a customer ID, drew a two axis line chart, and saw the client nearly fall off their chair in amazement. But I've also been involved in projects that made me feel like Leonardo the Turtle, wading through a sewer of data and misunderstood objectives.

It's made me think a lot this year about the responsibilities and checklists from both a client and agency perspective. To get the best out of your dashboard project, whether it has two data sources or 202, here are a few key learnings and things to think about for both sides.

For agencies or those internals teams tasked with building the tool:

1. Be a Doubting Thomas and don't scope out your project until you physically see the data. Clients love to say to you, "Here's my data, I just want you to pop that in a beautifully visualized set of charts." But under the bonnet of any tool, web analytics, sales, or customer relationship management (CRM) could be a whole mess of data that will take weeks to get to a point where you can easily visualize. This is particularly true of multi-data source dashboards, say ad serving and web analytics data. If you don't have a long-term relationship with your client and they want you to scope without seeing the data then walk away now.
2. Deliver a data audit. This should include profiling the data you receive so that you can understand underlying data distributions and also a quality assurance review of calculated metrics. Too often clients believe their data house is in order and when resulting visualizations don't look the way they want, the person building the dashboard gets blamed.
3. Visualization is less important than data structure. Did I really just say that? Stop scratching your head deciding if you agree with Stephen Few about pie charts and start thinking about the database or data tables you are storing your data in. Does it need to be based on a data cube? Are you likely to have to build a gazillion summary tables? Will your dashboard have to read new data sources in the future and is your data layer flexible enough to do that quickly?

Learnings for clients or the teams, like marketing, who are going to be using the tool:

1. Beware the funky white elephant. "Sex sells" and you want a bit of wow factor to impress your boss. But your company is already littered with reporting packages. Ensure that the visualization tool that you use can be shared across a number of users and that internal security settings won't mess up everyone being able to view the pretty Flash charts. To be frank, if you are impressed by a moving chart, then you shouldn't be the person deciding what software to use.
2. The value of the data layer is probably 10 times the value of the dashboard. If you are merging multiple data sets and building a multi-channel dashboard, you are very likely to be pooling data that has previously been sat in disparate data silos. If this is the case, the data layer you get built for you, say a SQL database or even a set of Excel workbooks, is going to contain data over and above that which is summarized in your visual layer. You must get ready to mine this data like you would have mined each data source separately. Do you have the people in place to do that?
3. Cost of ownership is likely to be way more than cost of development. If you haven't thought yet about who is going to own, run, and analyze your dashboard then stop now.

I haven't even begun to talk about which is the right visualization tool or what metrics are the right ones to include. From a structural perspective, data is a messy plate of spaghetti. But if you are prepared to audit properly, spend time developing the right metrics, and ensure there is an after-life for your dashboards then the visual impact will be dwarfed by the business impact from all your hard work.