Lots of organizations have seen the benefits of citizen data science and self-service BI programs. Instead of waiting for a centralized IT or data science team to produce analysis, dashboards, and reports, citizen data scientists are given tools like Tableau and Microsoft Power BI to develop, support, and enhance these capabilities. After all, they are the subject matter experts and stand to gain the most by becoming more data-driven with their business processes, so why not give them the tools to succeed?

Readers of this blog and my book Driving Digital already know that I am a huge proponent of citizen development and democratizing data. But these programs require IT and data leaders to provide more than tools and access to data. In some cases, what’s needed are practices like using agile in data science programs, while other times governance on citizen development is required. I don’t want to see organizations deploy more advanced tools, only to see a data debt explosion similar to what’s happened before with spreadsheets and Microsoft Access databases.

Three Capabilities of a Data Visualization Center of Excellence

So in Episode 23 of 5 Minutes with @NYIke, I explain three aspects of developing a citizen data science center of excellence (CoE). You can watch the episode below, then please continue reading!

The episode shares three best practices.

• Using agile methodologies to guide how citizen data scientists intake work, prioritize, and establish requirements
• Instituting naming conventions, data catalogs, and data dictionaries and creating other conventions around dashboards and worksheets
• Establishing user experience and design standards in visualizations such as how different chart types are used or how colors are applied

As I mention in the video, StarCIO has twenty-five focus areas for CoEs in disciplines like agile planning, PMOs, devops, and citizen data science. Hence, the video provides a starting point. For small organizations, some disciplines are common sense, and self-organizing teams with at least one internal expert will figure out reasonable practices.

What Happens to Citizen Data Science Without Functioning CoEs?

But for larger and more distributed organizations, it’s easy for citizen programs to fall victim to one of these tragedies.

• Anarchy where no common practices lead to mounds of debt and potentially other risks
• Chaos because too many people are trying to create standards or promote their way of working
• Analysis paralysis where a CoE is defined, but productivity suffers because they take too long to provide value to their constituents

So what should leaders do to avoid anarchy, chaos, or analysis paralysis?

It comes down to finding leadership with a background in the domain, defining a vision for the CoE, and leading by example while planning and executing the CoE’s mission. It requires seeking outside-in feedback by attending conferences or seeking advice from experts.

If you want my help, just ask!

Data viz references: Must Read Books By Black Authors, Bangalore’s Residential Hotspots, Climate Change: Global Temperature, Tableau Public Explorer