How to Expand Citizen Data Science: From Successful Analysts to Data-Driven Organizations

More companies have embraced citizen data science and have deployed tools like Tableau, Microsoft Power BI, Qlik, Domo, KNIME, Sisense, Google Data Studio, Amazon QuickSite, and other self-service BI to business teams.

It’s common to see these tools deployed to individuals scattered across the organization, and each focused on their department’s business objectives. For example, digital marketers are likely to analyze campaign performance, operations managers review supply chain risks, and financial analysts evaluate profitability.

Other times, a centralized group takes on dashboarding, data visualization, and lightweight data science work as a service. Business leaders expect this team to maintain reporting tools and respond to special requests.

To centralize or decentralize data scientists?

While centralized citizen data science groups are more likely to drive the creation and support of best practices, it can also be a barrier for organizations looking to become more data-driven. Having a centralized data visualization or reporting team is effectively a punt – allowing business teams to stay an arm’s distance from analytics work. They can sometimes formulate unrealistic requests to the centralized data science team and are likely still using complex spreadsheets for their most strategic analysis.

As a CIO, I’d rather push the citizen data science tools to business departments where they are more likely to ask the right questions, understand the underlying data, and translate insights into faster data-driven decision-making. I seek data owners who will improve data quality and find innovative ways to use analytics to grow business or drive efficiencies.

But I’m also worried about creating a new generation of data governance issues, where citizen data science work goes unchecked, and business users create an unmanageable number of complex dashboards and data visualizations. I’m concerned about duplicate reports, outdated dashboards, and data visualizations with no one supporting them – all examples of data debt.

Avoid centralization by establishing a center of excellence

My answer to this conundrum is to create citizen data science centers of excellence (CoE) that promote best practices, drive skills development, and establish self-organizing standards. While they may focus on data visualization, their responsibilities extend to dataops and data governance.

You can read some of my previous posts on these topics, including these three top articles:

• Energize a Center of Excellence that Empowers Citizen Data Scientists
• How to Expand Data Governance with Successful Citizen Data Science
• Five Ways to Finding the Citizen Data Scientists in your Organization 

Also, please read some of my stories on being buried in bad data, chapter 7 of Digital Trailblazer, and some of my best data-driven organization best practices in chapter 5 of Driving Digital.  

Start developing a citizen data science CoE

Here are a few things to focus on in developing a citizen data science CoE:

1. Drive collaboration among practitioners – Identify tools and communication mechanisms for knowledge sharing. For example, the CoE can use an Atlassian Confluence Space to document best practices, share reusable data visualizations, or ask questions.
2. Take ownership of data catalogs and dictionaries – When a citizen data scientist adds a new data source, creates a new calculated measure, or builds a rule to create dimensions, are these documented in the data catalog? CoEs should have checklists and establish naming conventions for managing derived data and calculations.
3. Establish release standards – When is a data visualization ready to be released to end-users and decision-makers? What are the quality standards, and what testing is required? Self-service BI tools often support real-time editing, which is powerful when developing dashboards but makes it easy to deploy costly errors. What’s your organization’s definition of done?  
4. Incentivize internal training – Requiring a citizen data scientist to take a tool’s basic online training is an important first step but insufficient training for working smart and safely with enterprise data sources. A CoE should set up business-specific training that includes labs with proprietary data sources and covers data governance practices. Leadership should incentivize CoE members to develop course materials and teach classes, while employees should be required to take the training.
5. Celebrate and market success – The best way to grow support and develop a data-driven culture is by showcasing and marketing the CoE and analytics programs’ successes. Empowerment is contagious; the more leaders celebrate wins, the more people seek involvement.

When it comes to data and analytics, having a few go-to people or a centralized data science department doesn’t go far enough to drive a data-driven culture. Reach out to me if you have questions or want me to write more about data-driven centers of excellence.