How to Kick Off a Citizen Data Science Program

Big Data Scientists
Citizen data scientists is catching on. Perhaps your organization can't hire or retain fully skilled and trained data scientists because of the skill shortage and the increasing salaries top talent command. Or perhaps you believe in the democratizing of data and that business analysts that were once influenced to become spreadsheet jockeys just need to be retooled with new data visualization skills and data governance principles.

Either are strong rationale to rethink how to transform the people, practices and technologies around your internal enterprise data and to take steps to drive a data driven culture.

Getting Started with Citizen Data Science Programs


So here are some tips on how to getting a citizen data science program started:

  1. Find a handful of underserved business units or operational groups that desire to be more data driven but are lacking the tools or practices, This can easily be the marketing department that needs to be more data driven to define segments and process leads. It could be the sales department that needs better reporting to drive sales management practices or the finance department that are under pressure to slice/dice P/Ls in new ways at greater frequency. 

  2. Cultivate a relationship with these business leaders to insure they are ready to take on a transformational challenge. If they are not ready to participate and sponsor this initiative it will fall short when you need to engage their resources to become citizen data scientists or you need their sponsorship to promote organizational change on leveraging any new data tools. 

  3. Find the data super users that are currently doing data work manually. These may be users that are skilled at asking good data questions, are very hands on with spreadsheets, or elect to use database tools that drive data silos. They may also be versatile running analytics in specialty tools like CRM, web analytics, or even ERP. These users have skills, but need technical direction on which tools the organization wants to support long term. They also need defined data governance practices to help avoid a new generation of data landfills.

  4. Define standards that can be developed incrementally but insure a scalable set of organizational practices. What data visualization tools will be used? What tools will be used for modeling? How do you separate what's in "development" vs. testing vs. ready to be used in decision making? Where are data dictionaries maintained? How is data access established? These are some of the new data management practices IT needs to help support and that citizen data science teams need to practice.  

  5. Define an agile data practice that provides citizen data scientists a prioritized list of problems to solve, a delivery practice based on agile principles, and assigns roles/responsibilities in developing solutions. By way of example, here is a practice defined to help find nuggets in an organization's dark data using an agile data mining process. The idea is to make sure there is a disciplined cadence that defines priorities, drives citizen data sciences to commit to completing an analysis in a fixed duration, and insures results are presented before moving onto new challenges.

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