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When I survey the data and analytics practices in large organizations, I often see three distinct types of data workers:
- Reporting and Data Analysts who are mired in spreadsheet hell, and whose days consist mostly of cutting and pasting columns and rows from disparate spreadsheets in hopes of getting to one simple report,
- Citizen Data Scientists, who have been able to get out of spreadsheet hell with self-service analytics tools, and can automate their data blending and prep to have time to think about their data, and
- Data Scientists, who are writing code to create machine learning models to solve highly-focused, but limited, business problems.
Often, these worlds rarely collide, as each type of data worker analyzes data in silos in separate lines of business. The shortcoming with these type of disjointed data practices, however, is the lack of a natural path to go from getting simple reports to automated data blending and prep to advanced machine learning models.
With today’s self-service analytics and data science technologies, however, it doesn’t need to be this way. In fact, the more you can bring these data practices together and create a continuum, the faster you can elevate your organization’s overall analytics and data science strategy.
What does this actually look like in practice? Well, first, it means that you have to build out a solid Citizen Data Science community who are the glue along your continuum. Citizen Data Scientists are typically data analysts who come from the front lines of working with your data day in and day out, and who are growing in their statistical skills and knowledge about machine learning. Given the right technology and training, these data experts can rapidly help your organization accelerate data science.
While the technology is the key enabler, the technology in and of itself is not enough. As I describe in “Citizen Gain: Citizen Data Scientist as the Catalyst for the Modern Analytics Strategy”, it takes a holistic deployment strategy focused on people and processes along with the technology to achieve successful business outcomes and sustainable solutions.
As part of this strategy to successfully deploy your technology, putting Citizen Data Scientists on the same team as more traditionally trained data scientists allows you to create apprenticeships that bring significant analytic gains. The data scientist can develop machine learning models that the Citizen Data Scientist can inherent and learn from. The Citizen Data Scientist can then build upon or tweak these models under the data scientist’s supervision and mentoring.
This approach allows the Citizen Data Scientist to learn advanced analytics in the context of your organization’s business problems and data. The data scientist, in turn, develops management skills, and can grow into leadership roles. And, by having your data scientists move into leadership positions, you elevate the overall data culture of your organization. Additionally, with data scientists being in short supply, a handful of Citizen Data Scientists, who are already experts in your data, may give your organization greater analytics lift than if you were to bring in one data scientist unfamiliar with your data and business.
In reality, however, the Citizen Data Scientist is an oft-overlooked catalyst to modernizing analytics initiatives quickly. Having Citizen Data Scientists and data scientists practicing data science as a team sport together creates a synergistic advanced analytics practice in which your whole organization benefits.
This blog was originally published on Heather's website, citizengain.net.
