What are some characteristics of citizen data scientists? They’re often tired of looking at the same old reports. They want to get their hands on all the data themselves and find new ways to get answers. They’re willing to learn new methods and use new tools. They often think, “I don’t want to ask a statistician. I want to try it myself. How could I get at the answer?”
These analytically minded workers go by other names too, but the citizen data scientist term is gaining in popularity. Coined by Gartner, the analyst firm defines a citizen data scientist as “a person who creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics.”
How can you identify these people in your organizations, introduce policies to help their numbers multiply and point them in the right direction? Start by following these steps:
- Empower people with access to data and tools for analysis (for example protonautoml.com is a one click automated data science tool with no pre requisite)
- Recognize, encourage and reward citizen data scientists for their contributions.
- Recognize people with high analytical potential and provide them with training and developmental assignments.
- Make sure citizen data scientists don’t feel like they’re swimming against a tide of business as usual.
- Reward imaginative and innovative approaches to traditional businesses issues.
Any program that supports and encourages citizen data scientists should start with a plan for democratizing analytics. You democratize analytics when you give people access to data and the tools to work with it to transform the discovery process. With more people actively looking for new answers, discovery becomes more widespread in the organization and a bigger part of the mindset. It is practiced by people in all roles at all levels, not just R&D or the analytics staff.
Discovery is exciting, but you don’t realize business results until you deploy new insights as business decisions and actions. Full-scale deployment may include many steps, including:
- Embedding predictive models in business processes and workflows.
- Training people to work and make decisions differently based on new information.
- Putting analytical applications into production with live data sets.
- Implementing all the necessary business governance, controls and metrics.
To help citizen data scientists succeed, we need to make it easy for them to transition their insights and methods to the team responsible for deployment, and offer them key roles in training business colleagues on how to use the new analytics. The citizen data scientist is an experimenter, a prototyper and an anticipator.
As the discovery process becomes more pervasive, you will notice that people explore and discover in different ways. Consider the millennials. They’re the gaming generation. They expect an instant response. They expect a slick and intuitive interface. Their games send them on quests, where you have to learn as you go, and learn to put new tools to use. Test-and-learn comes much more naturally to them than plan-and-execute. They are attuned to discovery and speed, and that doesn’t change when they enter the workforce.
By most corporate norms, the millennials have a bit of a rogue attitude. They question business as usual, not just conform to it. Rather than seeing these attributes as negatives, consider how the millennial mindset can help your organization. Many millennials have the right attitude and the technological facility to be citizen data scientists. However, they may lack experience and knowledge about the business, its customers and its processes.
Try teaming the millennials on your staff with more experienced data scientists, and empower them with technology. Then observe how the teams think, work, prototype and discover together — and how fast they get things done. Organizations aren’t going to tame the millennials. On the contrary, the millennials’ methods are going to prevail, and that includes a gaming approach to analytics.
Embracing citizen data scientists will likely spur some organizational adjustments. You can have positive results by making adjustments in the professional analytics group, the IT organization and in the leadership of the organization. Let’s look at each.
Professional analysts. With the democratization of analytics, does it mean we won’t need as many statisticians and data scientists anymore? Quite the opposite. As organizational maturity with analytics rises, people understand what analytics can do. They want to explore more sophisticated questions, and they envision analytical models far more advanced than they can build. They need the data scientists to work with them, push the envelope even more and validate what they are trying to do. As you encourage citizen data scientists, the overall demand for analytics rises. Professional analysts on your staff should see citizen data scientists as collaborators — even catalysts — not as a threat.
IT organization. Citizen data scientists also place new and different demands on the IT organization. They want more data, including more unfiltered data. They want detailed transaction data to work with and business performance information that isn’t summarized and denatured. They also need computing environments in which to experiment with data and analytics and to prototype models and applications. Sometimes those environments need large scale and processing power. And the citizen data scientists want to be empowered with up-to-date tools and technologies (and they’re probably not shy about supplying their own if readily available in the marketplace). IT must recognize and cultivate this new class of power user. The citizen data scientist is a harbinger of things to come as technologies of many kinds are democratized and more readily available outside the organization.
Business leadership. Business leaders should embrace the democratization of analytics. It’s happening, it’s going to be pervasive, and it’s good. But it’s not something that you’re going to control. So don’t try the top-down approach: “We’re going to appoint and train citizen data scientists.” Instead, teach your managers to recognize and reward those who emerge. Note where you have pockets of citizen data scientists, most likely in finance, manufacturing and marketing. Find ways to spread the wealth, through collaboration or rotation, to functions underserved by analytics.
Leaders should create an environment where citizen data scientists can flourish. On one hand, give them time and license to experiment. On the other hand, challenge them: “Show me things that I don’t know about my own business.” Pay special attention to the younger talents in this area, with their experimenting and gaming mentality, and see what you can learn. Let the citizen data scientists influence others, and you’ll see the organization’s analytical maturity rise. Seed analytics champions, and let them organically transform your organization.