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Grow your own citizen data scientists with these 5 tips
By Matthew Denham,
Each day, 2.5 quintillion new bytes of data are created around the world. That’s a lot of data to manage, and it requires companies to invest in experts capable of analyzing it and using it to spur innovation.
A data scientist can be a valuable addition to any enterprise organization. In fact, there are over 2.5 million data scientist jobs in the US and this number grew by 12% from 2012 to 2016. The need for data scientists is so great, there has been a rise in what’s known as the “citizen data scientist.” This scientific role isn’t filled by someone who specializes in statistics and analytics — these scientists perform a different business function in an organization, with statistics and analysis taking a secondary role in their job obligations.
Citizen data experts aren’t meant to replace traditional data experts, but they serve a critical role by giving data-driven insights a business context. Though a master of analyzing data, a trained data scientist often doesn’t know the particular business complications that lie outside the information. And though a data scientist can optimize the international distribution strategy for a product, that scientist likely doesn’t know the complications that come with trade across certain borders or the unwieldy business partnerships that might obstruct these data-driven recommendations.
In this way, citizen data scientists provide a unique perspective and service to an enterprise. Here are five ways to grow your own personnel to fill this role:
1. Create and foster a business culture of self-service
A collaborative work environment is vital to encourage citizen data scientists to develop within your organization. Without interactions across departments, there is no reason for these professionals to acquire skills outside of the primary scope of their responsibilities. By encouraging work across departments, business and IT get to know one another better, and they can begin to benefit from their varied perspectives.
Ultimately, this encourages an agile environment, with business and IT communicating throughout development processes and with business professionals gaining a familiarity with how data can be leveraged to effect changes to the company’s operations. To maximize this self-service culture, professionals on both sides need access to a range of tools, including cloud-based solutions that can support their efforts to dig deeper into data science and apply their business knowledge to data analysis and inquiry.
2. Remove barriers to secure data access
Enterprises can cultivate their employees’ ambition by empowering their curiosity. Delving into data science is not going to be a quick and painless experience for business types who lack this experience. These workers need the freedom to test, explore and learn about this field without the fear of repercussions or impatience on the part of the organization. Not every business professional who dips their toes into the waters of data science will take the plunge and make meaningful contributions, but the encouragement of this experimentation will speed up their development and productivity.
3. Offer easy-to-use tools to encourage data exploration
Cognitive solutions are very valuable to the learning and development process. The technology behind a cognitive tool is very complex and very valuable to professionals tackling large-scale data analysis. However, the best of these tools are packaged into easy-to-use platforms that simplify the process to get answers. It is these types of tools that encourage the discovery and exploration of data to unlock hidden insights. These cognitive solutions can also support automation, which eases the burden on IT and business professionals. Meanwhile, data-driven product feedback can help guide users to meaningful insights — and your citizen scientists can place this knowledge in the proper context.
4. Operationalize the ecosystem of data assets
A citizen scientist is an asset, but it’s not the only one you should seek to develop. Your organization will need to identify the data roles required in your own work environment. This isn’t just the tandem of data and citizen scientists, but also the presence of data engineers and developers so your organization can take action from these newly “unlocked” insights.
Additionally, you need to develop a template for how these various roles will collaborate with one another. As insights are developed, your team needs to have a system in place that takes promising insights and turns them into active operational strategies. It is vital to develop a plan of action so that the enterprise can leverage this analysis for tangible gains.
5. Start small, think big
The development of citizen scientists will take time. As such, enterprises are wise to commit to an iterative approach that challenges results with evidence. As these scientists gain an understanding and familiarity, they will have greater confidence to challenge these results and apply their experiences to improve the use of data. Success will breed repeatability, and the value of this scientific approach will grow exponentially.
Citizen data scientists apply their business knowledge to data analysis, explaining how the data fits into their companies’ strategies. This synthesis of analytical skills with specialized skills is an important asset for enterprise brands to cultivate.
In the end, you have to build the culture to support the citizen analyst as they explore data and find meaningful insights.
Once found, key roles in the organization can collaborate to operationalize those insights to optimize decision making and ensure better business outcomes.
This article was originally published on Mobile Business Insights.