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Data & Analytics

Rise of the citizen data scientist

03/21/2019

by Scott Miller

There is a lot of talk in today’s business climate about data and analytics, but what is the best way to unlock the full potential of your company’s data?

We sat down with Scott Miller to discuss the emerging role of citizen data scientists and how your company can empower its employees to become proficient in new, emerging technologies and start delivering advanced analytics.

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I know that the term “data scientist” has been around for a while, so what is a “citizen data scientist” and why is this a new trend?

The big technology research houses coined the term and they define it as the bridge between traditional mainstream self-service data discovery by non-IT users and the advanced analytics techniques of data scientists.

Why don’t firms just hire a data scientist and be done with it?  Why go to all the trouble of transitioning their business analysts to be citizen data scientists?

It is a bit daunting if you break down what skills and experience a data scientist needs to possess to be effective in their role. Requirements include, but are not limited to:

• Significant advanced math and quantitative statistical analysis education, as well as practical application of these skills
• Extensive exposure to enterprise data and information management technologies and solutions
• A firm grasp of the strategic and functional dimensions of the company where they work
• Significant software engineering background

In a word, a data scientist is a unicorn – and they are in very short supply. The ability to hire or develop data scientists is out of reach for all but the largest companies or governments with significant financial resources.

So how do you replace all of that experience, skill, and knowledge of a data scientist to have a highly functioning citizen data scientist?

Well, you can’t really completely replace them, but with the advancements of data analytics tools and technologies these capabilities are becoming more accessible to the average analyst. Instead of the years of education and experience, more junior analysts can spend months in training on these tools and how they are applied and can quickly become proficient and start delivering more advanced analytics.

In the same way, these new analytics tools are democratizing the practice of advanced analytics to a broader set of employees.  It is also important to identify data scientists, either inside an organization or external, to be resources to coach and encourage the citizen data scientists in their pursuit of their analytics journey.

Does this mean that there is no longer a need for data scientists?

No! There definitely is still a place for the data scientist, and this space is where the scope and complexity of a business challenge or problem set is beyond the reach of the typical citizen data scientist. These are the extremely complex use cases and patterns that require significant resources, extended timelines, and the most advanced capabilities that a company can employ.

What are some of these new capabilities that exist in the analytics tools that enable the existence of the citizen data scientist?

New technologies, such as no-SQL and Hadoop, expose larger, more complex, and often unstructured data sets for analysis. These “richer” and more complex and contextualized data sources allow for insights through correlation that previously were not possible. Smart profiling and other advances in data preparation and curation allow very rapid processing of data to prepare it to be used in analytics.

Other advancements that show promise are natural language processing, geospatial analytics, behavioral analytics, and graph analytics.

What is the business value delivered by citizen data scientists?

The promise of the rise of the citizen data scientist is that the capabilities of more advanced analytics can now be applied more broadly and to more use cases in an organization. This enables more analysts and knowledge workers to be able to unlock insights from the ever-growing sources and volume of data that are being generated through an ever increasing digital business landscape.

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