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The rise of the citizen data scientist

15 July 2019 by Michael Giffney 0 Comments

The rise of the citizen data scientist.

The world of data is growing exponentially every day - the places we capture it, the means of collecting it and of course, the many many uses for it. However, as the data boom has been so fast and so recent, the world is struggling to provide an ample number of data scientists that can put this data to proper use.

To become an expert data scientist takes years of study and real-world training. Currently, there isn’t enough time for that, as people want to use their data now. To combat this lack of skilled data scientists and the rising cost of hiring them, citizen data science (CDS) has come to the rescue of the data that would have otherwise fallen by the wayside.

Citizen data scientist VoicePlus

What is a citizen data scientist?

Gartner defines a citizen data scientist as a person who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics. In other words, it’s an individual who can perform both simple and moderately sophisticated analytical tasks that would have previously required more expertise.

How does CDS help my business?

By learning in weeks instead of years, business analysts can extend their analytical abilities and provide businesses with far cheaper and much more readily available analytics-derived insights. The tools used for CDS can also help highly skilled data scientists become more productive, which benefits all, as no matter how many citizen data scientists you have working for you, deep and demanding projects will always need a highly trained professional.

As more mainstream self-service analytics and business intelligence (BI) tools become available, the rise of the citizen data scientist will become more apparent. To exploit CDS and ensure your business improves on their analytics initiatives, you should:

Expand the variety of data accessible for analysis.

The objective for CDS is often to access sources that would otherwise be overlooked. They may be too complicated, too large, too diversified or even just too off the grid for exposure to mainstream self-service users. But it’s these sources that will allow citizen data scientists to create more precise and more detailed models that will ultimately lead to increasingly accurate and representative insights.

Increase the range of analytics capabilities available to users.

CDS gives businesses a way to properly utilise modern analytics and business intelligence (BI) tools. With proper training and access, CDS will allow users to:

  • Cleanse and pre-process external data.
  • Quickly find information needed to make decisions.
  • Embed automated insights in business applications, i.e. impact all employees within the context and not just analysts and data scientists.
  • Leverage real-time context data to support decisions for customer support.
  • Track a model’s degradation signal.

The introduction of citizen data scientists will bring a new and fresh perspective on the analytics process. With their unique approach, they will be able to uncover, correlate and explain events and characteristics that may otherwise go unnoticed or be misunderstood.

Make data science and machine learning accessible to a broad audience.

A business should be thoroughly vetted for potential candidates to undertake CDS training, and the use of data science and machine learning should be extended to all employees with extensive analytical skills. Once trained, these individuals should be able to operate almost autonomously, with some ongoing support to prevent siloed results.

By having a range of employees with a CDS understanding, the overall costs of analytics will drop dramatically. However, most businesses choose to continue budgeting the same amount but put the costs into undertaking more projects to increase their analytical study output.

Increase analytical agility and enhance the analytics process.

It should become a company policy to utilise internal citizen data scientists for analytical reasons before engaging a highly skilled and highly expensive data scientist. By having internal interests, citizen data scientists offer business-critical outcomes that can come from the domain experts themselves. By identifying internal teams with CDS skills, an organisation can accelerate and enhance their analytical process where appropriate (See figure 1).

Insert Figure 3 from Gartner doc.


Citizen data scientists can use their familiarity with a business to enhance the acquisition of data.


Utilise CDS and self-service preparation tools to save up to 23% of a data scientist’s time on a project.


Maximise the powerful tools available to citizen data scientists to shorten the decision-making process.


Eliminate the often siloed analyses of a traditional data scientist by keeping the analysis and changes in your business.


Critical understandings of their business allow citizen data scientists to deliver faster and more relatable feedback and in turn, tune and modify models as needed.

While it would be ideal to roll CDS out across an entire business, it’s just not feasible, however, with the proper training and correct tools, certain areas may be able to operate autonomously across their analytical workflow. What’s important to remember is that every analytical strategy must clearly state who will do what, so that ideally, a business question can become a business outcome within a self-contained workflow.

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