March 9, 2021

How to Find, Train, and Empower Citizen Data Scientists

Post by: Adam Widi

Adam Widi is a Senior Solution Architect in Data Analytics with over 15 years of experience in Business Intelligence and Data Analytics. In his spare time, he enjoys dabbling in machine learning and data science tools and online courses.

About the author: Adam Widi is a Senior Solution Architect in Data Analytics with over 15 years of experience in Business Intelligence and Data Analytics. In his spare time, he enjoys dabbling in machine learning and data science tools and online courses.

Advertisements for artificial intelligence (AI) and machine learning (ML) tools have been on the rise in recent years. This has raised awareness of and excitement for the capabilities these tools provide, and the business problems they can solve.

Some use cases – like customer churn analysis, product recommendation, and fraud detection – have leveraged data science concepts for quite some time, but the advancement in AI/ML technologies has made it possible to take on much more complex problems.

This rise in awareness and potential use cases has created high demand for (and a resulting shortage of) experienced data scientists to meet these growing needs.

Businesses are left with great ideas, but the inability to act on them. With the rise in demand, and advancement in AI/ML tools, many organizations are now looking for talent outside the traditional data scientist role.

Enter the Age of the “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.” 

Translation: not just anyone can become a citizen data scientist. 

How to Find Citizen Data Scientists

Citizen data scientists must share many of the same traits as a data science professional. Look for people with the following traits:

  • Familiar working with data and their relationships
  • Great problem-solving skills
  • Can think outside of the box
  • Curiosity
  • Thoroughness
  • Cautious to not jump to conclusions

These traits are important because often in data science (especially with those new to the process) the initial analysis can look very promising but may not generalize well to a production solution. This caution and thoroughness will result in safer and more trusted solutions.

To find citizen data scientist candidates, look outside and within your organization in the business and IT. Consider hosting a hackathon or development camp focusing on a fun data science problem. This is a great way to both identify potential candidates, as well as provide a kickstart for the training they will require. Kaggle provides great use cases for an event like this.

How to Train Citizen Data Scientists

Once you’ve found the right individuals, you’ll want to ensure they build the necessary skills to succeed as a citizen data scientist. Ideally, they would shadow a professional data scientist, participate in projects, and incrementally take on more of the responsibility for the data science activities in their projects.

This, however, is often not possible.

For many companies, there is no existing data scientist to learn from. In this scenario, it is important that you’ve chosen someone who is a self-starter and willing to explore the plethora of materials that the Internet has to offer. It is also important to partner your new citizen data scientist with representatives within IT to ensure tools are chosen that will integrate well within your infrastructure. 

How to Empower Citizen Data Scientists

Know that this will be a journey that you travel together. Patience is key, and training is important. This person will need time to build their skills and likely won’t hit a home run on the very first project.

They will need to learn about correlation analysis, model overfitting, supervised vs unsupervised learning, types of algorithms such as classification and regression (and when to use them), and many more things as they build their skills. They will also need to get some understanding in statistics as the complexity in their projects grows.

Small wins on less complex problems early on will give them confidence and keep them excited to take on bigger challenges. But be prepared for projects to fail through no fault of their own. Often, existing data doesn’t support the result you desire. Don’t let this person get discouraged when (not if) this happens. Be sure to ask what additional data may support the end goal. This may require investment in new business processes to gather this currently non-existent information.

Citizen Data Scientists Can Fill Your Data Talent Gap

Whether it’s in their job title or not, citizen data scientists are becoming more common in many industries. They help fill the gap in current data science talent, and (with the advancement of tools) can take on some of the challenges that were traditionally reserved for highly skilled data science professionals.

If you’re looking for data expertise to help you make data-driven decisions, contact us. Our team of data and BI experts can assist you with everything from developing a strategy to designing and implementing effective data solutions.

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