Business Use Cases Create Momentum for Citizen Data Scientists

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Read more about author Kartik Patel.

As businesses plan for data democratization, it is essential to include a strategy to ensure that business users will accept augmented analytics solutions and adapt to the new citizen data scientist role. With business users on board, the enterprise can capitalize on its strategy and optimize return on investment (ROI) and total cost of ownership (TCO) for its technology investment. 

Gartner predicts that by 2023, “overall analytics adoption will increase from 35% to 50%, driven by vertical and domain-specific augmented analytics solutions.”

So, how does your business ensure that the adoption of analytics will be successful? There are many components to be considered in your strategy. But perhaps the most important component is to make the adoption of augmented analytics and analytical techniques something that your business users can understand. 

In this article, we provide two examples of business use cases that will resonate with your users. If you want your users to transition to citizen data scientists and to willingly adopt this role, tell them a story! Make it clear how these tools can help them to perform tasks, be more productive, and gain more visibility by producing clear, accurate results that will impress their management and colleagues. 

Customer churn is something every business wants to avoid. The cost of acquiring and interacting with customers is one a business must fund and, every time the business loses a customer (customer churn), it must spend more money to replace that customer. Every business wishes to identify the issues that most often cause a customer to leave. Dissatisfied customers often close an account or choose another service provider without explaining their decision. The business wants to use predictive analytics to identify those customers who were most likely to leave and develop processes and strategies to improve customer retention and reduce customer churn.

Business users can leverage tools provided in assisted predictive modeling to identify customers who are likely to leave, and their issues, improve services and processes and increase customer retention. This technique can be used in banking, financial institutions, utility companies, business to consumer (B2C) and business to business (B2B).

Benefits include:

  • Reduce customer churn
  • Improve customer retention
  • Identify and rank customer dissatisfaction issues
  • Identify and improve marketing messaging and campaign effectiveness
  • Identify and create new services or products to attract and retain clients

Maintenance management is used by wise businesses to focus on maintenance to keep equipment up and running and reduce downtime. For businesses that perform maintenance services, anticipating required resources, hours on the job, and the types of training required is also required. Advanced analytics can take the guesswork out of production equipment maintenance and anticipate routine maintenance, which parts should be ordered and when and when equipment should be replaced. For maintenance businesses, advanced analytics can help to predict when maintenance and repair will be required on a piece of equipment placed in a customer location and which parts to keep on hand, as well as what resources and training employees will need to satisfy the demand. This technique can be used in manufacturing, production, infrastructure, utilities, and services businesses. 

Benefits include:

  • Optimize resources
  • Optimize parts and inventory
  • Optimize schedules and training schedules
  • Manage costs
  • Improve customer satisfaction
  • Anticipate and mitigate downtime

These are just two examples of business use cases. Every business is a bit different. Take a look at your teams, departments, and business units and develop an understanding of how each entity can use augmented analytics to improve performance and workflow. If you can show your business users how analytics will make their jobs easier and better, they will be more likely to want to adopt the citizen data scientist role and the augmented analytics tools they will need. Transforming business users into citizen data scientists provides advantages to the organization, to the business users, and to data scientists.