Gartner technology analysts predict that organizations leveraging augmented analytics solutions will grow at twice the rate of those that do not use these solutions. Those organizations that provide self-serve augmented analytics to their business users can achieve market goals and stay abreast of the competition with fact-based decision-making and a team that leverages analytics daily to make those decisions.
If your business is considering the citizen data scientist approach and wishes to democratize data and cascade the use of analytics across the organization, it is important to engage business users and show them how they can use analytics to make their job and role easier.
In this article, we consider some business use cases and examples of how predictive analytics can help the average business user get real, actionable information to complete tasks more accurately and quickly.
Predictive Analytics Sample Business Use Cases for Citizen Data Scientists
Customer churn: 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. Citizen data scientists can use predictive analytics to improve customer retention and reduce customer churn, identify and rank customer dissatisfaction issues, and identify and improve marketing messages and campaign effectiveness. Business users can also identify and conceive new services or products to attract and retain customers.
Loan approval: The cost of dealing with “bad” loans is high, and it reduces profitability and productivity. To succeed, these businesses must have a dependable process for attracting the right clientele and reviewing, approving, and managing loans. Citizen data scientists can use predictive analytics to improve the loan approval process to speed the process, provide a more accurate review and decision process, decrease loan defaults, and optimize available funds.
Predictive analytics using external data: The ability to integrate data from sources outside the enterprise is crucial to the success of a business and is often a major portion of a team member’s role in the organization. External macro data is often readily accessible and government data is often available for free, but analysis of multiple sources of external data can require a manual process that is tedious and time-consuming if an augmented analytics solution does not easily handle it. Citizen data scientists can plan more accurately adjust and manage marketing messages and advertising, optimize inventory and product supply, analyze and make decisions about pricing, products, and services and improve maintenance and planning processes.
These are just a few of the ways in which a citizen data scientist can use augmented analytics and predictive analytics on a day-to-day basis to test the accuracy of existing policies and decisions and to adapt quickly to the market and competition. You can explore more business use cases for a variety of business functions and industries here.
When an organization implements a citizen data scientist initiative, it can leverage assisted predictive modeling and provide advantages to the organization, business users, and data scientists, and it can provide numerous benefits to you as a citizen data scientist candidate.