In numerous recent blogs, we’ve highlighted how optimization helps businesses – from Southwest Airlines to Sprint – determine the action or actions they should take to achieve the right mix of risk mitigation, profitability and positive customer outcomes – all within the timeframes that drive the greatest value. Southwest told us how this analytic capability helps them optimize everything from gate assignment to provisioning beer and peanuts, while Sprint is using optimization to intelligently onboard new wireless customers, ensuring they’re happy with their handset and plan choices without taking on additional unnecessary risk.
It has become clear, however, that many businesses, from banks to utilities, are still trying to figure out how to start their first meaningful optimization project – and IT is a critical barrier that often stops efforts before they get past the planning stage. One can hardly blame them – in fact, IT needs to juggle legacy application support, line of business (LOB) requests for new applications, and supposedly optimized budgets that continually shift resources to “what’s most mission-critical” – only to have those efforts redeployed yet again based on the whims of executive leaders. This makes it difficult to get any analytic projects off the ground. In addition, key analytic resources such as data scientists and operations researchers are in short supply at most companies, further hindering development and rollout of new projects.
In the past couple of years, however, tremendous progress has been made that finally allows businesses to rapidly turn insights from Big Data into executable decisions that are optimized to balance customer engagement and loyalty, profit, competitive gain, and even compliance. Three critical evolutionary steps have opened up the optimization playing field to more businesses across more industries. These include:
- The advent of the Citizen Developer: The emergence of a new breed of business user – also known as citizen developers – has spurred the development potential of data-driven, analytically powered applications. These business analyst-level resources can modify rule systems, wrangle data, do champion-challenger testing, recalibrate predictive models, and work on complex optimization business cases. Also, the presence empowered citizen developers can help reduce the overreliance on expert analytic resources, allowing an organization to better allocate its most skilled people to the most critical efforts.
- Technologies that help reduce the IT burden: The demand for rapid development of business solutions necessitates a new view on how IT and department LOBs join forces to build and manage applications. Open cloud-based technologies provide flexible deployment options and reduce barriers to entry for organizations that, in traditional IT environments would have forced a delay in the adoption of Big Data initiatives such as prescriptive analytics. The rise of virtually automated application development systems – powered by microservice architectures (i.e., breaking applications into services) and model-driven approaches – is reducing IT burdens by simplifying and abstracting the development process to lines of businesses. With these technologies now available, citizen developers now have the ability to help democratize business applications and help facilitate faster, smarter decisions by freeing IT from gratuitous (and slow) recoding efforts, while consequently redirecting scarce analytic resources to the most critical work.
- Powerful optimization modeling, solving and rapid application development are now bundled: Transforming Big Data insights into citizen developer-generated analytically powered applications results when open technologies and the best-in-class solution for solving large, complex optimization problems converge into a single, integrated platform. In this case study, you’ll learn how Sprint was able to rapidly develop powerful analytically powered applications without requiring additional development efforts, allowing them to increase profitability without increasing their acquisition expense, and quickly modify credit policies to adjust to changing market dynamics.