Adopting a decision management platform is often driven by the demands of ensuring a business is more responsive to its market needs. It can be prompted by a need to better define and implement the strategies required to become more competitive. Or alternatively, it can be a route to achieving regulatory requirements. Others regard it as a way to speed up adoption of strategies that use more advanced predictive models, or a path to richer datasets and alternative data, to help further drive accurate decision-making.
Whatever your goals, a decision management platform gives you the opportunity to pursue centralized decisioning. This doesn't mean all decisions are made in a single control center. It does mean that the whole business has access to better data, a 360-degree view of customers, advanced analytics and shared decision strategy tools that enable better decisions across the enterprise.
In this post I'll discuss some of the ways a platform takes you on the route to centralized decisioning.
Moving Beyond BI
Today most organisations have already implemented business intelligence platform. This offers a comprehensive understanding of what has happened in the past. It’s also regarded as the company’s rear view mirror. Observations provided enable organisations to analyse and draw critical conclusions about what changes are needed to achieve their commercial objectives.
A decision management platform takes this much further, enabling organisations to evolve their strategies to achieve selected objectives. In most instances, businesses start by implementing a single decision service, automating current baseline strategies alongside one or more challenger strategies. Over time, the successful challenger strategy is promoted to the new baseline strategy. From there a new challenger strategy can be developed, to help continually drive the business on.
The decision management platform enables use of richer datasets and a larger set of predictive models. As a result, we often see our strategic clients tweak and develop more sophisticated models by diving into the far broader datasets available today, along with advanced machine learning techniques, to help better inform smarter, scalable strategies.
Moving from Predictive Analytics to Prescriptive Analytics
The team responsible for the decision strategy is often capable of fine-tuning it even further to achieve significant business improvements. But much like drilling for oil, it can over time, become more challenging to continually achieve improved business results. The effort needed to put in often outweighs limited returns.
But by adopting optimization technologies - also known as prescriptive analytics - organizations can continue to understand, analyze and improve their business performance.
FICO optimization technology enables business owners to define one or more objectives, the likely constraints, the decisions to make and the data and analytics available to create more powerful decision strategies. The sophisticated mathematical solver underpinning the FICO optimization engine can run millions of permutations and calculates key performance indicators like profit, risk and investment.
Business users can then analyze and compare the strategies that best meet their objectives, based on trade-offs against risk and benefit. The technology and compute power available today enables evaluation of millions of combinations, enabling business to explore and find new strategies unlocking future business results.
Expanding from a Single Use Case
The more services implemented in the decision management platform, the more valuable it becomes for the business. In most cases we see business logic implemented in the first decision service being re-used for follow-on services.
Examples include affordability and profitability calculations, customer segmentations, or predictive modelling. FICO Platform enables continual re-use of logic, or connected decisions, across services.
Several organizations have already moved all of their decision logic onto a common decision-making platform. They developed blueprints and templates that act as starting point to enable the rapid roll-out new decision services within a matter of days. Businesses adopting this model have truly embraced the centralized decisioning concept and are now achieving significant benefits, as illustrated in case studies from a large North American bank, African Bank, and Bradesco and Itau in Latin America.