How many decisions does your organization make every week, every day, every hour – even right now, as you’re reading this article? Decisions range from operational (such as “which offer should we present to this customer?”) to strategic (such as “should we acquire this company?”).
Real-time analytics enable faster, more precise and more effective decisions than conventional decisions made with stale data or no data. Think about how one poor decision at a telecommunications company could result in bad mobile phone service for the president of a large customer, and ultimately lead to a multimillion dollar contract being cancelled. Or, likewise, the consequences of making 100,000 bad $50 decisions.
High performance analytics helps companies make better real-time operational decisions. But they can also be used to improve the quality of tactical and strategic decisions.
Using high-performance analytics, data science teams can perform modelling, simulations and optimizations based on a complete set of transaction data, rather than relying on samples. Packaged high-performance analytics embedded into data discovery tools and applications allow the team to harness increasingly sophisticated analytic capabilities, with no need for prohibitive processing wait times or developer assistance.
Here are some best practices for making fast, real-time decisions without giving up the quality of the decisions.
1.Make slow operational decisions real-time
Operational decisions are mostly, or entirely, structured, and are typically repeated many times. Operational decisions that go from slow to near-real-time may require new software tools, new kinds of data, new business process designs, and other changes to the business. The point of real-time analytics is to respond to conditions as they are at the moment, not to process yesterday’s data or data from last month. Decision automation reduces costs by offloading work from people, can accelerate decision making to sub-milliseconds, and results in more consistent, documented and auditable decisions.
2.Track the results of real-time decisions and frequently modify rules and analytics
Most real-time operational decisions are repeatable. For example, a scoring model used to approve credit card transactions may be developed once on historical data, and then used for evaluating real-time credit card transactions for days or weeks. It’s important to track the results to make sure the models are still working correctly and to modify rules and analytics frequently to get best results as business conditions change.
3.Use system “guard rails” and human oversight to prevent real-time mistakes
Computers have no common sense, so they will make mistakes — sometimes dramatic and consequential mistakes. System logic should be used to check other systems, and people periodically should monitor systems. A “stop” button should be incorporated, so people can halt a rogue system quickly when a problem is detected. Also, system guard rails should be in place, sometimes in the form of “circuit breakers” that stop processing automatically when a problem arises.
4.Use continuous intelligence for situation awareness
Continuous intelligence (monitoring) systems run all day, listening to events as they occur, until they detect a threat or opportunity that requires a response by a person or system. The system proactively “pushes” an alert or other notification to a person via email, screen pop or other mechanism; or it triggers an automated response. For example, if customers are experiencing long wait times when they call the contact center, a real-time monitoring system will notify supervisors that service levels are degrading so they can make adjustments.
5.Provide multiple personalized views, but a common operating picture
Use continuous intelligence to provide a common operating picture across the enterprise. Each person involved in a situation may have a personalized view specific to their role within the organization, but providing the real-time analytics across the organization ensures that all involved have the same understanding of a situation.
6.Pursue decision management as a discipline comparable to data management and business process management
“Decision management” is the discipline for designing and building systems that make or assist decisions, where “decision” means determining a course of action. Decision-making systems are typically implemented by using rule engines, analytic software tools or third-generation programming languages (3GL). As companies use more real-time analytics, they are starting to practice Decision Management, alongside their Data Management and Business Process Management activities.