What is Decision Intelligence?
“Decision intelligence” is a discipline first introduced by Dr. Lorien Pratt in the mid-2000s. In a 2008 Quantellia white paper co-written by co-founder Mark Zangari, Pratt and Zangari argue that modern decision making is becoming more complex. As a result, traditional data (analysis) and (expert) knowledge approaches are insufficient and often add more complexity. Therefore, a methodical approach that synthesizes data and knowledge is needed to improve decisions. This approach, called decision engineering at the time, included the following:
- A clearly defined decision objective translated into formal specifications
- An iterative design process that combines data and knowledge
- Change control, QA, and security
- A visual representation of all aspects of the decision
Fast-forward a decade, Cassie Kozyrkov, former Chief Decision Scientist at Google, formed an internal advisory group, Decision Intelligence Engineering. At the time, she defined decision intelligence as “the discipline of turning information into better actions at any scale.”
Through the writings and talks of Pratt and Kozyrkov, the term caught on and evolved. Gartner defines decision intelligence (DI) as “a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback.”
What is the Difference Between Decision Intelligence and Business Intelligence?
BI is a discipline for uncovering insights from business information. Typically BI involves a process of collecting data, “cleaning” it, querying and/or aggregating it, and producing visualizations that are understandable by business users. Organizations usually have a data warehouse and leverage a variety of tools ranging from spreadsheets to sophisticated reporting software. BI examples include sales reports, campaign metrics, and financial summaries. BI provides a view of the past that must be interpreted by human decision makers to inform future decision-making. The goal of BI is to make better, data-informed decisions; however, BI in itself doesn’t lead to any decision. DI, on the other hand, starts with a decision. Therefore, DI provides a context for insights (from BI and other sources) and a path to transforming those insights into action.
What is a Decision Intelligence Platform?
A decision intelligence platform (DIP) is technology that supports decision intelligence through a collection of tools that model, execute, and monitor decision making. Organizations leverage DIPs to augment and sometimes automate decisions. Due to its broad definition, DIP vendors vary in scope. Decisions can be short-term, mid-term, or long-term. In addition, they can range in number and frequency. The data, DIP, and infrastructure required will depend on the type of decisions DI is applied to.
What’s the Difference Between a Decision Intelligence Platform and a Decision Management Platform?
Decision management (DM) is a related but separately emerging discipline to manage decisions in automated systems. Traditionally, these decisions are hard-coded into process steps. As a result, decision logic is scattered and often duplicated across various systems within an organization. Over time, managing this logic becomes increasingly cumbersome and requires significant IT involvement. With DM, decisions underlying processes are treated as a separate asset.
Through decision management platforms, organizations can isolate and manage decision logic on its own lifecycle. Decision management platforms provide an environment where business analysts and domain experts can define decisions and deploy decision logic as a decision service. Once deployed, the service can be invoked by any application, system, database, or other service. In addition, many decision management platforms have monitoring capabilities to measure business performance. These insights can be used to further refine and continuously improve decisions. Therefore, decision management platforms are decision intelligence platforms specifically for automated, short-term, operational decisions.
Decision Intelligence with SMARTS™ Decision Management Platform
SMARTS™ by Sparkling Logic is a cloud-native, enterprise-level decision management platform that empowers business users to leverage their own data to easily model, design, test, deploy, monitor, experiment with, and update decisions, whether explicit or AI-driven. SMARTS™ combines proprietary and best-of-breed technologies to support rules authoring, AI & ModelOps, decision analytics, and lifecycle management in a low-code/no-code and fully transparent manner. In addition, SMARTS™ modern technical architecture ensures scalability while maintaining high availability and performance.
Unlike other decision management platforms, SMARTS™ enables users to develop, maintain, and optimize decisions in the context of data and decision analytics. Users can create and run regression tests, simulations on large data sets, and champion-challenger experiments. Pre-defined QA flags, KPIs, and other metrics can be viewed in a customizable dashboard. This continuous feedback ensures consistency and quality throughout a decision’s lifecycle.
Learn more about decision management and how SMARTS™ Decision Manager can help you achieve decision intelligence for your operational decisions.