Decisions are at the core of every organization, be it a Fortune 100 company, a start-up, or a governmental agency. In this blog post, we provide answers to the ten most frequently asked questions about decision management technology.
1) What is decision management technology?
There are two types of decision-making technologies: First, descriptive technologies implement how people make choices among alternatives based on their beliefs and preferences. Second, normative technologies implement regulations, policies, or strategies regardless of the beliefs or preferences of those who follow the decisions. The latter type is decision management technology. So, when someone refers to decision management, they are referring to technologies implementing formal laws, industry regulations, company policies, and business strategies.
2) What is the difference between business rules and decision management?
Business rules implement industry regulations, business policies, or subject matter knowledge in the form of if-then statements or conditional action procedures. A rule engine checks all the predicates/conditions and fires the statements/procedures. To select which statement to add or procedure to run, rule engines rely on heuristics that are part of the industry regulation, business policy, or subject matter. Decision management is more than that. In fact, decision management comprises multiple technologies. The simplest are decision tables, trees, and graphs. The most sophisticated combine business rules and predictive models. For example, SMARTS™, Sparkling Logic’s decision management platform, integrates eight decision engines into the same platform. An organization may combine multiple decision engines to solve the problem at hand.
3) What types of decisions do decision management technologies automate?
Like any technology, decision management technologies are not a one-size-fits-all solution for every decision problem. They are not suitable for strategic, long-term decisions that companies make once a year. In these cases, organizations tend to use optimization technologies. They are also not suitable for cases with uncertainty and where probabilistic technologies such as probabilistic graphical models are used. Decision management technologies work best when there is a substantial number of decisions and calculations that are often nested, invoked, and likely to change often. Therefore, one must consider a decision management product for the operational and day-to-day decisions.
4) In which industries are decision management technologies primarily used?
Decision management technologies are primarily used by companies working in highly regulated industries such as financial services, insurance, and healthcare. They allow the implementation of legal requirements in the nervous system of companies, in front-, middle- and/or back-office. But you can find them in other industries such as telecommunications for network, service, or customer management, in retail for product recommendation, and even in media for content personalization.
5) For what applications are decision management technologies used?
Decision management technologies are widely used for loan origination, risk management, fraud detection, and money laundering prevention. These are typically cases where organizations make decisions and calculations thousands and sometimes million times a day and may change based on the market dynamics or global economy, or updates to regulations or business strategy. Decision management technologies can also be used for data transformation as a better alternative to scripting languages to move, unify, and enrich data from one layer to another of a data platform or marketplace. In fact, decision management technologies can be used to automate every complex non-linear process such as the ones we find in product configuration or condition-based diagnosis.
6) Who are the users of decision management technologies?
The users of modern decision management systems are not IT people but business people. So, they come with features that allow non-specialists to use them without IT intervention. With modern decision management systems, IT only takes care of the first installations and configurations. Decision management systems come with everything necessary to ensure the governance and security of the applications developed as well as the ease of integrating them into the corporate IT architecture. For example, SMARTS comes with an easy-to-use graphical authoring interface, pre-deployment rule testing, real-time decision performance monitoring, and much more.
7) How is decision management related to data analytics?
There are three types of data analytics. First, descriptive analytics that allows companies to get a status of how they are performing against their goals. Then, predictive analytics focuses on what might happen. Then, prescriptive analytics that transforms insights from both descriptive and predictive analytics into decisions and actions with the support of decision management technologies. In this sense, decision management technologies operationalize data analytics.
8) Is decision management data-based or knowledge-based?
The “data vs. knowledge” debate is an old debate about whether knowledge about a subject should be hard coded or machine learned. Some researchers and practitioners encode this knowledge in the form of rules. Others develop programs that learn from available data using statistical methods to generate models that can make predictions. At Sparkling Logic, we support a pragmatic approach that consists in using data, knowledge, or both depending on the problem and the situation at hand.
9) What are explainable or understandable decisions?
As more of our personal and professional activities are managed by data and algorithms, bias and discrimination is becoming more of a concern. Decisions, whether for eligibility or personalization, must be understood by all the stakeholders. Stakeholders not only include the analysts that manage the decisions but also the customers. SMARTS’ latest version, Vienna, implements understandable decisions through additional visual features that go beyond intuitive business rules writing. For instance, users can use the play-by-play feature to watch the decision happen before their eyes while refining the expected behavior.
10) How do decision management technologies avoid bias?
In our vision, one of the best ways to reduce biases, is to make decisions explicit (like the rules of laws) so that those who implement the decisions can test them out, one at a time or in groups, and visualize their outcomes in dashboards. Business rules with dashboards help to detect the consequences of decisions before putting them into production. Decisions should be kept separate from the rest of the operating system (ex. CRM, loan origination system, credit risk management platform). If you want to modernize or develop a decision management application, we can help. Just contact us to request a custom demo.