You’ve collected your data, organized it, and created a model to help you understand what you’re working with.

Now what?

For many organizations, the next step isn’t so clear. Only 50% of models ever make it to production, which means many aren’t even realizing the value of their investment.

One direction organizations can take when using their data models is decision intelligence. This involves applying machine learning and automation to enhance human decision-making, leading to better, faster and more insights-driven business decisions. With this approach, organizations can answer key questions using insights derived from their data.

Whether you’re new to decision intelligence or already familiar with it, here are 10 terms you should know when pursuing a decision intelligence solution, along with industry use cases.

1. DecisionOps

DecisionOps refers to practices and tools used to manage the decision intelligence process, including monitoring, versioning, deployment and governance. A DecisionOps strategy aims to make it easier to build, maintain, and govern decision-making processes. This involves maintaining accuracy, decision flows and outcomes. DecisionOps helps organizations efficiently use data insights, ensuring models are robust, reliable and compliant with standards and policies.

2. Decision – but not your standard definition   

In broad terms, a decision is a conclusion reached after consideration. In decision intelligence, this consideration equates to decision analysis, which includes a systematic, quantitative and visual approach to evaluating the choices available.

3. Decision architecture

Decision architecture is the design of choices and decision-making processes. It involves systematically organizing and structuring information, options and feedback to guide an individual’s decision toward a desired outcome. This practice is seen in various settings, including product design, marketing, policymaking and consumer behavior.

4. Decision analysis

Decision analysis takes a systematic, quantitative and visual approach to addressing and evaluating the important choices that businesses face. It uses tools like decision trees and influence diagrams to visualize and analyze options. SAS Intelligent Decisioning users can use decision objects – predefined components within the software that represent specific decision points or criteria – to streamline and enhance   their decision analysis. These decision objects simplify the process of modeling complex decisions. Overall, decision analysis helps organizations make more informed, strategic decisions by providing a clear framework for evaluating complex scenarios and choosing the best action.

5. Business rule

A business rule is a condition to be evaluated, followed by an action to be taken if the condition is met. A set of rules is known as a rule set, where rules are grouped due to their interactions and dependencies.

For example, a bank may want to automate loan approvals and rejections. After developing their decision architecture, leadership determines that all applicants with no delinquencies and credit scores above 700 are preapproved for loans. In contrast, those with scores below 700 need additional validation. The bank then creates a rule set that evaluates additional criteria, such as years on the job and debt-to-income ratios, to make a final approval decision.

6. Treatments

When people hear the term ‘treatments,’ they typically think about medicine or how to manage a lawn. In the context of SAS Intelligent Decisioning, a treatment is a feature that allows organizations to define and present specific offers to individuals who contact them due to an inbound marketing campaign. Treatments help tailor the customer experience by providing relevant, personalized offers based on data collected during the campaign. This targeted approach enhances customer engagement and increases the likelihood of successful outcomes for marketing initiatives.

7. Reference tables

Reference tables, often called lookup tables, are essential tools that help associate data with abbreviations or other simplified forms. They decode shorthand data into complete information. For instance, a lookup table can convert a country’s abbreviation into its full name.

In SAS Intelligent Decisioning, you can access simple lookup key-value lists and more complex advanced lists. Advanced lists can handle multiple keys and values and are stored in low-latency data libraries, managed through user-friendly, no-code interfaces.

Decision scientists can easily retrieve values from this lookup and advanced lists within rule sets. They can use built-in functions like LOOKUP and LOOKUPVALUE for simple lookups or the advanced list reference definition wizard for more complex queries. This makes the process efficient and keeps the data organized and accessible.

8. Segmentation tree

A segmentation tree is a decision-making map that helps you navigate various options to reach an outcome. It starts with a root node at the top and branches into different paths, ending in leaf nodes called ‘outcomes.’ Each branch in the tree represents a specific set of criteria—an individual variable, a combination of variables, or a Boolean expression. As you move through the tree, the requirements on each branch determine which path is taken until you reach the outcome.

9. Rule matrix

A rule matrix is a table where each cell identifies a different combination of two other variables’ values. Both variables used to build the matrix must be associated with a list of allowable values. The values in the lists are used to determine the rows and columns in the matrix. For each cell in the table, you specify a result string label. When you use the rule matrix for a segmentation tree, a branch is created for each unique result string, including another result.

10. Decision support

Decision support refers to tools that use AI to help humans make decisions and solve problems more effectively. Often called decision support systems (DSS), these tools can analyze large amounts of data using models, documents, or communication channels. By doing the heavy lifting with data, a DSS saves time and boosts confidence when making decisions. Whether facing complex challenges or just needing to streamline routine decisions, a decision support system can be a valuable ally in making informed, data-driven choices.

You now have a solid foundational understanding of decision intelligence. You can also learn more by exploring our decision-making resources, including our Intelligent Decisioning website and SAS Decision Builder, as well as our upcoming integration with Microsoft Fabric.

Dive deeper

• SAS Intelligent Decisioning
• SAS Decision Builder on Microsoft Fabric (in private preview)

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