How to establish a decision intelligence framework

Decision intelligence (DI) is reshaping the way modern organizations act on data insights.

When your organization has implemented DI, you’ll be able to operationalize investments in business intelligence (BI) and/or artificial intelligence (AI), make more informed business decisions, and know which types of business decisions are likely to make the best impact. 

When your organization implements DI, you’ll understand exactly how decisions are made within your company, how to evaluate the outcomes, and how to use those results to manage and improve your decision framework. 

You’ll also gain insight into not just what your data is telling you, but what questions you can ask to find answers and make decisions that will reshape the future of your company.

Shifting your organizational mindset toward decision intelligence

According to Gartner, by 2023 more than 33% of large organizations will have analysts practicing DI. How can your organization join the revolution?

A good place to start is by communicating the “why.” You can’t make every C-suite leader or organizational stakeholder an expert in advanced, non-deterministic techniques or complex data science methods, but you can help them see how your data—combined with a DI model and AI—will help them arrive at more impactful decisions.

In the past, data was first priority. Tools were chosen and queries built to fit around existing data.

But with DI, the decision being sought takes first priority. Decision-makers first determine what questions need to be answered. They look at what they want for the future of the company and start asking the questions about how they’re going to get there. Then they find out what data is needed to support the decision and evaluate the impact it will have across the organization.

With DI, the “data takes a supporting role rather than the starring role when making data-driven decisions,” PCMag.com’s Pam Baker wrote in April 2021.

As you support this mindset shift, create a DI framework that includes the following so it can be leveraged across the organization:

• Decision research. As Gartner points out, a critical component of DI involves helping data and analytics leaders “design, compose, model, align, execute, monitor and tune decision models and processes” in the context of business outcomes and behavior.
• Learning from decisions. Gather feedback—both automated and from stakeholders—on the impact of models and decisions. Use this data to learn from and improve upon the outcomes of decisions.
• Understanding how decisions will be made. Will they be primarily outcome-driven, where business leaders will evaluate results first and then how data supports those results? Or will they be data- and process-driven, where IT and data leaders consider the best ways to use technology tools to visualize and find insights to support decisions with data?

Implementing decision intelligence into your workflow

Here are some examples of how organizations can implement a DI framework to support various teams in their decision-making processes:

• Customer satisfaction. With DI, you can identify patterns in customer sentiment with Natural Language Processing (NLP) and machine learning (ML) modeling. Use these tools to accurately predict positive and negative customer experiences in advance so you can address risk in time. DI powered by ML can help predict unsatisfied customers, anticipate their needs, and create opportunities to turn these customers into lifetime value members.
• Marketing attribution. DI enables you to understand which channels are most likely to perform via proactive, data science machine learning (DSML) modeling. Integrate web analytics data with CRM campaigns, Facebook, Google, social, and programmatic data to understand which channel drove the most conversions on your site through a data science pipeline you can model and deploy into production.
• Logistics optimization. Build systems to identify on-time shipment arrival risk proactively. Further, you can leverage model insights to build prescriptive optimization systems in order to make improvements across the entire chain.
• Inventory optimization. Leverage SKU-level demand forecasting in order to make accurate inventory planning across the entire supply chain. Identify likely outages and overages ahead of time in order for proper adjustments and action.
• Fraud prevention. Sophisticated detection systems identify transactions that pose risk. Leverage prescriptive early warning systems in order to remediate fraud risk ahead of time.

Read the third and final blog post in this series on decision intelligence to learn more about how you can make DI work for your organization.