Decision Intelligence

What Is Decision Intelligence?

Decision intelligence is the process of applying analytics, AI and automation to decisions that impact an organization’s performance at the executive, line-of-business, or individual level.

You can use decision intelligence automation to:

• Identify opportunities for growth
• Improve customer experiences
• Provide personalized financial offers
• Optimize supply chain operations
• Increase local and regional services

By automating the data analysis process and removing the guesswork from decision-making, decision intelligence empowers you to make data-driven decisions that lead to improved business outcomes.

Using Decision Intelligence in the Decision-Making Process

While decision intelligence informs the business decision-making process, it’s still up to you to determine how to use the information you receive from it. The two main routes your team could take with decision intelligence include:

1. Using the information to determine the best decisions to make
2. Allowing AI to automatically trigger actions based on the results

As part of the decision intelligence process, your team could use machine learning and AI to improve the accuracy and the level of detail included in the reports and dashboards you use to inform stakeholders.

From there advanced analytics could be applied to the insights you glean from those reports and dashboards. AI can surface the outliers and the anomalies in your data to help you determine which factors could potentially lead to increased growth and decreased cost.

For example, you could use decision intelligence to analyze a mix of customer, location, and sales data to determine different pricing and product placement options. The information would indicate which prices might work best for individual locations and include provide a range of projected sales increases.

Then, based on the processes you’ve set up (decisions made by people or decisions triggered by AI), you could determine the new prices for the products or allow AI to do it automatically.

Through the process of decision intelligence, you can use insights to identify potential business opportunities and spend more time discussing the actions you want to take.

When used in decision-making processes, decision intelligence leads to improvements in operational efficiency while having strong potential for top-line revenue growth and bottom-line returns.

The Difference Between Business Intelligence and Decision Intelligence

The main difference between business intelligence and decision intelligence is the information that each provides. While business intelligence involves the process of presenting information in visual formats, such as reports and dashboards, decision intelligence provides additional information you can use to solve business problems.

Whereas business intelligence might tell you how much sales went up or went down over the last year, decision intelligence would provide further explanation to help you solve business problems.

Perhaps total sales went up because one of your campaigns outperformed expectations. Or maybe your total sales went up even though sales were down across 70 percent of locations.

With the latter scenario, if you were to only look at the results the business intelligence provided, you might continue operating business as usual—and then be shocked the next year when performance dropped or wasn’t as high.

With decision intelligence, you would be able to investigate why sales were down across most of your locations and take actions to improve it, including investigating what made the other locations so successful. You could then share this information with other decision makers.

Decision intelligence provides a deeper understanding behind the performance of your business, and it’s because of this that it leads to increased performance.

Other examples of decision intelligence improving business decisions include:

General Business Functions

• Customer Segmentation: Classifying customers based on demographic, behavioral, and transactional data to determine which products and services to offer them
• Churn Prediction: Using historical customer data to predict which customers are likely to stop doing business with you
• Marketing Campaign Optimization: Analyzing customer data to determine the most effective marketing campaigns to target specific customer segments

Finance

• Credit Scoring: Evaluating a loan applicant’s creditworthiness based on their financial history and current financial status to provide you with recommended actions
• Fraud Detection: Identifying potentially fraudulent transactions based on pattern recognition and machine learning algorithms
• Portfolio Optimization: Determining the optimal investment portfolio for a financial institution based on market trends, risk tolerance, and investment goals

Supply Chain

• Demand Forecasting: Predicting future demand for a product based on historical sales data, economic indicators, and customer trends
• Inventory Management: Optimizing inventory levels to ensure that products are available when customers want them while minimizing waste and obsolescence
• Route Optimization: Finding the most efficient routes for delivery trucks to take based on traffic, delivery time windows, and fuel costs

Public Sector

• Social Service Allocation: Predicting which communities are most in need of social services and allocating resources accordingly
• Healthcare Resource Planning: Forecasting future demand for healthcare services to ensure that hospitals and clinics have the resources they need to meet patient needs
• Emergency Response Planning: Predicting the impact of natural disasters and other emergencies on a community and planning resources accordingly to respond to those impacts

Benefits of Decision Intelligence

While the primary outcome of decision intelligence is to improve decision-making, using it can improve other aspects of your business. Some use cases include:

• Improved Accuracy: The use of multiple data sources, data science, and algorithms delivers information that helps you make accurate and confident decisions, which also reduces the risk and potential bias anyone might introduce during the decision-making process
• Increased Speed: When you automate business processes, it can help you make decisions much faster than traditional methods, which is always crucial to staying ahead of your competition
• Better Data-Driven Insights: When you have data and confidence scores with predicted outcomes, you can shift your decision-making away from gut- or experience-based decisions and focus on the data (which might go against what your experience tells you)
• Enhanced Customer Experience: Businesses depend on their customers and the businesses that make smarter decisions can provide better customer experiences, starting with offering personalized offers and products
• Increased Cost Savings: More efficient shipping routes, accurate materials projects, and faster-go-to-market launches all mean streamlined processes, increased cost savings, and more revenue
• Digital Transformation: The need for centralized data sets and assets needed to carry out analytic workflows and support decision models naturally leads to the digitization and democratization of data sources, data warehouses, data lakes, and analytics

Conclusion

Decision intelligence can help you improve business outcomes by creating a data-driven decision-making process. The use of machine learning, AI, and data science provide insights to help you understand past performance and the actions you can take to improve future outcomes.