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Decision Intelligence: Using Data to Drive Better Outcomes

Are you looking to enhance decision-making processes in your organization? Decision intelligence can help you achieve this goal by providing a structured way of combining data, analytics, and technology to make better decisions. Not only that, but it also helps identify new opportunities for growth and transformation in the most cost-effective manner possible. 

In this post, we will discuss what decision intelligence is all about, the key benefits it can provide, how to go about implementing it into your own company, and successful real-life examples of its usage. By familiarizing yourself with these topics, you will be able to take advantage of the tremendous power offered by decision intelligence for unlocking value from within your business ecosystem.

What is decision intelligence?

Decision intelligence is a process that involves the use of data, analytics, and artificial intelligence to optimize decision-making. It encompasses a range of techniques including machine learning, natural language processing, and predictive analytics. The goal is to help organizations make better decisions by providing them with insights that can inform their data-driven decision-making processes.

At its core, decision intelligence works using data and analytics to evaluate different scenarios, weigh their potential outcomes, and select the most appropriate option. It is about making decisions based on data, rather than relying on intuition, past experiences, or gut feelings.

Top benefits of decision intelligence

As the business landscape becomes increasingly complex, decision-making has become a critical component of success. Here are some of the benefits you can gain by employing decision intelligence in your business.

Improved decision-making

By applying analytics during times of decision making, executives can better identify and evaluate different scenarios, weigh their potential outcomes, and select the most appropriate option. 

Reduced risks and uncertainty

While risks and uncertainty are ever-present, decision intelligence can help organizations identify potential risks, investigate possible outcomes, and develop mitigation strategies

Increased efficiency

By providing leaders with the information they need at the moment of impact, decision intelligence can lead to faster decision-making and a more agile organization overall.

Enhanced collaboration

Because decision intelligence provides a single source of truth for data and analytics, it can assist in aligning stakeholders and improving communication by ensuring that everyone is working toward the same goals. 

How to get started with decision intelligence in your organization

1. Define your decision-making criteria

This step involves identifying the factors in the decision-making process that are most important and determining how you will evaluate these factors. Defining the criteria upfront helps to avoid confusion later on. You can also use this as an opportunity to get buy-in from all stakeholders.

2. Gather data and analyze

The next step is to gather data that is relevant to the decision being made. This can include both quantitative and qualitative data from both internal and external sources. Before we move onto the next step, let’s refresh our memories on the differences between quantitative and qualitative data:

Quantitative data

Quantitative data is numerical data that can be measured and analyzed statistically. This can include data such as sales figures, customer demographics, and website traffic. You can collect this data from a variety of sources, such as CRM systems and web analytics tools.

Qualitative data

On the other hand, qualitative data is non-numerical. It provides insights into customer behavior, attitudes, and opinions. This data might include customer feedback, social media sentiment, and focus group results. You can collect qualitative data through surveys, interviews, and other forms of market research.

3. Identify patterns and insights

Once the data has been collected, it needs to be analyzed to identify patterns and insights. This process involves the use of tools such as data visualization and statistical analysis to identify trends and correlations in the data. By identifying patterns and insights, organizations can gain a better understanding of their customers and the factors that influence their behaviors.

4. Evaluate options and make a decision

Finally, you can use the identified patterns and insights identified to evaluate different options and make a decision. By using data-driven insights to inform the decision-making process, organizations like yours can feel confident that their decisions are more likely to lead to positive outcomes.

Examples of decision intelligence in action

The practice of applying data to the decision-making process has become increasingly prevalent across a range of industries. Let's explore some examples of how decision intelligence is being used in key industries:


In the healthcare industry, decision intelligence is helping medical professionals make more accurate diagnoses and develop more effective treatment plans. 

For example, researchers are using machine learning algorithms to analyze medical images, such as X-rays and MRIs, to identify early signs of disease that may be difficult for the human eye to detect. Decision intelligence is also being used to personalize treatment plans based on a patient's genetic makeup and medical history, improving patient outcomes, and reducing healthcare costs.

In the case of MDaudit, they use decision intelligence to help healthcare organizations improve revenue, mitigate risk, and streamline operations. While the relationship between data and healthcare has sometimes felt like oil and water, decision intelligence solutions are able to bridge the gap to apply BI in healthcare. As CEO, Ritesh Ramesh said: 

“If you’re a healthcare compliance or a revenue integrity professional, data is not the first thing on your mind. You’re not trained to be a data analyst. So we need to provide people with the right tools to do their job better.”

-Ritesh Ramesh, CEO at MDaudit


In the finance industry, decision intelligence is being used to improve risk management, fraud detection, and investment strategies. Machine learning algorithms can analyze vast amounts of financial data to identify potential risks and opportunities, enabling financial institutions to make more informed decisions. 

Decision intelligence is also being used to develop more accurate credit risk models, reducing the risk of default and improving lending practices. Let’s consider Loan Market Group, Australia's biggest loan aggregator. By revamping the reporting experience in their SaaS product, MyCRM, they were able to empower more finance professionals to make data-driven decisions, and better serve their clients.


In the retail industry, decision intelligence is helping companies optimize inventory, pricing strategies, and customer engagement. By analyzing customer data, such as purchase history and online behavior, retailers can personalize their marketing efforts and improve customer loyalty. 

Decision intelligence is also being used to optimize supply chain management, ensuring that products are in stock and available for purchase when customers need them. That was the case for T-Mobile Netherlands, who was able to sustain their leadership in the market by decreasing time to insight for critical decision making. 

“Using ThoughtSpot during our company’s sale we estimated saved us between 10 to 15 days of work—which helped us get the M&A done as rapidly and successfully as it was.”

-Herman Geerts, Product Owner B2B and B2C Value Streams for DI, T-Mobile Netherlands


In the manufacturing industry, decision intelligence is being used to improve quality control, reduce downtime, and optimize production processes. Machine learning algorithms can analyze data from sensors and other sources to detect potential issues before they become major problems, minimizing downtime and reducing costs. 

Decision intelligence is also being used to optimize production schedules and improve resource allocation, ensuring that manufacturing processes are as efficient and effective as possible. That’s the case for Fabuwood, who was able to bring their siloed data together to reduce data-fueled frustrations across the entire organization.

“Before, people would make decisions and then use data to back it up. Today, with ThoughtSpot, people are relying on data to make decisions because they actually have access to the data. Trust and access play a huge role here.”

-David Samet, Data team Manager at Fabuwood

Make more informed decisions with data-driven insights

Decision intelligence is an integral part of modern business practice. It provides meaningful insights on how to streamline and improve operations, as well as economic returns. When properly implemented, decision intelligence can help a business make well-informed decisions faster than ever before. 

By leveraging ThoughtSpot’s AI Analytics, your organization can harness a powerful search engine to make data-driven decisions. And because ThoughtSpot is built for businesses of all sizes across all industries, anyone can access their data to make decisions in real time. 

If you’re looking for a way to leverage your data and take your business strategy to the next level, join the data leaders mentioned above and start a ThoughtSpot free trial.