Decision intelligence (DI) combines data science with different scientific theories to help people make the best possible decisions. It aims to provide actionable insights by translating raw data into formats that decision-makers can easily understand.

DI aims to identify trends and predict the outcomes of different decision options. A typical example of DI is a recommendation engine. When you go to Amazon, you will likely see a list of suggested products the vendor derived after analyzing your past purchases, viewed products, and search history.

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What Is the Purpose of Decision Intelligence?

The primary purpose of DI is to improve the quality of the decision-making process in data-rich environments. In today’s data-driven world, we are often bombarded with too much information that could lead to decision paralysis. DI helps individuals and organizations make quick and better decisions.

DI combines concepts and methodologies from various fields, including data science, artificial intelligence (AI), behavioral science, social science, and management science, to understand decision-related situations and create models.

What Is the Importance of Decision Intelligence?

Businesses have vast volumes of data that can only be useful if they glean insights from them. As such, DI can help organizations effectively and efficiently take advantage of today’s data-driven environments. It helps businesses:

benefits of Decision Intelligence
  • Handle complex decisions: DI considers various factors, dependencies, and potential outcomes, helping organizations make well-informed decisions. That is beneficial since organizations often face decisions that have significant impacts.
  • Interpret data accurately: DI provides methodologies to transform raw data into meaningful insights, ensuring more accurate interpretation than manual analysis.
  • Reduce risks in making wrong decisions: DI helps mitigate risk and uncertainty by predicting potential outcomes and their impacts. It allows decision-makers to choose the path most likely to lead to a successful outcome while avoiding options that may carry unnecessary risks.
  • Enhance operational efficiency: DI can streamline operations by automating data analysis and decision-making processes, increasing efficiency and productivity.
  • Make ethical decisions: DI includes behavioral and social science elements, helping businesses make ethically and socially conscious decisions.

What Are Examples of Decision Intelligence?

Here are some examples illustrating the application of DI in various fields.

  • Climate change mitigation: DI can help policymakers make informed decisions about climate change mitigation strategies. They could use models to predict the impact of different policies on greenhouse gas emissions, economic growth, and social factors and then choose the policy that best meets their objectives.
  • Financial services: Banks can apply DI to assess the risk of lending to a particular individual or business. They can make more informed lending decisions by analyzing data, such as credit histories, incomes, and market trends.
  • Healthcare: DI can improve patient care. For example, hospitals can use AI algorithms to predict patient deterioration based on health data, helping doctors make more informed decisions about care and treatment.
  • Human resources (HR): HR departments can use DI to help with hiring, promotion, and retention. By analyzing employee data, they can identify the factors that predict success in a role or determine which benefits most effectively boost employee satisfaction and retention.
  • Retail and e-commerce: A retail business can use DI to optimize pricing and inventory management. For instance, machine learning (ML) models can predict the popularity of various products based on historical sales data, current trends, and seasonal factors, helping a retailer decide which items to stock up on.
  • Supply chain management (SCM): Companies can use DI to optimize their supply chain operations. They could predict potential bottlenecks or disruptions, evaluate alternative suppliers or routes, and make decisions that minimize costs while ensuring timely delivery.

In these examples, DI combines data analysis, predictive modeling, and consideration of human factors to help individuals or organizations make better decisions.

What Is the Difference between Business Intelligence and Decision Intelligence?

Business intelligence (BI) and DI are critical components of an organization’s data strategy, but they have distinct characteristics and different purposes.

Decision Intelligence vs business intelligence

BI primarily focuses on historical data analysis to provide descriptive insights. It uses tools and software to transform raw data into meaningful information, reports, and dashboards that reflect past and present business performance. It answers questions like:

  • What happened?
  • When did it happen?
  • Where did the problem occur?
  • What actions are needed based on past performance?

On the other hand, DI is a broader, more forward-looking field. It combines data analysis with other disciplines to model, analyze, and improve decision-making.

DI is concerned with predicting future outcomes (predictive analytics), understanding why those outcomes would happen (prescriptive analytics), and providing insights for making more effective decisions. It answers questions like:

  • What will happen in the future?
  • Why will it happen?
  • What should we do about it?
  • What is the impact of this decision?

With Decision Intelligence, businesses can take advantage of all the data they have. They can make informed decisions and even get a glimpse of what could happen if they choose certain paths. 

Key Takeaways

  • DI is a multidisciplinary field that improves decision-making by translating raw data into actionable insights using methods from data science, AI, behavioral science, and management science.
  • DI has practical applications in various fields, such as climate change mitigation, financial services, healthcare, HR, retail and e-commerce, and SCM.
  • DI helps organizations handle complex decisions, accurately interpret data, reduce risks, enhance operational efficiency, and make ethically responsible decisions.
  • DI is different from BI.