Q 719. What is Decision Intelligence (DI)? Which of the four types of analytics - Descriptive, Diagnostic, Predictive and Prescriptive contributes the most to DI? Support your answers with suitable examples. 

 

 

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Decision Intelligence (DI) integrates data, social, and managerial sciences to improve decision-making by transforming data insights into actionable strategies

Types of Analytics in DI: Descriptive, diagnostic, predictive, and prescriptive analytics each play unique roles in DI, from summarizing historical data to providing actionable recommendations

1. Descriptive Analytics: This analytics type focuses on summarizing historical data to describe what has happened. While it provides valuable insights, it stops short of explaining why certain events occurred or forecasting future outcomes. Example: A report that shows last quarter's sales figures across different regions.
2. Diagnostic Analytics: This approach digs deeper to investigate the reasons behind past outcomes. It identifies correlations and causations to understand why events occurred. Example: An analysis that examines the reasons for a sudden drop in sales, considering factors like customer feedback, market trends, or changes in competitor strategies.
3. Predictive Analytics: Predictive analytics uses historical data and statistical models to forecast future trends or events. Although it provides foresight, it doesn't advise on the steps to take. Example: Using historical sales data to predict next quarter's revenue based on trends and patterns.
4. Prescriptive Analytics: This advanced form of analytics not only predicts future outcomes but also recommends actions to achieve desired results. It evaluates various possible scenarios and provides advice on the best course of actions. Example: A retailer deploying a model that forecasts inventory needs and simultaneously suggests optimal restocking strategies to minimize costs and prevent stock outs.

Of these, Prescriptive Analytics is most integral to Decision Intelligence. For example, a tourism company looking to maximize profit during the peak season might follow these steps:

1. Descriptive Analysis: Summarize past tourist inflow and revenue data to understand patterns.
2. Diagnostic Analysis: Investigate factors influencing tourist numbers, such as weather conditions, holidays, or marketing efforts.

Decision Intelligence (DI) refers to discipline which combines data science, artificial intelligence and managerial science to make data-driven and the most logical human like decision making. This requires study of advanced analytics, machine learning and use of some cognitive tools to make much more informed decisions.

 

If we consider 4 types of analytics: Descriptive, Diagnostics, Predictive and Prescriptive analytics then the Prescriptive Analytics contributes the most to Decision Intelligence.

 

Since I am from manufacturing industry, let me give the most populist example when it comes to 4 types of analytics:

 

"The Bearing Failure Example"

 

Descriptive Analytics (Reactive in Nature):  Here, we describe or summarize historical data to understand what has happened. In Industries, we generally create a metric like "Mean Time Between Failure (MTBF)" to create a baseline on this and try to increase that as high as possible through Kaizens to reduce bearing failures.

 

In modern days, we use tools like vibration testing or bearing temperature measurement with the help of thermostat. In descriptive analytics, we would measure what is average operating temperature & vibration frequency and what is standard deviation for the same.

 

That's descriptive analytics, we describe central tendency or variation of a metric to measure & try to improve upon that.

 

Diagnostics Analytics (Reactive in Nature):  Here, as the name suggests we diagnose on bearing failure either with Why-Why Analysis or Fishbone Diagram or we use data analytics like assign the root cause to anomalies or sudden spikes in temperature or vibration. With this, we can investigate possible root causes leading to the failure and take corrective and preventive actions (CAPA) in such a manner that the issuer never repeats.

 

With diagnostic analytics, we may reach to many probable root causes like inadequate lubrication, misalignment or overloaded bearing and take suitable CAPA actions on that.

 

Predictive Analytics (Proactive in Nature): Here, we try to predict bearing failure by applying machine learning algorithms on temperature or vibration with the help of which we can predict an impending failure and once we are confident on the regression model or ML algorithm, we schedule bearing replacement accordingly.

 

In industries, we use Maximo systems for CMMS (Computerized Maintenance Management System) by IBM in which we there is measurement of predictive parameters like temperature and vibration and there is auto maintenance work order created based on alert set on temperature and vibration triggers. 

 

Prescriptive Analytics (Proactive in Nature): Just like doctors prescribe the medicines, these analytics expect systems not only to predict but also prescribe best course of action.

 

Like in the bearing example, based on temperature and vibration predictions, prescriptive analytics can recommend the actions like optimized lubrication quantity and schedule, reduction in load or schedule on when to replace the bearing. 

 

Thus, prescriptive analytics is highly correlated with Decision Intelligence (DI).

 

 

Decision Intelligence is a discipline which combines the data science and managerial science to improve the decision-making process. It utilizes advance analytics/ML/AI to provide a framework for data-driven decisions. All four types of analytics do contribute to Decision Intelligence, but Prescriptive Analytics plays the most significant role. As prescriptive analytics not only analyzes past data and predicts future outcomes, provides actionable recommendations to achieve desired results. Decision Intelligence integrates insights from descriptive analytics to capture data and examine what happened from past, it integrates diagnostic analytics to examine why something happened in the past to gain insight on the causes, it integrates predictive analytics to predict likely outcomes and guide decision-makers towards the best possible actions.

 

For example, in my current organization (Pharma), a product was created which pulls data from our customer data hub and uses different analytical models to generate actionable recommendations, tailored to different key roles and designed to help them decide on the best actions to take. This helped teams like sales representatives and brand managers leading to higher impact discussions with Health Care Professionals and increased commercial performance.

Type of Analytics	Description	Role in Decision Intelligence	Example in U.S. Health Insurance & Medical Billing
Descriptive Analytics	Analyses historical data to understand past events.	Provides context but lacks actionable insights.	Tracks claims, appeals and volume trends, customer demographics, and common claim rejection reasons. Helps insurers identify historical patterns, but doesn’t directly drive decisions.
Diagnostic Analytics	Explores reasons behind past outcomes.	Helps identify root causes but is limited in proactive guidance.	Analyses reasons for claim rejections (e.g., missing data, billing code errors, Medical necessity, Out of network hospitals, Experimental treatment, Coverage exclusions) to reduce denial rates. Gives insights into process issues but doesn’t offer action plans.
Predictive Analytics	Uses historical data to forecast future.	Helps in anticipating future outcomes but lacks recommendations on actions.	Predicts claims and Appeals likely to be denied, allowing proactive error-checking. Forecasts high-cost claims  and Performance Guarantee fixed TAT for resource allocation, but doesn't directly suggest how to handle the predictions.
Prescriptive Analytics	Recommends actions based on predictive insights.	Directly helps in Decision Intelligence by providing actionable steps for desired outcomes.	Recommends policy/process improvements to reduce denial rates, such as automated error-checking for commonly rejected claims. Suggests optimal billing codes to reduce processing time and improve revenue cycle management.

Decision Intelligence in Action

• Example: In health insurance domain , a DI framework might combine predictive analytics (identifying high-risk claims and appeals, specific scenarios state specific) with prescriptive analytics (suggesting error-checking steps) to enhance decision-making by reducing claim delays and improving accuracy.

Overall, Prescriptive Analytics plays the most crucial role in DI, as it not only anticipates future scenarios but also provides actionable guidance which is programmed from Machine learning, directly impacting the quality and speed of decision-making.

Edited November 12, 20241 yr by Puneet Vohra
Supplemental information added

Decision intelligence is an analytical framework that combines the core processes such as data science, social science and decision theory. The objective of the DI framework is to improve the decision making processes by utilizing the data insights in decision making stages. DI framework can understand what is currently happening in the processes and also align on the next step or informed actions.
As mentioned in the question there four types of analytics in DI framework, namely Descriptive, Diagnostic, Predictive, and Prescriptive. However, Prescriptive analytics contributes the most to DI because it directly supports actionable decision-making. Prescriptive analytics helps in identifying the specific actions based on the data available and the insights generated. It not only identifies delivery outcomes, but also provides the next actions to make an informed decision. By delivering actionable recommendations, prescriptive analytics empowers decision-makers to act on insights with confidence.
In healthcare, prescriptive analytics can suggest treatment plans for patients based on their unique medical history, providing personalized care recommendations that integrate medical expertise and patient data.

Decision Intelligence (DI) is a field that combines analytics artificial, intelligence and automation to improve decision making across organization. This aims to take decision process more effective by providing data integrity, AI & machine learning models, focusing human understanding, optimization to the cause of action. This is used in many industries such as manufacturing, public sector, retail and etc.

 

Descriptive analytics focus on what has happened in the past.

•              We can find the trend such as peak months of the last years.

 

Eg: November and December sales were x% higher than in other months.

 

This helps to identify the higher sales in the w inter months due to the holiday season but it doesn’t predict what will happen next.

 

Diagnostic analytics focus on past data and helps to understand why something happens.

•              Company can go deeper into why those trends occurred.

 

Eg: there was a sales peak in last year December due to increase of marketing campaigns and promotions.

 

This helps the company to understand the reasons behind the successful sales but it doesn’t give insight about the future.

 

Predictive analytics forecast future outcomes by using historical data.

·         Company can forecast what will happen in upcoming months

 

Eg: Based on the historical trends the company predict that the sales will increase by X% in December this year

 

For the decision intelligence this will provide the deeper insight since it helps company to forecast future outcomes and plan accordingly.

 

Prescriptive analytics providing the recommendations based on the predictive data.  

·         Company can decide the best action for future.

 

Eg: To achieve predicted X% of sales increase we recommend ramping up inventory for the most popular items start campaign in end of October increase staff at stoles and etc.

 

This doesn’t predict what will happen but provide the best recommendations based on the prediction.

 

When comparing the above types of analytics 'predictive analytics' contributes the most to the decision intelligence since it provides what will happen in future (eg: it will provide the forecast of future sales). Therefore, this helps the company to data driven decisions such as budgeting planning and etc.

While everyone has been spot-on saying that Prescriptive Analytics plays the most important role, Sanuja has provided the best answer with valid examples for each analytics approach. What tipped the scales in her favour was the way she took each explanation a step further, presenting each example with a smooth and cohesive flow.