BI – Management science

“Effective business intelligence management is about equipping decision-makers with the tools and insights they need to make informed, data-driven decisions that drive business success.”

– Shree Shambav

Descriptive, Predictive, Prescriptive : Business Analytics with Management Science

The purpose of any analytical solution is to deliver actionable insights to the organisation so that it can make better decisions faster and with better results. Different analytics provide different insights, so it’s important to know what each type of analytics provides and how to match analytics functions to the organization’s operational capabilities in areas like banking, insurance, utility, hospitality, healthcare, real estate, facilities, and asset management.

Business Intelligence solutions are of four types:

  • Standard Reports – What happened?
  • Ad-hoc Reports – How many, How often, Where?
  • Query Drilldown (OLAP) – Where exactly is the problem?
  • Alerts – What actions are needed?

Business Analytical solutions are of four types:

  • Statistical Analysis – why is this happening?
  • Forecasting – What if these trends continue?
  • Predictive Modeling – What will happen next?
  • Optimization – What’s the best that can happen?

Descriptive Analytics: This type of analysis employs data aggregation and data mining tools to provide historical context and answer the question, “What happened?”

The reports that provide historical insights into the company’s production, financials, operations, sales, finance, inventories, total inventory on hand, average dollars spent per client, sales growth, and customers – this form of post-mortem research–are instances of descriptive analytics.

Predictive Analytics is a type of analytics that uses statistical models and forecasting methodologies, as well as rules and, on occasion, external data, to comprehend the future and answer the question, “What could happen?”

A credit score is something that most people are familiar with. These scores are used by financial services to predict customer behaviour when it comes to making future credit payments on time. Understanding how sales may close at the end of the year, predicting customer purchasing behaviour, or forecasting inventory levels based on a plethora of variables, Multichannel marketing effectiveness, Price Optimization, Healthcare better treatment, Insurance fraud, security, and so on.

Prescriptive Analytics: which use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?”

Prescriptive analytics predicts not only what will happen and when it will happen, but also why. Furthermore, prescriptive analytics suggests decision options for how to capitalise on a future opportunity or mitigate future risk, as well as the consequences of each decision option.

Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead and prescribe how to take advantage of this predicted future without compromising other priorities.

Larger companies are successfully using prescriptive analytics to optimise production, scheduling, and inventory in the supply chain to ensure that the right products; are delivered at the right time and that the customer experience is optimised.

Prescriptive analytics combine Disparate Technical discipline:

  • Applied statistics
  • Operations Research
  • Pattern recognition
  • Image processing
  • Speech recognition

Prescriptive analytics is applied to the following: pricing, inventory management, operational resource allocation, production planning, supply chain optimization, transportation and distribution planning, utility management, sales lead assignment, marketing mix optimization, application in Oil and Gas, application in Healthcare, application in Banking for unexpected bankruptcies, application in Planning for the Far Future, Fraud Analytics, and financial planning.

Prescriptive analytics, for example, is used by airline ticket pricing systems to sort through complex combinations of travel factors, demand levels, and purchase timing to present potential passengers with prices designed to maximise profits while not discouraging sales. Another well-known case study is UPS’s use of prescriptive analytics to optimise package delivery routes..

Although prescriptive analytics has a high potential for business impact, it can quickly become overwhelming and complex. As a result, it remains a largely untapped opportunity in the vast majority of organisations. According to the Gartner report, only 3% of surveyed companies; are currently using prescriptive analytics software, while 30% are actively using predictive analytics tools. However, with the continued explosion of data combined with vast technological advancements, prescriptive analytics adoption is expected to skyrocket in the coming years.

“All human beings have eternal life. No matter how strongly intellectuals may reject the idea, our souls are eternal; we are beings living in an eternal chain that consists of past, present and future.”

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