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Prescriptive Vs Predictive Analytics – Yes, there is a Difference

For manufacturers, the digital economy has turned up the heat for investments into data-driven business outcomes. At the heart of this initiative is the growing importance of analytics that empowers the workforce and decision-makers with knowledge on leading the way forward.

It is estimated that the global market size for analytics in the manufacturing industry will be worth over USD 28 billionby 2026.

However, for manufacturers, the biggest dilemma is not often on deciding the importance of analytics, but on choosing between different kinds of analytical approaches. One of the most common debates in the industry is to pick a winner in the predictive vs prescriptive analytics battle.

To make it clearer, let us have a closer look at the key differences between the two based on four major attributes.

Direction of Offering

The first major difference between the two forms of analytics lies in the strategic direction that both offer to companies that leverage either of them.

In the case of predictive analytics, companies gain insights into identifying possible future turn of events based on AI and machine learning-enabled processing of historical and real-time data. In simple terms, predictive analytics deals in pointing out what will happen for enterprises based on how they run their business.

Prescriptive analytics is more focused on the outcomes i.e., it leverages intelligent statistical modeling to process real-time and historic data points to offer a strategic direction that enables enterprises to plan on how to make outcomes happen in due course of time. In other words, prescriptive analytics suggests a course of action for a business that it needs to take to achieve a milestone or objective.

Risk Exposure

Predictive analytics is prone to more risky outcomes because of its inability to factor in uncertainties. As the market and consumer philosophies evolve over time, there may be events that trigger data points that need an updated processing model to be leveraged by the predictive analytics system for best results. In the absence of the update, the risk elements are very high. Some of the biggest examples cited in this context would be the subprime mortgage crisis in the latter half of the 2000s, which ultimately paved way for the 2008 Global Financial Recession.

Prescriptive analytics, on the other hand, can factor in uncertainties while modeling scenarios. With the right data points collected from scenes of uncertainty, companies can leverage prescriptive analytics to quickly find ways to respond and mitigate challenging market conditions.

Scope of Coverage

Predictive analytics often restricts its impact zone in areas where short-term trends have a bigger say. These trends can work independently from other broader enterprise trends. Hence, when we consider the scope of coverage, predictive analytics helps manufacturers in areas such as demand planning, inventory management, maintenance schedules, etc.

Prescriptive analytics, however, deals with the entire spectrum of micro and macro-level organizational trends and behavioral data to provide a more strategic direction of action for companies. It can be used in scenarios where a critical business objective is to be achieved and the steps that must be taken can be determined through prescriptive analytics.

If we were to cite a use-case, then identifying an optimal production or manufacturing schedule to achieve a desired sales or revenue target would be an example. Prescriptive analytics can throw light on specifics that can help achieve success faster. For example, an automobile manufacturer can leverage prescriptive analytics to identify which vehicle model has to be produced or in which geographic region do they need to invest more in marketing to reach overall company sales and revenue targets faster.

Cost of Operations

Prescriptive analytics operates at a much larger scale as evident from the above examples. This ultimately makes them more expensive given the wider range of insights that it can offer in the form of strategic directions.

Predictive analytics works on a shorter and relatively smaller set of data parameters for analyzing risks and discovering benefits in a shorter term. Ultimately, it boils down to the core requirement of the manufacturer for which business analytics was considered as an investment. If they want to have a comprehensive end-to-end capability of finding ways to run their business on a pure data-driven model, then prescriptive analytics is the ideal choice. If the manufacturing business is more lenient on being run autonomously with visibility into short-term risks and opportunities, then predictive analytics is a worthy investment option.

For manufacturers, it is important to have a deep understanding of the capabilities of either of these business analytics mechanisms. Though, they have their differences, both prescriptive and predictive analytics handle their own unique range of functions and help companies better their business proposition. It is very important for them to have the right strategic guidance in implementing an analytics infrastructure to support their growth ambitions.

Get in touch with us to explore the world of analytics for your business and eliminate doubts about exploring new frontiers of innovation through prescriptive and predictive forms of analytics.

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