Key differences between predictive analytics and prescriptive analytics

Key differences between predictive analytics and prescriptive analytics

Data analytics is one of the frontier technologies, that is important for businesses to survive and succeed in the big data era. The three main types of data analytics are: descriptive, predictive, and prescriptive. In this article, we are going to understand the fine line differences between predictive and prescriptive analytics and how the two are not the same.

What is predictive analytics?

Predictive analytics is an amalgamation of various statistical techniques such as data mining, predictive modeling, and machine learning, using which companies can identify optimal strategies for business growth and sustainability, recognize potential markets, pinpoint profitable insurance products, etc. Predictive analytics provides businesses an indication of what will happen.

What is prescriptive analytics?

Prescriptive analytics is the last phase of business data analytics and shows the best way for companies to reach their goal. It includes both descriptive and predictive analytics. The various analytics techniques involved in prescriptive analytics are machine learning, regression techniques (linear, time-based, and logistic regression), neural networks, and Naïve Bayes conditional probability. As per Gartner’s report, 11% of medium and large-sized businesses are already using prescriptive analytics

Understanding the key differences between the two

While both predictive and prescriptive analytics operate on collected data, the former helps businesses to forecast possible and potential future outcomes, while the latter recommends necessary actions to be taken in order to achieve a desired business goal. In other words, predictive analytics is reactive as they help businesses in identifying potential problems and challenges, whereas prescriptive analytics is proactive as it not only determines the outcomes but also provides the best solutions to address the outcomes.

For instance, predictive analytics can predict an organization’s sales performance but cannot measure the impact on sales and profitability due to an increase in the price of raw materials. On the contrary, since prescriptive analytics takes into account all the inputs, processes, and outputs and it is capable of recommending the best way forward to maximize overall returns and profitability.

Predictive analytics provides short-term metrics and measures metrics in isolation. Though prescriptive analytics builds on predictive analytics, its results outweigh those from predictive analytics. Most importantly, both predictive and prescriptive analytics is necessary to improve business outcomes and make better decisions.

Though prescriptive analytics enables businesses to explore various what-if scenarios, secondary options, and trade-offs, its solutions are expensive than predictive solutions. However, prescriptive analytics is worth the investment as the ROI of prescriptive analytics is far greater than that of predictive analytics. In fact, according to Gartner’s estimates, the prescriptive analytics software market is expected to grow at a CAGR of 20.6% between 2017 and 2022.

Tips to make the most of your analytics program

Applications of data analytics range from expediting market research to optimizing and improving customer experience. Following are a few tips to take advantage of data analytics:

  • Take baby steps: Businesses need to brainstorm on various aspects, so they can benefit fully from data analytics and not miss out on their actionable insights. However, to attain the big goal its always wise to start small. Also, while dealing with big data, there is a higher possibility of companies overlooking minor details, which actually could be a potential solution. Hence, think big with an overarching analytics strategy, but start small with tactical steps. Also, small wins will boost confidence in long-term analytics projects.

  • Understand the logic behind prescriptive suggestions: Businesses need statistical insights to thrive in the information age. Therefore, companies need to look at the big picture and understand the reason behind prescriptive recommendations.

  • Update systems regularly: As businesses evolve, so should their algorithms. Also, the output of analytics programs is as good as the input data. Hence, it is necessary for companies to continuously update both predictive and prescriptive analytics with the latest data, so they can trust their data-driven decisions.

Summing Up

Predictive and prescriptive analytics are two sophisticated tools that business leaders and executives greatly look forward to as they are stepping into the data-driven decade. The growing importance of both the predictive and prescriptive analytics tools could be clearly sensed from the following prediction -“the global market for predictive and prescriptive analytics is estimated to reach nearly $28.7 billion between 2017 and 2026 (riverlogic)”. Though predictive analytics is inferior to prescriptive analytics, each of them is important and has a role to play in enabling businesses to grow and achieve their goals.

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