Using Data in Business: What Is Prescriptive Analytics?

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In today’s digital age, data is being created at an astronomical rate in diverse forms like emails, internet searches, social media likes, and video views. According to a World Economic Forum report, in 2017 the world was producing roughly 2.5 quintillion bytes of data each day. Businesses are taking advantage, using analytics to gain insights and drive decision-making, with predictive and prescriptive analytics often being used in combination. Where the former is utilized to learn when problems are likely to occur, the latter is relied upon to suggest actionable next steps. The central questions to ask are: “what is prescriptive analytics and how can it help an organization grow?” In fact, prescriptive analytics has various applications. For example, airlines use it to maximize profit by determining when ticket prices should be automatically adjusted based on weather, oil prices, and consumer demand. Prescriptive models are also being used by fire departments to determine which communities should be evacuated during a wildfire. Hospitals use it to predict which patients are predisposed to readmission so staff can take steps to prevent it.

Techniques Used in Prescriptive Analytics Modeling

Professionals who are interested in pursuing a career in prescriptive analytics should be proficient in various modeling techniques, including statistical and quantitative analysis, spreadsheet modeling, machine learning, and the use of algorithms.

Statistical and Quantitative Analysis

Statistical analysis involves collecting and scrutinizing data sets as a means to understand trends, such as customers’ past buying patterns that can predict what products are likely to be bought in the future. This analysis takes a “big picture” approach to identify past patterns and predict future trends.
Quantitative analysis can also be used to predict future outcomes. But instead of examining large data sets, this model delves deeper into why a specific situation or event occurred. Although both methods are valuable, analytics professionals need to understand when to best use each one.

Spreadsheet Modeling

Spreadsheet modeling is another valuable prescriptive analytics technique. It is used to perform complex mathematical calculations that help businesses determine measures of success from the past, their current operating status, and what’s likely to happen in the future. Not only can spreadsheets be designed to allow for changes in input data, but they can provide new, conclusive results each time the input data is updated.


Optimization in predictive analysis can be defined as how to achieve the best outcome with an existing set of data. It’s typically used to solve complex problems within the predictive analysis process by assembling data, building models, evaluating them, and presenting the results. Optimization is most often used to optimize production, scheduling, and inventory in supply chains.


Simulation predicts future trends and recommend optimum decisions by creating a model of the system in which the problem operates. Using a cause-and-effect algorithm, data is connected to the model to obtain a future projection. For example, to predict future sales numbers, models can be created using such data as the sales staff experience, product quality, various market factors, and how they all relate to one other. Those soft factors are added to the model to simulate the cause-and-effect scenarios that may predict future opportunities.

Machine Learning and Algorithms

Although many organizations are still trying to understand what prescriptive analytics is and whether they can use it to streamline business processes, those who have started to use it are finding they have better outcomes. Algorithms and machine learning are being relied upon to apply statistical formulas against a company’s data sets, including transactional, historical, and real-time data inputs.
Organizations that are harnessing the power of prescriptive analytics find they’re able to better manage their supply chain, optimize production, and enhance their clients’ experience. One key example of machine learning relates to the ability of online vendors to suggest newly launched products to customers based on their past purchases.

Skills Analytics Professionals Need to Be Successful

Although the exact skill sets that analytics workers need to succeed is likely dependent on the industry they’re working in, there are several that are uniform across the board.

Analytical Skills

Professionals who have advanced analytical capabilities can collect and evaluate information to solve problems. In addition to being able to find solutions, they’re able to interpret data and detect patterns, both of which are critical components of careers in data science.

Attention to Detail

Successful analysts are meticulous in their work because they know small mistakes can have large consequences. As such, the ability to identify and remedy inconsistencies is paramount.

Communication Skills

Data scientists need to identify patterns and propose solutions and be able to communicate those findings in simple, easy-to-understand reports. In addition to helping build working relationships with team members, strong communication skills help analysts work more efficiently.

Critical Thinking Skills

The job of an analyst is to identify and solve problems, and critical thinking capabilities help professionals view problems rationally. Analysts who are charged with reviewing customer feedback data, for example, may use critical thinking to pinpoint patterns in that data, which could be used to improve customer service or order fulfillment modalities.

How Analytics Professionals Help Businesses Thrive

Professionals who have a strong understanding of analytics find their knowledge of predictive modeling, data mining, and executive information systems is applicable across many industries, including pharmaceuticals, manufacturing, finance, and others.
The ability to identify problems in large data sets and use that information to forecast outcomes not only increases organizational efficiency, but it helps decrease costs and increase profitability. Professionals who use datasets to create forecasting models and management support system software programs are in high demand because these capabilities enable organizations to access information in statistical form and build a competitive advantage.

Taking Steps Toward a Career in Prescriptive Analytics

Prescriptive analytics is still a relatively new field, but businesses see the value of developing mathematically prescribed actions for business scenarios. Not only can prescriptive analytics improve outcomes, but it also allows managers to quantify the effect of decisions before they’re made.

Professionals who are interested in pursuing this career will find the Online Master of Business Analytics program at Ohio University offers the education and skills to be successful. The coursework is robust, teaching conventional analytic tools and methods, as well as the advanced analytical skills to analyze and predict outcomes.

Learn more about what prescriptive analytics is and how an Online Master of Business Analytics degree from Ohio University can help you toward that career path.

Recommended Readings

Understanding the Future of Business: What Is Business Analytics?

Making Use of Data: What Is Big Data Analytics?

Two Necessary Analytics Careers: Business Analytics vs. Data Analytics


Dimensional Insight, “3 Advantages to Using Simulation in Predictive Analytics”

Forbes, “Descriptive Analytics, Prescriptive Analytics and Predictive Analytics for Customer Experience”

Halo Business Intelligence, Descriptive, Predictive and Prescriptive Analytics Explained

IBM, Machine Learning as Prescriptive Analytics

IBM, Prescriptive Analytics Investopedia, “Prescriptive Analytics”

Proponent, Predictive Analytics Vs. Prescriptive Analytics: What Is the Difference?

Raconteur, A Day in Data

TechRadar, “The Path from Predictive to Prescriptive Analytics”

VanRijmenam, “What is Prescriptive Analytics and Why Should You Care”

World Economic Forum, “The Value of Data”