After using predictive analytics to determine what could happen based on data trends, the next step is to figure out what should be done moving forward. Prescriptive analytics is used to help companies make informed decisions about what move to make next to optimize operations and fix problems.
Prescriptive Analytics Process
Prescriptive analytics is the third step that follows descriptive and predictive analytics. Unlike descriptive analytics, prescriptive analytics is an advanced concept that requires deep insight into data.
The ultimate goal of prescriptive analytics is to come up with ways to address and optimize the possible future outcomes identified during predictive analytics. It’s an answer to the question, “what should we do next?”
Another question that can be answered is, “how can we line things up to get the best possible outcome even though there are uncertainties?”
Every business has issues that managers and executives have to solve. That’s a lot easier to do when you know why those issues occur and what you can do about it. Prescriptive analytics is a viable solution since the process yields “prescriptions” for diagnosing a problem.
What-if scenario analysis is one technique that can help with diagnosis. Using the data that’s been collected, a series of assumptions can be made to generate various scenarios that may occur. From there, data analysts can apply different actions to measure effects within the simulated scenarios.
The effects are analyzed using mathematical modeling. The most common types of modeling include:
- Machine learning
- Natural language processing
In many cases, you’ll be analyzing an “if this, then that” scenario. Before creating scenarios you’ll need to:
- Define the problem being addressed
- Establish the goals of your analysis
- Identify variables, both known and random
- Set control factors
- Know the potential constraints
It’s best practice to also identify where there are data uncertainties or gaps in the information. Those unknowns can have a profound impact on potential outcomes and what actions need to be taken. That’s why in addition to running scenarios with known variables, they should be run with random variables as well to mitigate the unknown.
In the end you’ll run a large number of scenarios with different variables to gauge cause-and-effect. The scenarios are then compared to determine how to optimize for the best potential outcome. In doing so, the insights from predictive analysis become actionable.
One of the most well-known examples of prescriptive analytics in the real world is credit scoring. Financial institutions use prescriptive analytics to estimate a person’s ability to pay back a loan and in doing so generate a credit score.
Benefits of Using Prescriptive Analytics
Prescriptive analytics isn’t a silver bullet that can fix every problem a company faces, but it does have its advantages.
Informed Decision Making
One of the most important things prescriptive analytics does is take some of the guesswork out of decision making. Being able to run simulated scenarios gives you an idea of how a change affects operations before making it. It can also give you a range of possibilities so that an organization understands the best case/worst case scenarios.
Analyzing potential scenarios beforehand also helps a business pivot more quickly if things don’t go exactly as expected. Chances are you came across a similar situation when you were analyzing and are better prepared to react.
Cuts Down on Trial and Error
In some cases, prescriptive analytics is used so that a company can experiment and explore possibilities with less trial and error. Ultimately, a lot of time and money is saved by running scenarios first.
Wide Range of Applications
Prescriptive analytics is an optimization strategy that can be used to identify ways to enhance production, sales, lead generation, supply chain and customer satisfaction. It can also be used for financial planning, inventory management and pricing. In other words, there’s a wide range of applications.
Challenges of Using Prescriptive Analytics
Right now only a fraction of businesses use prescriptive analytics despite knowing that it could give them a competitive edge while saving time and resources. Why? There are two important reasons businesses are hesitant to start using prescriptive analytics.
The complexity of prescriptive analytics is one of the top reasons why companies shy away from using it. The right tools and professionals need to be in place in order to make predictions and create simulated scenarios that can be used to test outcomes for different actions or variables.
Not 100% Accurate
Prescriptive analytics is based on insights from predictive analytics. Even if you do everything right, predictive analytics is not foolproof. It’s a guesstimate based on data for what’s happened in the past. This fact scares some businesses away from using it, but without prescriptive analytics a business is making decisions totally blind.
As technologies improve and data-driven businesses gain a better understanding of the process, prescriptive analysis should be adopted at a much higher rate. Businesses that already have data mining tools for conducting descriptive and predictive analytics are halfway there. The next step is putting the right people and resources in place to create models that can test out how the predictive analysis predictions could pan out so you can act accordingly.