The ultimate goal of data analytics is to provide decision-makers with actionable insights. Understanding what happened in the past and why it happened is a crucial foundation, but teams must be able to determine a plan of action based on these insights.
Prescriptive analytics builds on predictive analytics, giving teams direction on the best probable actions to take based on predictions made. Let’s look at a few reasons why your organization may want to implement prescriptive analytics and examples of predictive analytics in action.
What is Prescriptive Analytics?
Prescriptive analytics uses data to consider all the factors bearing on a business question or situation and to identify the courses of action likely to have positive outcomes. It’s the most complex type of data analytics, incorporating advanced techniques such as simulation, neural networks, and machine learning — which is why less than 3% of companies are using it in their business. But it is a valuable discipline that more are taking advantage of.
Advanced technologies and techniques are necessary for prescriptive analytics primarily because of the amount of data that must be processed and analyzed. For accuracy, every relevant factor must be taken into account. Manual analysis of the amounts of data involved is impractical.
But it’s important to note that while using AI in prescriptive analytics is currently making headlines, the fact is that this technology has a long way to go in its ability to generate relevant, actionable insights. The use of AI at scale requires running thousands of queries in search of statistical anomalies. But randomly identified anomalies don’t always point directly to business opportunities. At least until AI technology advances, uncovering truly meaningful business insights requires human involvement to some degree — analyzing data in the context of business processes, market trends, and company goals, and interpreting it.
4 Benefits of Prescriptive Analytics
Today’s organizations are using prescriptive analytics for a variety of reasons and are driving outcomes such as greater efficiency, risk reduction, and improved profitability. Here are four of the most significant benefits of implementing prescriptive analytics.
- Action-oriented. Prescriptive analytics is action-oriented. It goes beyond forecasting to identify levers that can be deployed to achieve desired outcomes. It can also recommend particular actions or changes to make based on the factors involved.
- More accurate scenario modeling. Few business questions are simple. Most involve complex scenarios with many constantly-shifting factors. To get accurate insights, you must use accurate scenario modeling. Prescriptive analytics can account for the fact that situations are continually changing, and it uses real-time or near-time data for better recommendations.
- Speed. Because prescriptive analytics involves building reliable models and using machine learning to analyze near-time or real-time data, it allows teams to quickly understand the probable outcomes of various courses of action and which plan of action is best.
- Reduced human error. Prescriptive analytics reduces the opportunities for human error to skew the accuracy of insights. Again, human involvement is crucial to analytics of any kind, but with predictive analytics, this involvement can focus on critical thinking rather than simple computations.
Examples of Prescriptive Analytics
Let’s make the benefits of prescriptive analytics more concrete by looking at a few examples of how organizations in a variety of industries are using it.
Healthcare — Hospitals are implementing prescriptive analytics to reduce their hospital-acquired infection rate. Prescriptive analytics is able to flag instances of infection and identify which ICU patients are likely to experience infection based on their vitals. It is also able to identify commonalities in the patients with hospital-acquired infections, including staff in contact with these patients who may need retraining. Hospitals can also use predictive analytics to develop programs to prevent hospital-acquired infections.
Manufacturing — Prescriptive analytics helps manufacturers see where process improvements could have the most significant and immediate impact on profitability. Through prescriptive analytics techniques, manufacturers can optimize production planning, scheduling, inventory, and supply chain logistics with specific recommendations for action. Additionally, manufacturers can use prescriptive analytics to manage and maintain equipment more effectively.
Finance — Financial institutions are using prescriptive analytics for risk reduction. Using risk assessment models, they can thoroughly qualify potential borrowers before extending credit. Additionally, early warning systems powered by predictive analytics can mine transactional data in real-time to detect fraudulent activity as it happens.
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Using Sigma for Faster Prescriptive Insights
Sigma was purpose-built to empower teams to independently investigate live data at scale, easily find answers to ad hoc questions, and work together to get to the heart of complex problems in real-time. Here’s how Sigma is supporting teams who want to use predictive and prescriptive analytics to improve decision-making.
Direct connection to cloud data platform
Sigma connects directly to your cloud data platform, so you can work with data in Sigma and then write back your data models to your cloud platform. The data underpinning scenario models in Sigma is always live, making the models easy to adjust over time without having to build them from scratch. And because data is never extracted to a spreadsheet, sensitive information and corporate plans are kept safe, secure, and governed.
Predictive analytics depends on having all relevant data to account for all factors influencing the question or situation being inquired about. For this reason, it’s vital to get line-of-business teams involved who are the closest to the meaning of the data. Sigma’s intuitive user experience and flexibility of analysis empower cross-functional team members to do productive, free-flowing analysis. Sigma also allows multiple scenarios to be organized and annotated within a single online doc for a collaborative, Google Docs-like experience.
Speed for faster insights
The speed and concurrency of Sigma’s direct connection to a cloud data platform mean that users have a snappy, responsive experience no matter how large and complex the models are, the number of scenarios modeled, and the number of users collaborating on the models.
Turn Predictions into Action with Prescriptive Analytics
Prescriptive analytics is focused on providing direction to teams who are looking to drive specific outcomes and improve decision-making. It builds on the foundation of descriptive, diagnostic, and predictive analytics to identify the actions most likely to produce positive results. While it’s still in its early stages, predictive analytics presents an exceptional opportunity for companies that take advantage of the insights it can offer.