Predictive Analytics to Picture the Future

Julian Alvarado

Sr. Content Marketing Manager, Sigma

Businesses across industries are seeking to reduce risk, identify new opportunities, and make better decisions. Predictive analytics gives companies an edge by allowing them to see likely outcomes of various actions. Let’s look at how today’s businesses are using predictive analytics, the most common type of predictive model, and a caution to consider when implementing predictive analytics.

What is Predictive Analytics?

Predictive analytics both forecasts possible future outcomes and identifies the likelihood of those events happening. It helps organizations plan more effectively, set more realistic goals, and avoid unnecessary risk. It also allows teams to more accurately anticipate future performance based on all the factors affecting it. When business teams are able to conduct rapid, iterative analysis to evaluate options, they’re empowered to make better decisions faster.

Scenario Modeling: What If?

Scenario modeling is one of the most valuable types of predictive analytics. It’s the process of creating data models of potential future scenarios in order to aid planning and decision-making. Business users such as financial analysts, capacity planners, and CFOs use scenario modeling to explore iterative “what if?” questions to evaluate possible outcomes.

Unlike conventional forecasting, which is based on a “most likely” or “best-case” prediction, scenario modeling produces many possible outcomes based on a variety of inputs and variables. This level of insight allows companies to create flexible long-term plans, develop contingencies, and make decisions.

What You Can Do With Predictive Analytics

Companies of all types and sizes are using predictive analytics to spot new opportunities, reduce risk, and make data-driven decisions. Here are several examples of how today’s organizations are using predictive analytics.

Predictive analytics across business teams

Predictive analytics is especially powerful for teams because it allows decision-makers to be more confident about the future.

  • Sales teams use predictive analytics to forecast the revenue potential of particular customer segments.
  • Marketing teams predict how much revenue they’re likely to generate with an upcoming campaign based on various contingencies using predictive analytics.
  • Finance teams create more accurate projections for the next fiscal year based on different scenarios.
  • Operations teams predict demand for various products in different regions at specific points in the upcoming year based on the likelihood of possible impacts.

Predictive analytics across industries

The value of predictive analytics isn’t limited to just a few industries. Nearly every industry is benefitting from the insight that predictive analytics delivers.

  • Retailers use predictive analytics to optimize pricing and merchandise planning. They also use it to create more effective product recommendations and other promotional strategies.
  • Banks and financial services companies employ predictive analytics to measure risk and reduce fraud, as well as to informing their marketing efforts.
  • Municipalities use predictive analytics to understand and predict population behavior and trends to minimize congestion in cities, reduce crime, and allocate resources.
  • Healthcare organizations are able to better detect illness and manage patient care using predictive analytics. They also use it to detect claims fraud.
  • Manufacturers implement predictive analytics to predict consumer preferences and demand as well as to improve quality and efficiency on the factory floor.

Cautions When Using Predictive Analytics

It’s important to note that while predictive analytics can help predict probable outcomes based on known variables, it is not failproof. Predictive analytics can only identify correlations, anomalies, and patterns. It cannot determine cause and effect. Especially when organizations rely on AI-driven analytics, which does not have the complex critical thinking capabilities that humans do, using predictive analytics can have unintended consequences.

One of the greatest examples of the danger of using predictive analytics without a thorough understanding of underlying cause and effect is inequity. In applications such as home lending, credit scoring, and hiring processes, the use of inadequate predictive models has resulted in statistical discrimination against racial and ethnic groups.

When using predictive analytics, it’s important to remember that correlation does not imply causality, and teams must ensure that any action that a company takes as a result of predictive analytics does not result in inequity or discrimination.

Predictive Analytics with Sigma

The nature of predictive analytics is complex since it bases predictions on numerous factors and contingencies. Traditional BI tools simply weren’t built for the speed, scale, and complexity of today’s world. But Sigma was purpose-built to empower teams across finance, sales, marketing, and more 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 answers to their pressing “what if” questions.

Direct connection to a centralized data source

Sigma connects directly to a company’s cloud data platform, a centralized repository that automatically collects and stores data. This means that the data underpinning scenario models 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

Intuitive interface and simple collaboration

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 analysts 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.

See into the Future with Predictive Analytics

While no one can predict the future, predictive analysis lets you see likely outcomes that await you if you take a given path. Understanding probable outcomes and having the ability to prepare for them is more than a competitive advantage in today’s uncertain world: It is the most critical component of a competitive, highly successful, and future-proof business.

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