The Road to Higher-Value Decision-Making

Predictive analytics is one type of data analytics (also referred to as advanced analytics) which uses past and current data in order to make predictions of what is to come. This form of data science relies on deep learning and machine learning algorithms, like regression and classification, to generate predictive models that are used to make further decisions.

74% of health plans said that they would increase investments in predictive modeling in 2021. HealthcarePayer Intelligence

Business Intelligence vs Predictive Analytics

There is a common thread between the practices of business intelligence and predictive analytics. Business intelligence (BI), as traditionally defined by Forrester, is a “set of methodologies, processes, architectures, and technologies that leverage the output of information management processes for analysis, reporting, performance management, and information delivery.” Forrester has since adjusted their definition of BI to include traditional data warehouses, associated dashboards, and end-user reporting. This is normally used in addition to data mining with analytics focused on the use of historical data.

SDLC Predictive Analytics Chart

Moving from descriptive to predictive analytics becomes more complex, but yields more value.

The Value Chain Model of Analytics – From Descriptive to Diagnostic, Predictive, and Prescriptive

There are a variety of models of analytics all based on what data is being measured. Descriptive analytics, for example, looks only at past data and asks “what happened?” Diagnostic Analytics, on the other hand, offers insight into past data by analyzing why something happened.

Predictive analytics looks forward and takes the patterns of descriptive analytics in conjunction with the insight behind diagnostic analytics to further make predictions of what will happen next. Following predictive analytics is something known as prescriptive analytics, which utilizes the predictions made and asks how those outcomes can be brought to life.

How is Predictive Analytics Used?

Some of the many uses of predictive analytics includes risk and opportunity evaluation. Overall, businesses use predictive analytics to suggest future actions to make positive changes. This can lead to outcomes such as fraud detection, reduced risk, optimization of customer care and new products, improved operations and increased revenue via the competitive advantage given from the culmination of hindsight and foresight.

Predictive analytics is commonly used to assess risk and is thus able to be applied in a variety of business models. Some industry examples include banking and financial services, retail, oil gas and utilities, government and the public sector, health insurance, and manufacturing.

illustration of the USA and some medical symbols representing the need for predictive analytics in healthcare
Using Predictive Analytics in Healthcare to Forecast 87% of Sudden Death Events Read Insight

Predictive Analytics Tools & Techniques

Generally, there are six categories of software or platforms that are used for predictive analysis in a business setting. Platforms can be free, or range widely in price, all depending on capabilities and the capacity needed:

  1. Open Source and Freeware
  2. Proprietary
  3. Software API
  4. Predictive Lead Scoring Platforms
  5. Predictive Pricing Solutions
  6. Customer Churn, Renew, Upsell, Cross Sell Software Tools

Many of these platforms use similar techniques in order to organize data and find patterns, including:

Healthcare Analytics logos
Healthcare Analytics: Choosing the right data analytics tools, for the right role, and the right purpose Read More on Analytics

Predictive Analytics Delivers Higher Value with Greater Complexity

Predictive analytics is the next step in the AI evolution as it gives business leaders the foresight they need to improve performance, make more successful strategic decisions, and predict customer wants and needs. As more organizations shift from traditional decision-making approaches to more data-intensive and complex methods, insights will be more valuable but also more complex, requiring new tools and capabilities.

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