The term "predictive analytics" is often used in the context of business intelligence, business analytics, data mining, or data science. Other keywords, such as descriptive or prescriptive analytics, only cause extra confusion.

Business Intelligence and Business Analytics

Predictive analytics is considered a subset of business intelligence (BI) and business analytics (BA). These terms are often used interchangeably, although there are slight differences in the used methods.
Business intelligence is used to answer questions about the current situation. It is concerned with the events of the past and their effects on the present.

Business analytics, on the other hand, extends BI's view into the future. It relies on a statistical analysis of the company data and helps to answer questions about the causes, effects, and consequences of such events. Also, by tweaking some parameters, it uses scenarios to point out alternative courses of action.
The primary difference between predictive analytics and business analytics is that BA is reactive in nature – analysts observe and act on historical data (e.g., revenues, profits, loss).

Whereas predictive analytics is proactive: it assists companies in actionable intelligence and preventive action, even without human intervention.

Another significant difference is that predictive analytics mostly uses unstructured public (e.g., social media) or proprietary data (e.g., from research companies). However, business analytics uses traditional data sources, such as data warehouses and data marts (structured data).
Most likely, in the future, both concepts will blend and be equal.

Predictive analytics is easy to understand but challenging to implement

The concept in itself is easy. It has been around for decades. But predictive analytics is not the first step you take when you plan to set up a data-driven business. You need to clarify your strategy, build infrastructure, and get and maintain the right data.

Before committing to long-term investment, you need to understand the ROI (Return on Investment). The cost threshold for an analytics program usually starts around $1 million - this is how much your company will need to operate a team of four to six members (data scientist, data engineer, data architect, software developer). However, businesses can optimize costs by using specialized companies and utilizing various engagement models (nearshore dedicated teams, contracting full projects, hiring an expert to close specific gaps).

"Predictive analytics is the GPS of Business Intelligence."

The benefits of accurate predictive analytics models are overwhelming.  Netflix, for example, based its whole business model on predictive analytics. They use massive amounts of data to predict which TV shows and movies their users would like to watch. They also implemented a well-refined recommendation algorithm to predict what users will likely want to watch.

Stages of analytics

The Gartner analytics maturity model distinguishes four stages. These stages are:

  • Descriptive analytics  - What happened? It deals with the past and tries to understand the effects on the present (Business Intelligence).
  • Diagnostic analytics – Why did it happen? It answers questions about the causes, effects, interactions, or consequences of events (Business Analytics)
  • Predictive analytics – What will happen? It looks to the future and provides predictions about the likelihood of future events based on data mining, machine learning, and other statistical methods.
  • Prescriptive analytics – What should we do to prevent an event from happening? Prescriptive analytics goes one step further - it automates the decision process and triggers the actions.
Chart showing the analytics maturity model
Analytics maturity model

Potential use cases for predictive analytics

Many industries have used predictive analytics with great success. And it seems there are plenty of use cases. Banks use it for credit scoring; retailers implement dynamic pricing to increase their revenue; airlines forecast the maintenance of plane parts. 

Use of predictive analytics across different industries
Use of predictive analytics across different industries

Yet, there are limits to it.

No one can look into the future. No one can know and analyze data from the future. Predictive analytics is a method that helps to predict trends. But it is just that, a prediction. Based on a simplification of the existing, complex world. 

Moreover, the fundamental assumption in predictive analytics is that past behaviors will not change in the future. The models in use are mostly static, stationary. Therefore, the once-derived model has to be repeatedly questioned. Managers and experts should keep asking what the fundamental assumptions were and when they no longer apply.

How can we help?

Companies need to set up solid professional information management. It is the prerequisite for successful predictive analytics to turn big data into smart data. If you face these challenges, let us know, and we will connect you with experts who know how to set-up proper big data pipelines. Unlock the full potential of your data estate and start monetizing it.