Clear the confusion of advanced vs. predictive analytics
Advanced and predictive analytics are sometimes used as interchangeable terms. Users need to understand the differences of advanced analytics techniques to maximize insights.
As organizations strive to increase data-driven operations, the number of analytics tools available grows. Understanding the differences between advanced analytics techniques and when to use them is key to unlocking maximum performance.
However, the different analytics terms have created some misunderstandings in the enterprise. This can cause confusion as automated tools and self-service BI put data and analytics in the hands of all users, not just data scientists and analysts.
One area where this is prevalent is advanced analytics and predictive analytics, with some employees using the terms interchangeably, experts said.
"Many workers don't know the terms and the differences; it's still a black box for them," said Bob Parr, chief data officer for U.S. advisory at the professional services firm KPMG.
The market for advanced analytics is growing. The predictive analytics market in particular is estimated to hit $44.3 billion by 2030, up from $13.5 billion in 2022, according to a March 2023 market report from Reports Insights.
Types of advanced analytics
There is a ladder of advanced analytics capabilities -- descriptive, diagnostic, predictive and prescriptive. Each rung increases in complexity and potential. Descriptive and diagnostic analytics both fall at the lower end of complexity.
Descriptive analytics itself is a step up from reporting, which simply presents data without any insights -- like a total sales report for the prior year. Descriptive analytics gives insight into what happened and puts context around the data.
For example, descriptive analytics reports the sales for the quarter, compares it to past sales and notes how sales changed from year to year. Diagnostic analytics delves into the why. In this sales example, diagnostic analytics can offer insights on why sales increased -- maybe a new store opened during the period being analyzed.
"Diagnostic analytics explains the why, and it's the correlation among the data points," said Ravi Teja Bommireddipalli, CEO of Robosoft, a digital transformation services and solutions company.
The next rungs in the ladder are predictive and prescriptive analytics, which are more advanced than descriptive and diagnostic and focus on the future.
What is predictive analytics?
Just as the name states, predictive analytics dishes out predictions; it's a look at what may happen in the future.
I like calling it 'perhaps analytics,' because we can't really ever predict what will happen.
Heather WhitemanProfessor and program chair, University of Washington Information School
Predictive analytics uses statistical models to study data about past performance and identify patterns to predict future outcomes.
"Predictive analytics is around the idea that if past behaviors stay the same, this [outcome] might happen," said Heather Whiteman, an assistant teaching professor and interim MSIM Online Program chair at the University of Washington Information School. "I like calling it 'perhaps analytics,' because we can't really ever predict what will happen."
The next step up in the advanced analytics category is prescriptive analytics. Prescriptive analytics takes the predictions about future outcomes and provides options for actions to take that could influence those outcomes.
"The system tells us what might happen going forward and suggests certain steps to take," said Vishal Gupta, vice president at the research firm Everest Group.
Prescriptive analytics uses advanced modeling structures, algorithms and business rules, artificial intelligence and machine learning to study how various actions might change forecasted outcomes and detail the alternatives.
Enterprise use cases for advanced analytics
Most companies are employing all four types of advanced analytics at some level, Parr said. BI tools have put descriptive and diagnostic analytics into the hands of users throughout the enterprise.
Advanced analytics typically requires workers with specialized skills, such as data scientists, although experts said some predictive analytics software vendors make products for data-literate business users.
Data professionals expect more BI software, as well as descriptive and diagnostic analytics software, to offer predictive analytics capabilities and enable real-time decision-making for enterprises.
Data scientists and analysts are not the only ones using advanced analytics tools to gain insights. The consolidation of different advanced analytics capabilities in a single tool, along with self-serve options, can enable workers throughout the enterprise.
"That has led to the power being given to the business users, and I see more and more business users using these tools," Gupta said.
Enterprise leaders must make sure their employees are ready to benefit from the increasing accessibility of advanced analytics capabilities and use the insights it creates, said Aura Popa, senior research director at Gartner. Decision-makers continue to prioritize "gut feeling over data" when making decisions, with 61% of surveyed business leaders cherry-picking data points to make a decision, according to Gartner research.
Experts agree that decision-makers, and employees overall, should understand where each analytics type brings value. This is especially important as interest and hype around generative AI accelerates.
The allure of AI use with analytics has many benefits -- reducing costs, time and resources to get insights, and accelerating decision-making. However, there are instances where data teams prefer predictive analytics over AI or automated responses to keep humans involved in these processes. This helps ensure results or responses are appropriate, legal and ethical, experts said.
Predictive maintenance. Systems use data on a machine's past performance as well as when and why past problems in performance occurred. Those insights can predict when organizations should schedule maintenance before a problem arises, but not too early as to be inefficient.
Fraud detection. These systems use past patterns to identify the likelihood of fraudulent activity. One software company used an end-to-end machine learning environment to integrate various data sources and automatically flag, identify and log novelties or deviations from the norm in transaction activity to prevent more than half a billion dollars in potential fraud loss, Gupta said.
Customer churn prediction. Analytics tools identify patterns that show why customers left in the past and can then use this data to predict possible customer churn in the future.
Employee attrition. Like customer churn, organizations can use predictive tools to identify possible employee attrition. In the case of employee attrition, predictive can provide more effective, accurate resource planning and insight to help design programs and initiative to retain employees, Whiteman said.
Demand forecasting. These models use past patterns to give organizations a look at what demand to expect in the future. For example, a global food and beverage company had disruptions in production due to stockouts. Predictive analytics forecast for inventory and estimate weekly stockout events for raw materials based on historical consumption trends and delivery lead times, Gupta said. This information helped identify a revenue loss of $90 million due to stockout risks in six months, along with a 50% reduction in disruptions.
When organizations need forecasts or they recognize that understanding patterns is just as important as a potential prediction, predictive analytics is the tool to choose, Whiteman said.
"I don't think that's going to ever stop basic and advanced analytics from being necessary, and I don't think generative AI will displace predictive analytics," she said.
