An explanation of advanced vs. predictive analytics

In this video, TechTarget's product marketing associate Katie Donegan discusses the differences between advanced analytics and predictive analytics.

Do you know what makes weather forecasting possible?

Companies are increasingly prioritizing and valuing business intelligence -- or BI -- which often involves putting data and analytics in the hands of all workers, not just data scientists. But this can create confusion. Notably, employees are conflating advanced analytics and predictive analytics.

Here, we'll go over the differences between the two methods of data analysis.

Let's start with advanced analytics. This is a data analysis approach that uses a wide range of statistical methods -- like predictive modeling, machine learning, business process automation and more -- to analyze business information from a variety of sources.

Advanced analytics can help organizations be more responsive and increase accuracy in decision-making because it offers capabilities that traditional BI reporting lacks. It combines consumption models with historical data and AI to help answer complex questions that can ultimately boost business, such as the following:

• What time of day are customers most receptive to advertisements?
• What level of profitability is achievable at that time?
• What price point are they most likely to buy at?

There are four levels of advanced analytics that increase in complexity and potential, like rungs on a ladder. At the lower end, there's descriptive analysis -- which just presents data without insight -- then diagnostic analytics -- which begins to delve into the "why" of the data, like why sales increased, for instance.

The next rungs are predictive analytics, and prescriptive analytics.

So, when talking about the differences between advanced and predictive analytics, advanced is an umbrella term, while predictive is just one form of advanced analytics. As the name suggests, predictive analytics dishes out predictions.

One of the best-known uses of predictive analytics is weather forecasting -- along with election forecasting, disease spread predictions and climate change modeling.

The use and effectiveness of predictive analytics have grown alongside the emergence of big data systems, as well as the commercialization of machine learning tools.

Predictive modeling teams are often comprised of data scientists trained in math or statistics, as well as hard science disciplines like physics. And while advancements in AI and automation are making it easier than ever to simplify the predictive analytics process, experts warn -- due to the relative unreliability of anything AI-generated -- that reliable predictive analytics is still a professional's game.

What other use cases are there for advanced and predictive analytics? Share your thoughts in the comments and remember to like and subscribe.

Sabrina Polin is a managing editor of video content for the Learning Content team. She plans and develops video content for TechTarget's editorial YouTube channel, Eye on Tech. Previously, Sabrina was a reporter for the Products Content team.