Sophisticated predictive analytics platforms generate dynamic insights that inform forward-looking business strategies. By harnessing data-driven intelligence to bridge the gap between artificial intelligence and machine learning (AI/ML) technologies and real business value, these platforms help organizations hone their competitive edge, adapt to rapidly changing markets, and achieve sustainable growth. Source: https://www.stradigi.ai

University of Michigan - Ann Arbor

Google

Business intelligence (BI) systems have steadily grown faster, simpler to use, more accurate at processing massive amounts of internal and external data, as well as identifying patterns that were previously undetected, or even undetectable, by human users. And yet, some of the more popular BI systems on the market do little more than repackage and distil existing data about past performance, albeit in a visually palatable format. A new approach to mining and deciphering unstructured data using algorithms and automated machine learning (AutoML) to forecast changes in the business landscape before they happen has quite rightly been hailed as the next big thing in artificial intelligence. Here’s how next-generation business platforms that leverage these predictive capabilities put you in a better position than traditional BI.

https://www.stradigi.ai/blog/5-ways-advanced-predictive-analytics-outshine-bi/

How advanced predictive analytics is outpacing old school business intelligence

Combining classical machine learning and deep learning within predictive analytics platforms enables users to forecast future trends and behaviors with high precision. These data-driven insights accelerate time-to-value across industries by optimizing courses of action and reducing margins of error, effectively replacing retrospective interpretations with forward-looking decision-making.

Predictive Analytics for Accelerated Decision-Making

According to a recent Forbes article, “the greater the uncertainty in an industry, the more likely their executive and C-suite leaders are using analytics to search for greater stability.” The manufacturing industry, for example, is a great candidate for advanced ML and deep learning technology to cut costs and adjust to dramatically and rapidly shifting consumer behaviors. It seems as if they are catching on, too, as the same article mentions deeper findings that show over the past year analytics’ perceived importance increased 38% among those surveyed in manufacturing.

https://www.forbes.com/sites/louiscolumbus/2020/05/31/what-you-need-to-know-about-bi-in-2020/?sh=105717493aa8

Hedge against uncertainty

Predictive analytics can forecast resources and proactively manage inventory to enhance the agility of organizations and teams. For example, in the transportation industry, predictive capabilities help leaders find supply chain efficiencies and stay adaptive to keep operations running smoothly despite uncertain external market forces. Source: https://www.stradigi.ai

Predictive Analytics for Organizational Agility

In the Consumer Packaged Goods (CPG) industry, machine learning algorithms analyze customer behaviors, demographics, and transaction histories to enable precise customer segmentation. This predictive segmentation allows decision-makers to target specific consumer groups with relevant products at optimal times, optimizing marketing strategies and product placement.

Learn Before

Related