We saw the usual spate of announcements coming from the major software companies this fall. Lots of new extensions to the Salesforce.com and Oracle marketing clouds, Adobe extensions and updates to Creative Cloud, and most recently, IBM’s Launch of IBM Verse. And that’s just the big players.
All of the fall software announcements had one thing in common: analytics. Whether it’s sales analysis, data for making marketing decisions or prioritizing emails, analytics -- predictive analytics especially -- is everywhere. It’s behind the latest supply chain management tools and integrated into CRM systems. Analytics is in email too.
The Drivers Behind the Analytics Explosion
Two trends are driving this explosion in analytics. First, the technology has matured. It is at the point where analytics can deliver reliable and relevant results most of the time. Many of the cloud analytics platforms have helped to make deploying and using analytics much simpler as well. Analytics is everywhere because it is easier and more useful.
The second trend -- and by far the more important one -- is that businesses need analytics to cope with the complexity of modern global business. New marketing methodologies such as consumer decision journeys only work when marketing professionals can detect signals in data from social and digital channels, point-of-sale transactions, a consumer’s location and other interactions between a company and its potential customers. Marketers need analytics for that. Sales professionals are trying to better discern which leads are best to pursue. Analytics can help make those decisions. Is there a bottleneck in a global supply chain? Once again, analytics helps to answer that critical question. Which server in the data center might fail in the next three days? Analytics!
Relying on Intelligence, Not Luck
In the past, most business professionals were forced to rely on a combination of incomprehensible raw data (and way too much of it for a human to process), experience and intuition. Insights were hard to come by and often amounted to luck, not intelligence. The pace and scale of global business no longer allows that mode of operation. It has taken 30 years to get to the point where machine learning, statistical analysis, software infrastructure and computer horsepower can now provide meaningful and actionable insights to help run businesses more efficiently.
While there is still a place for experience and intuition in interpreting data, businesses are no longer required to rely solely on the rare and expensive experts who can do that. Instead, those experts are free to tackle the truly complex problems, the strategic problems, of global business because analytics is covering the common problems.