The device landscape is as vast as it is complex. With at least 63,000 possible device profiles reported—a number growing at almost 20% per year—the scale of device fragmentation is staggering. New models, operating systems, browsers, screen resolutions, etc., make it extremely difficult for web and app developers to deliver a consistently flawless user experience across all combinations.

Given the competitive value of analytics and rapid adoption rates across industries, you can’t afford a subpar analytics program. In the late 90s, Oakland Athletics general manager Billy Beane used data to discover undervalued talent and assemble a perennial playoff-caliber team, and he did so on a shoestring budget compared to Major League Baseball’s heavy hitters. Beane’s pioneering use of data analytics became the subject of the bestselling book Moneyball.

Many analytics programs struggle to assimilate data from numerous and unpredictable sources, but automated ELT offers a solution. Why do so many businesses struggle to establish successful analytics programs? A lack of data is not the problem. Data volumes — from hundreds of cloud applications to millions of IoT endpoints — are exploding across organizations and industries.