Issue link: https://cp.revolio.com/i/760098
1. ai- and analytics-vendor m&a Will accelerate t here's no doubt that a massive land grab for anything AI, machine learning or deep learning is taking place. Research and venture database company CBInsights published a graphic showing the significant M&A activity up to October 2016, with major players as diverse as Amazon, AOL, Apple, Google, Microso, Salesforce and Twitter all continuing to drive the acquisition trend. Since the graphic's publication, General Electric has also entered the fray, acquiring Wise.io, a machine-learning firm, and Bit Stew Systems. Owing to the short operating history of most of the startups being acquired, these moves are as much about acquiring the planet's few available AI experts as they are about the value of what each company has produced to date. Google recently made headlines by hiring top AI gurus from Stanford and Snapchat. e battle lines for AI-enterprise mindshare have clearly been drawn between IBM's Watson, Salesforce's Einstein and Oracle's Adaptive Intelligent Applications. What's well understood is that AI needs a consistent foundation of reliable data on which to operate. Because few startups offer these integrated capabilities, the quest for relevant insights and recommendations to improve forecasting and decision making will lead to even more-aggressive M&A in 2017. 2. data lakes Will finally Become UsefUl Many companies who took the data-lake plunge in the early days have spent lots of money buying into not only the promise of low-cost storage and processing but also a plethora of services to aggregate and make available big-data pools to gain better insights. e challenge has been finding the skilled data scientists (although noted expert Tom Davenport recently put forth a theory that it's a myth that data scientists are hard to find) that can make sense of the information while also guaranteeing the reliability of the data to which other data is being aligned and correlated. Data lakes have also fallen short in providing input into and receiving real-time updates from operational applica- tions. Although macro insights are attainable, a closed loop of recommended actions to outcomes has yet to be consistently achieved. is round trip is essential not just for active learn- ing through AI, as previously outlined, but also for actually measuring the value of the data and the investment in time and resources. Fortunately, the gap is narrowing between what has tra- ditionally been the discipline and set of technologies known as master data management (MDM) and the world of operation- al applications, analytical data warehouses and data lakes. e THE DATA CENTER JOURNAL | 11 www.datacenterjournal.com