Jan 26, 2017
Big Data Trends for 2017
There are several big data trends making the rounds in the latest tech news, one of them being the revival of artificial intelligence (AI). Google has documented tests which ultimately determined it was more cost-effective and yielded better results with simple (AI) algorithms executed frequently against large datasets rather than other approaches applied against smaller sets. The second trend concerns the shift from industry sector leaders to data driven companies for many organizations. The motivation behind this is the ever-increasing tug of war between governance required for compliance, and the use of data to provide business value while implementing appropriate security in order to avoid damaging data breaches and leaks.
In 2017, many organizations will move away from data lakes to a business-driven data approach. Some companies have found out that data lakes often turned into data swamps, as the concept of one centralized, secure, fully governed place where any department could access data anytime, anywhere simply could not deliver much in the way of agile performance. Taking this trend one step further, companies will begin to understand that data agility -- the ability to understand data in context in order to take business action -- is the real source of any competitive advantage, rather than simply having the biggest data lake on the block. Companies can find great value in data agility when the same instance of data supports multiple avenues such as global messaging, database and file-based models, batch and interactive analytics.
Lastly, blockchain technology will continue to increase in popularity, particularly in the financial industry due to its distributed trust features and machine learning will begin to integrate more with microservices that leverage big data, breaking away from past integrations that typically applied only to narrow bands of streaming data.
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