During 2017, consumers and merchants alike can expect to see their data production greatly increase. Larger and larger amounts of data are produced every single day, and this is proving beneficial from a number of viewpoints, especially in regards to artificial intelligence. What might come as a surprise, though, is that while investments in big data technology are increasing, fewer companies are hopping on the bandwagon, meaning this “trend is likely to lead to a growing divide between organizations that ‘know’ and those that don’t know.” Those who take advantage of big data need to start asking deeper questions to ensure they are getting the best insights out of the data they collect and analyze. In addition, they must take into consideration that the results of their analyses may require them to change their business behaviors.
2016 Election Outcome
The outcome of the 2016 Presidential Election is a prime example of why big data users need to reevaluate the way they use their analyses. While the data was there, some big data specialist were not able to identify pinpointing trends because these trends went against their present understanding, thus causing them to not see what the actual outcome would be. There were a few analysts who were able to recognize these trends and create an accurate hypothesis of who would win the election, but these analysts “were repeatedly laughed at and disregarded.”
To move past biased views of big data, organizations must develop environments that foster the creation of new ideas. More so, they must embrace these new ideas and safely explore them; this is what produces a convergence of the information they collect and the accurate insights they are able to develop.
How does the convergence take place?
In order to develop accurate insights from big data, smarter algorithms along with the use of superior AI systems will be needed. Automated responses can then be developed in regards to the revealed insights obtained through big data analytics. Cognitive computing is greatly enhancing the ability to reveal such insights because it creates valuable findings from data that is either structured, unstructured, or both.
Refined Machine Learning
Streaming analytics is going to become a default for enterprises, providing them with the ultimate advantage in relation to maintaining a competitive edge. While the adoption of streaming analytics took several years for large enterprises during the past decade, the adoption rate is going to be greatly slashed during 2017. We are going to see enterprises go from analyzing their data in batch modes once or twice a day to streamlining the data in real-time, enabling them to take opportunistic actions; this is going to lead to these enterprises better meeting the needs of their customers, and it is thanks to refined machine learning that this benefit will be obtained.
Big Data is Still Moving Toward the Cloud
Big data can be stored either in or outside the cloud. In 2017, though, the rise of cloud-based big data computing is going to increase. In fact, nearly 35 to 40 percent of big data workloads will be processed via the cloud, with the majority of these processes being deployed in the cloud by no later than 2019 (that’s less than two years away). Why is this push toward cloud-based big data computing taking place? Well, it’s mostly due to security issues. Large enterprises — while many of them are nicely backed by financial resources — don’t have the necessary commercial security system resources it takes to maintain their own data.
C-suite Executives will Prioritize Outcomes Driven by Big Data
Lastly, in 2017, C-suite execs will gain more clarity in relation to the potential of analytics, and this will increase their participation in using data analyses throughout all departments. Employees across the entire organization are expected to have access to pertinent data, giving them the ability to make smarter choices and become more productive. When data is able to drive all decisions across the whole enterprise, this results in the development of a competitive edge and more profitable business outcomes.
Contributed by: Linda Gimmeson, a tech writer with a focus in big data, machine learning, & IoT. Linda discusses big data, emerging technologies, and how companies can get real value out of their data.
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