Big Data is only going to get much bigger, so big in fact that companies attempting to deal with massive and more complex data sets are going to find it much harder to manage and derive value out of those massive lakes of data. At least that’s what Neeraj Sabharwal, Head of Cloud and Big Data at Xavient Information System
s is telling us.
GigaOM had the opportunity to discuss the future of Big Data analytics with Sabharwal, who shared some interesting insights and statistics on how Big Data is poised to change in 2017. Sabharwal is no stranger to Big Data and has a long and storied experience
with the technology.
Sabharwal told GigaOM that Xavient has been analyzing customer trends, as well as Big Data technology adoption to come up with predictions that should come to light in 2017. Sabharwal said “Working with our internal team, we came to several conclusions, which will directly impact the financial markets.” Those conclusions include:
- Banks will seek the ability to access even more accurate data and in near real-time, allowing institutions to be more cost efficient, and more profitable.
- There will be an increased focus on customer engagement and the customer experience vs. traditional focus on customer service through the innovated application of big data and predictive analytics that allow organizations to provide better customized services.
- Institutions will seek the development of “sentiment analysis” technology that provides a gauge on individual customer satisfaction levels.
- There will be an accelerated evolution of cloud technology and its growing acceptance in banks and financial services organizations, and the pressures — cyber, legacy system issues, growing data sets — that are forcing the hands of banking IT execs.
“Naturally, those predictions will have a much farther reach than just the financial sector, impacting everything from retail to healthcare to government services,” said Sabharwal. “The key factor here is that with financial organizations leading the way, successes will grow and other industries will take notice.”
That said, the transformation of Big Data into something more won’t happen overnight. A fact that many analyst firms agree with. For example, Gartner
revealed at its October Business Intelligence & Analytics Summit 2016, in Munich, that surveys show that 48 percent of companies have invested in Big Data in 2016, up 3 percent from 2015. However, those who plan to invest in Big Data within the next two years fell from 31 to 25 percent in 2016. Nick Heudecker, research director at Gartner said, “Investment in Big Data is up, but the survey is showing signs of slowing growth with fewer companies having a future intent to invest. The big issue is not so much Big Data itself, but rather how it is used.”
A statement that falls in line with Sabharwal’s predictions. Sabharwal added, “Growth may be slowing, but the amount of data, both structured and unstructured, is increasing exponentially. It all comes down to how Big Data is being used.”
Therein lies the conundrum. Businesses today have more data than ever, which is growing rapidly, but if they do not know how to leverage that data, it becomes almost impossible to demonstrate the value of any Big Data project. “This could be due to the fact that many Big Data projects don’t have a tangible return on investment (ROI) that can be determined upfront,” said Heudecker. “Another reason could be that the Big Data initiative is a part of a larger funded initiative. This will become more common as the term “Big Data” fades away, and dealing with larger datasets and multiple data types continues to be the norm.”
“That is the very reason why companies like Xavient exist,” said Sabharwal. He added “Xavient is committed to providing customers with tailored capabilities and solution flexibility and making our real-time data analysis solutions ubiquitous in an enterprise.” A statement backed by Xavient’s further commitment to supporting the ODPi Interoperable Compliance Program
for its DiP (Data Ingestion Platform), a real-time data analysis application, and ensuring its ability to successfully run on multiple ODPi Runtime Compliant Platforms.
While Sabharwal is certain that his predictions will be on the money and the creation of Big Data is destined to grow, the most important issue facing Big Data in 2017, is just knowing what to do with it.