The rising promise of big data
Businesses across the globe are increasingly turning to big data analytics to provide them with essential market information and consumer behavior insight. While some skeptics may have initially dismissed this as another passing tech fad, more companies are deploying extensive and sophisticated big data and Hadoop programs, which have yielded high ROI numbers across the board. Adoption rates do not appear to be slowing down either, as a new Gartner survey reported that 42 percent of IT leaders had invested in big data technology or had plan to do so within a year. This shows that businesses are beginning to recognize the promise of data analytics solutions.
"Organizations have increased their understanding of what big data is and how it could transform the business in novel ways," said Doug Laney, research vice president at Gartner. "The new key questions have shifted to 'What are the strategies and skills required?' and 'How can we measure and ensure our return on investment?'"
To find actionable returns on big data investments, look no further than its implementation in the marketing industry. Firms that deployed data analytics tools saw revenue increase while their operating costs decreased, ClickZ contributor Krishnan Parasuraman reported. One agency was able to streamline their campaign rollout process, allowing them to execute several more a week and netting them a 20 percent higher revenue. Another firm witnessed its operating expenses plummet, dropping 66 percent due to savings on infrastructure.
The meteoric rise of big data and the enthusiastic response with which industries have greeted it have led to many to cite it as the next major technological movement. NPR contributor Adam Frank viewed data analytics as being filled with such potential that he dubbed it the "steam engine of our time". With the continued expansion and sophistication of software like Apache Hadoop able to gather colossal volumes of information and extract meaningful insights from them, there is no telling if there is a limit to big data's potential.