Taming Big Data
Written by Ken Gaebler
Big data is quickly entering the mainstream of American business. But the real winners may be the companies that know how to improve their use of big data over the next twelve months.
Big data is a buzzword across every industry and market sector. In 2013, 32 percent of companies invested in big data projects--a figure that is expected to double in 2014.
But while there is strong agreement around the idea that the most successful companies will be the ones that harness big data, there is still a lot of confusion about what must be done to convert big data to improved profitability.
Recently, Entrepreneur's David Steinberg reported on ways that brands and businesses can better use big data in 2014:
- Bridge Gaps: According to Steinberg, the use of big data currently suffers from several gaps: time, people and knowledge. Big data projects can take up to 18 months to implement and consume an inordinate amount of staff time, especially if the project lacks specialized expertise and insights. By leveraging new data mining tools and the expertise of data scientists, it is gradually becoming easier for companies to bridge these gaps.
- Improve Data Management: Data capture needs to be more closely tied to business results. This need is driving many companies to refine their data management systems, giving marketing teams the information they need to develop strategies that target key audiences.
- Go to Customers: More than ever before, customers are serving up volumes of data via social media and mobile devices. Rather than focusing exclusively on structured feedback, leading businesses are working to harness unstructured data from social and mobile channels, capturing insights that were previously out of reach.
Although the big data technology continues to evolve, it's certain that increasing ROI through data mining will ultimately become a part of most companies' marketing strategies. For small businesses, it will become increasingly important to leverage accessible third-party solutions to capture critical insights from large data sources.
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