Guide to big data analytics tools, trends and best practices
A comprehensive collection of articles, videos and more, hand-picked by our editors
The components of the phrase big data analytics seem to fit together like hand and glove. For example, in a 2013 survey conducted by The Data Warehousing Institute (TDWI), analytics was far and away the most-cited response to a question about the business and technology tasks most likely to improve inside organizations that harness big data. It was chosen by 61% of the 461 respondents; the closest follower on the list of big data use cases was selected by just 39%.
Another data point: Consultancy Gartner Inc. predicted in February 2014 that 25% of large, global businesses will adopt big data analytics tools for use in data security or fraud detection applications by 2016 -- up from 8% now. Companies that do so can give themselves a better chance to "stay ahead of malicious actors," said Gartner analyst Avivah Litan.
In healthcare, meanwhile, big data analytics could provide the scientific means to help foster better treatments for patients. "This is completely about outcomes, outcomes, outcomes," said Lisa Khorey, vice president of enterprise systems and data management at Pittsburgh-based health system UPMC, in discussing its big data initiative at the 2014 Oracle Industry Connect conference in Boston. "We're seeking a scientific orientation so we practice [healthcare] based on measurements."
But overall, it's still early days for big data analytics deployments: Forty-five percent of the respondents to the TDWI survey said their organizations didn't have any big data strategies in place. And all the promise is accompanied by plenty of possible pitfalls.
To help you find the former and avoid the latter, SearchBusinessAnalytics and sister site SearchDataManagement have recently published a range of content offering advice on managing big data projects. In one article, consultant Rick Sherman details a checklist of big data project management to-do items. Another looks at the long-term thinking that UPMC and financial services firm CIBC are applying to their big data analytics programs, while a pair of stories provide tips on evaluating big data software and deciding whether technologies such as Hadoop and NoSQL databases are right for your organization.
In a video Q&A, industry analyst John Myers discusses the mix of platforms typically tapped to help power big data environments; in another, consultant William McKnight notes that there's no single recipe for building a big data architecture. We also examine the touchy issues of big data analytics ethics and legal considerations pertaining to big data and data privacy. Good luck in your efforts to pair up big data and analytics in a way that provides the business benefits you're looking for.