E-Guide HOW TO GAIN CONTROL OVER BIG DATA PROJECTS

E-Guide HOW TO GAIN CONTROL OVER BIG DATA PROJECTS

N ow that you have your analytics projects in place, how can you ensure your organization gains true business value from these initiatives? In this e-guide, learn why industry leaders are focusing on big data value and maturity models. Additionally, uncover the data integration challenges organizations are faced with given the boom in big data growth. PAGE 2 OF 9

BUSINESSES DOUBLE DOWN ON BIG DATA VALUE, ANALYTICS ROI Ed Burns, Site Editor Businesses increasingly are done with academic and proof-of-concept analytics projects and are doubling down on extracting real business value from their data. The notion that more companies are now looking for tangible returns on their analytics investments was supported by two announcements over the past month: the release of survey results by IBM showing a heightened focus on big data value, and TDWI's launch of an online tool for assessing the maturity of analytics programs. IBM SAYS BUSINESSES HAVE A NEED FOR ANALYTICS SPEED In a survey of more than 1,000 businesses, IBM found that 63% of the organizations that had implemented big data technology saw an ROI within one year, which is up 10 percentage points compared to the results of a similar survey last year. And nearly three-quarters of the respondents said they expect their business executives to demand faster data-driven insights in the year ahead, PAGE 3 OF 9

according to a report on the survey that IBM issued last week. Report co-author Glenn Finch, global leader for technology and data in IBM's Global Business Services unit, said those the increasing ROI and greater demand for speed reflect a change in how businesses look at big data analytics. He feels that 2014 has been a "breakout year," in which big data projects went from theoretical to value-focused. And now that businesses increasingly are focused on big data value, they want to speed up their analytics processes to help differentiate from the competition, according to Finch. "Between the speed and what they're doing, it's a pretty big shift," he said. "Last year, everyone was talking about what they were going to do. This year, they started doing it." One of the main changes in this year's survey results from previous years is that businesses are now more likely to use their analytics programs to try to improve operational efficiency. In the past, the survey showed that analytics applications were being applied primarily to customer issues, like service or the acquisition of new customers. But 40% of the 2014 respondents said they're using analytics to boost efficiency, up 15 percentage points over last year. PAGE 4 OF 9

TDWI RELEASES ITS ANALYTICS MATURITY MODEL TDWI -- formally known as The Data Warehousing Institute -- last week issued the Analytics Maturity Model, an online self-assessment tool that the research and education group said would help businesses focus their burgeoning analytics projects on the importance of value. TDWI previously developed business intelligence and big data maturity models, which allow businesses to assess the progress of their projects and compare them to initiatives at other organizations. The new maturity model is the first created by TDWI to focus exclusively on analytics. The model charts an organization's progress from evaluating software and implementing simple tools to developing a data-driven culture, getting executive buy-in and operating well-established projects with strong data governance. In a press release, Fern Halper, TDWI's research director for advanced analytics, said the goal of the new model is to help businesses understand where their analytics projects stand and see where they have to improve in order to start getting real business value out of their data. Users who complete the 35 questions that are part of the assessment will receive a score assessing the current status of their analytics efforts. PAGE 5 OF 9

CORTANA SCORES AT PREDICTING NFL GAMES Microsoft's voice-activated mobile device assistant, Cortana, has quietly been racking up a strong record for correctly predicting the winners of NFL games. So far, it has a record of 108-55 on picks this season. Cortana's predictions are generated by the same Bing Predicts system that correctly predicted the outcomes of 15 of the 16 knockout stage games at this year's World Cup soccer tournament. The tool uses an algorithm based on team stats, home field advantage and weather. It also mines social media posts to incorporate sentiment analysis. You can find predictions for this week's NFL games on Cortana-enabled devices or at Bing.com. PAGE 6 OF 9

BIG DATA GROWTH INCREASES DATA INTEGRATION DEGREE OF DIFFICULTY Craig Stedman, Executive Editor Technology is ever-changing. But perhaps the one thing IT teams can count on is that the amount of data coming their way to manage will only continue to increase. The numbers can be staggering: In a report published last December, market research company IDC estimated that the total count of data created or replicated worldwide in 2012 would add up to 2.8 zettabytes (ZB). For the uninitiated, a zettabyte is 1,000 exabytes, 1 million petabytes or 1 billion terabytes -- or, in more informal terms, a whole lotta data. By 2020, IDC expects the annual data-creation total to reach 40 ZB, which would amount to a 50-fold increase from where things stood at the start of 2010. Corporate data expansion often starts with higher and higher volumes of transaction data. But in many organizations, unstructured and semi-structured information -- the hallmark of big data environments -- is taking things to a new level altogether. And because such data typically isn't a good fit for relational databases and comes partly from external sources, big data growth PAGE 7 OF 9

also adds to the data integration workload -- and challenges -- for IT managers and their staffs. SearchDataManagement recently published several articles that provide insight and practical advice on managing integration projects involving large amounts of data in general and big data in particular. In one, we look at the key considerations that need to be taken into account on big data integration initiatives. In another, we cover the complexities of integrating streaming sets of big data. And in an interview, Glasshouse Technologies consultant Jim Damoulakis offers tips on planning and implementing integration efforts that can accommodate big data growth. PAGE 8 OF 9

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