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  EDITOR’S NOTELARGE COMPANIES TAKE LONG VIEW ON BIG DATA PROGRAMSPROJECT MANAGERS MUST TAKE THE BIG DATA HELMIN EVALUATING BIG DATA TOOLS, LOOK AT THE BIGGER PICTURE The Path to Payoff on Big Data Analytics The hullabaloo over the promised business benefits of big data technologies may have some companies thinking they’re the only ones not invited to the party. But analytics deployments have yet to become regular fare.  HOMEEDITOR’S NOTELARGE COMPANIES TAKE LONG VIEW ON BIG DATA PROGRAMSPROJECT MANAGERS MUST TAKE THE BIG DATA HELM IN EVALUATING BIG DATA TOOLS, LOOK AT THE BIGGER PICTURE THE PATH TO PAYOFF ON BIG DATA ANALYTICS 2 EDITOR’SNOTE A Suitable Pairing 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, analytics was far and away the most-cited response to a question about the business and technology tasks most likely to improve inside organiza-tions that harness big data. It was chosen by 61% of the 461 respondents; the closest fol-lower was selected by just 39%.Another data point: Gartner predicted in February 2014 that 25% of large, global busi-nesses will adopt big data analytics tools for at least one data security or fraud detection application by 2016—up from 8%. 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 of the UPMC health system in discussing its big data initiative as part of the first story in this guide.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 strate-gies in place. And all the promise is accompa-nied by plenty of possible pitfalls. The three articles here offer advice to help you find the former and avoid the latter. First, we look at the long-term thinking that UPMC and financial services firm CIBC are applying to their big data analytics projects. Next we provide a list of project management to-do items. We close with tips on evaluating big data technologies. n Craig Stedman  Executive Editor, SearchBusinessAnalytics  HOMEEDITOR’S NOTELARGE COMPANIES TAKE LONG VIEW ON BIG DATA PROGRAMSPROJECT MANAGERS MUST TAKE THE BIG DATA HELM IN EVALUATING BIG DATA TOOLS, LOOK AT THE BIGGER PICTURE THE PATH TO PAYOFF ON BIG DATA ANALYTICS 3 STRATEGY Large Companies Take Long View on Big Data Programs Like many other organizations that have embarked on big data programs, healthcare services provider UPMC sees the flood of information it’s generating as a blessing and a curse. “We’re both drowning in big data and starving for it,” said Lisa Khorey, vice president of enterprise systems and data management at the organization, based in Pittsburgh.But UPMC is 20 months into a five-year plan to harness a wide variety of that data for analytics uses—and to bring in the big data management and analytics technologies needed to support the effort. In fact, company officials made a conscious decision not to invest in all of the required tools up front, said Khorey, who took part in a panel discus-sion on big data trends at the Oracle Industry Connect conference in Boston in March 2014.“I don’t think you have to buy it all on day one,” Khorey said in an interview after the panel discussion. She added that UPMC’s chief financial officer encouraged the phased approach by telling the big data project team “not to overload the buggy.” The health system did select an initial set of hardware and soft-ware at the outset, including Hadoop and prod-ucts from Oracle, IBM and Informatica. It plans to add predictive analytics tools in the summer of 2014 at the initiative’s two-year mark; pre-scriptive analytics technologies will follow 18 to 24 months later.Khorey noted, though, that project advo-cates got executive backing and organizational support for the full five-year program before beginning any deployments. That was crucial to making the three-step technology selection process work: She said the prudent approach wouldn’t be feasible “if we had to re-justify everything each year, because that takes a lot of energy.”  HOMEEDITOR’S NOTELARGE COMPANIES TAKE LONG VIEW ON BIG DATA PROGRAMSPROJECT MANAGERS MUST TAKE THE BIG DATA HELM IN EVALUATING BIG DATA TOOLS, LOOK AT THE BIGGER PICTURE THE PATH TO PAYOFF ON BIG DATA ANALYTICS 4 STRATEGY SWEATING THE TECHNOLOGY DETAILS UPMC, a sprawling organization that operates 22 hospitals and about 400 outpatient facili-ties, also developed the plan for its big data systems with clinical precision, according to Khorey. “We spent a lot of time designing this architecture and then picking the [technology] elements that would fulfill each job,” she said. For example, a Hadoop cluster is being used to capture and stage data on its way to a data warehouse; in addition, data discovery tools can be run against the Hadoop data to find rel-evant information for planned analyses.The company didn’t set up a formal commit-tee to evaluate and select the big data technolo-gies, but Khorey said a cross-functional group has been involved in developing the business requirements and technical specifications, as well as assessing the available options. IT is at the head of the table on that process, she said. But physicians and representatives from UPMC’s life sciences operations also have a say on the technology plans and decisions.The end goal is to enable collaborative analysis of genomics data and information on patient outcomes, physician performance, the cost and quality of care and other metrics—all in an effort to improve treatment and care delivery. “This is completely about outcomes, outcomes, outcomes,” Khorey said. “We’re seeking a scientific orientation so we practice [healthcare] based on measurements.”Thus far, UPMC has built the big data infra-structure, captured some initial data sets and run several proof-of-concept projects. Planned next steps include working to prove that the analytical processes can be repeated across dif-ferent data sets and starting to deploy data and self-service analytics tools for use by business analysts, data scientists and other end users. Starter sets of clinical and cost data are due to be made available in June 2014, and Khorey said there will be “constant data landings” over the next few years as the program proceeds. INITIAL BIG DATA DEPOSIT JUST THE START Canadian Imperial Bank of Commerce (CIBC) is also in the early stages of a big data analyt-ics program. The Toronto-based bank is test-ing marketing analytics, fraud detection and financial risk assessment applications; as part
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