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Information Report



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  SELF-SERVICE BI TDWI E-BOOK NOVEMBER 2012 1   DISCOVERY ANALYTICS: POWER TO THE PEOPLE 4   Q&A: HIGH BUSINESS-USER BI ADOPTION RATES 7 MAKING BIG DATA SMARTER 10 ABOUT WCI CONSULTING Sponsored by Presented by   About WCI ConsultingExpert Q&A Big Data 1   TDWI E-BOOK SELF-SERVICE BI Discovery Analytics DISCOVERY ANALYTICS: POWER TO THE PEOPLE The traditional business intelligence (BI) usage model casts business people as passive consumers of information. A recent bottom-up push aims to upend this model. An insurgency of sorts, it casts the business user in the role of analytic explorer. It wants to make analytic discovery a user-driven—not an IT-dictated—practice. Philosophers like to distinguish between facts and values. A fact is what is  ; a value is what we believe ought   to be. This distinction has been characteristic of business intelligence—of all of IT, really—from the very beginning: we might believe   that users ought to be able to serve themselves, but we likewise recognize that that’s not how things are.This distinction explains a BI usage model that casts people—“users”—as passive consumers of information: of preformatted data, predetermined relations, and preformulated insights. A recent bottom-up push in BI aims to upend this model. It casts the user in the role of analytic explorer, and it wants to make analytic discovery a user-driven—not an IT-dictated—practice. Depending on who you talk to, it might not be all that far-fetched.The “discovery” tool set has many components—including, as several vendors point out, a fit-for-purpose analytic database platform. However, the most important (if obvious) component of this tool set is the ubiquitous “discovery” tool, some variant of which is marketed by all the big BI vendors.Although it’s tempting to dismiss this user-driven analytic revolt as still another   would-be disruption that will ultimately fizzle out, many industry experts argue that things are different this time. Most, if not all, of the pieces are in place: the idea of an analytic discovery process that’s driven by end users—by self-servicing business people—has legs. BY STEPHEN SWOYER  About WCI ConsultingExpert Q&A Big Data 2   TDWI E-BOOK SELF-SERVICE BI Discovery Analytics For one thing, the tools themselves are much better. For another, the big BI vendors are also on board: IBM Cognos, Information Builders, Microsoft, MicroStrategy, Oracle, SAP Business Objects, and SAS market analytic discovery tools. Surprisingly, IT   is on board, too: the deep and seemingly insoluble divisions between IT and the line of business no longer seem quite so daunting. So claim proponents of user-driven analytics. “The reality is that most clients are not succeeding with their BI [implementations]. The BI space is one of the worst for end-user adoption,” says Keith Metcalfe, vice president of sales and marketing with WCI Consulting, an SAP BI integrator. “I think the problem is that BI’s been all or nothing for a lot of [IT organizations]: they went the enterprise BI route because that’s what the industry thought they should do, but [IT departments are] realizing that this [enterprise BI] limits their ability to respond [to business people]. They are open to something different.” User-driven analytics is unquestionably “something different.” Positioning for Success  Metcalfe and other industry watchers stress that turning business people into self-empowered analytic explorers isn’t simply a matter of equipping them with discovery tools and turning them loose to explore massive data sets. SAP, for example, markets two discovery-oriented tools: Business Explorer and Visual Intelligence. To the extent that business people are able to successfully use  either   tool, Metcalfe contends, it’s because they’ve been supported and enabled at each step of the way by IT. (SAP says that Visual Intelligence does not require IT support, noting that it’s a desktop product that can run exploration against a local Excel file.)In other words, IT must position   self-service analysts so that they can succeed, not by holding their hands but by doing its part—in the background.For example, a would-be discoverer needs to feel confident about the analyses he or she is producing: when running queries for “customer” or “profit,” both terms must have consistent definitions across all the sources in the query. A business user might not know it, but the success of the analysis depends on data quality and master data management (MDM). Furthermore, because analytic explorers will want to connect to as many data sources as possible, with as little latency as possible, IT can’t simply feed them data from batch-driven ETL processes, either. IT needs to give them fresher data, faster; IT needs agile data integration, a category that includes ETL, data federation, data replication, data quality, and several other technologies. The Performance Problem  On top of all of this, IT needs a fast, responsive analytic repository: it doesn’t want to worry about performance issues, especially if it’s going to permit an analytic explorer to define her own functions. BI adoption isn’t just problematic because the tools are hard to use; it’s problematic because—especially when it comes to the complex queries associated with analytic discovery—the “insights” are a long time coming. Users give up—or go somewhere else.As a case in point, Metcalfe cites the platform with which he’s most familiar: SAP BusinessObjects. SAP provides an end-to-end analytic solution that includes data integration and quality management, high-performance analytical databases, master data management, and intuitive visual discovery tools. If an organization’s concept of “self service” centers solely   on its client BI tools—or, in this case, on a discovery tool such as SAP Visual Intelligence—it’s setting up its users and itself for failure.The bottom line: self service is systemic. It makes discovery possible. However, it isn’t an out-of-the-box or turn-key proposition. It must be supported and enabled.“Everything self service ideally is driven off the semantic layer of the universe within SAP BusinessObjects. That’s a key component to effective self service, because while you can force the user to commit their KPIs, do you really want to do that? What if they’re using different definitions for [something such as] profit? If it’s never been defined properly, you’re just setting them up to fail,” Metcalfe points out.A tool such as SAP Visual Intelligence is designed to make it easier for a nontechnical user to jump into and start exploring data sets, but it isn’t a miracle worker, Metcalfe argues. If  About WCI ConsultingExpert Q&A Big Data 3   TDWI E-BOOK SELF-SERVICE BI Discovery Analytics a business person is to be a successful analytic explorer, IT needs to hold up its end of the deal. “Self service has always been the unicorn that people chase. They’ll buy a tool and they’ll throw it at a business user with no background, no knowledge, no escalation of knowledge, just because it’s supposed to be [a] ‘self-service’ [tool],” he continues. “What they need to do instead is to build out environments for their users that are self-service- ish  . They need to address the [background or enabling] requirements that make it possible for [a user] to take advantage of these [self-service] capabilities, which are built into the [BI discovery] tools. The idea is that you evolve the user to more advanced kinds   of self service, rather than just giving them a tool, giving them data, and saying ‘Go play.’ It’s really all about how [IT organizations] roll the tools to their clients.” Tackling Enterprise-Scale Issues  Industry veteran Cindi Howson, a principal with, thinks this is one respect in which big BI players such as SAP are at an advantage relative to Tableau, QlikTech, and other upstart competitors. Although the newcomers have worked feverishly to recast their products as “enterprise” discovery platforms, SAP and its larger competitors are already   in the enterprise. They’re used (extensively) by IT organizations. They’ve dealt with (or are still dealing with) the thorny issues (e.g., MDM, metadata sharing and consistency, data source diversity and data integration) with which most discovery upstarts are just now getting up to speed. In short, they’ve already done a lot of the prep work that goes into enabling a user-driven analytic effort to succeed. By delivering discovery-oriented end-user analytic tools, SAP and other big players have effectively plugged a big leak in their BI portfolios.“I think [SAP and other BI vendors] definitely have been releasing products to fill this void in their portfolios. We need to be realistic here, however. Visual data discovery has kind of become this new hyped-up form of self service. It’s definitely an improvement, but it’s always a question of how much self service can I realistically enable [in the tool], and also has IT been involved in doing what it needs to do [in the background]?” Howson explains.“The challenge is to address those bottlenecks that require IT intervention, like setting up the business [i.e., semantic] view, so that [users] can take advantage of those [self-service] features. Think about it as a continuum of self service: it’s a trade-off [between] the level of IT involvement and the degree of [self-service] flexibility [the tool] gives the user.”There’s another wrinkle here, stresses industry luminary Claudia Imhoff, a principal with Intelligent Solutions, Inc. Not everybody wants   to be an analytic discoverer: most business users, in fact, simply want to be able to find and consume analytic insights. An end-user-driven approach to analytics must address both   classes of users, she argues. “There are at least two kinds of business users: those that like to produce the BI objects—the reports, analytics, and so forth—and a much larger audience, [which] is the information consumers, the people who have no time, no experience, no desire   to create the report or the analytics: they simply want to be able to consume them,” Imhoff points out. “For self-service BI, we need to be able to support both the producers and the consumers.” Stephen Swoyer  is a Nashville, TN-based freelance journalist who writes about technology.
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