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In an age where data is king, the shift to self-service analytics seems threatening and even out of control for IT leaders—but it doesn’t have to be.
A new implementation model, anchored by a real partnership between IT leaders and business users, calls for IT to own the center of operations, security and governance, data acquisition, maintenance and provisioning.
Because the best analytical insights come from user-generated dashboards running on top of IT-managed infrastructure, this fundamental shift in work methodology will put information directly in the hands of business users who know what questions to ask, how to interpret the results, and even what follow-up questions to explore.
In this paper, you’ll find an in-depth roadmap for scaling self-service reporting at your organization.
Read this report to learn about:
We've also pulled out the first several pages of the whitepaper for you to read. Download the PDF on the right to read the rest.
Anyone in the world of data knows the signs. The amount and variety of data makes the concept of one-stop data warehouses obsolete, even as many companies still struggle to build their first data warehouse. Business users, adapted to user-friendly consumer technologies, demand the ability to work directly with their data. Technologies exist that allow interactivity and manipulation of data in ways that were unimaginable just years ago.
At first, this shift seems threatening, even “out of control.” It need not be. In fact, it can reduce the crushing workload of dashboard requests so that IT can focus on large and strategic issues. This elevates IT from the role of dashboard factory to architect and steward of the company’s assets. And it frees business users from the slow, deadening cycle of change request and response. Self-service analytics can yield huge business and employee dividends while protecting data assets and providing the “best” source of truth out to the enterprise.
But even with new technologies that empower business users, companies sometimes fail in their analytics strategies. New approaches demand a new methodology. We look to proven agile development and deployment methods that move as quickly as the changing requirements. We look to a methodology that allows IT and business to work together as partners. We look to a lighter process that allows people to exercise their natural creativity and curiosity. This is Tableau Drive: a methodology that draws from agile methods and is informed by the most analytically-minded companies in the world.
Facebook is a company that has achieved mass adoption of analytics. At Facebook, employees try to inform every decision with data. Business people are responsible for doing analysis. IT is responsible for managing and securing data. Each team respects what the other brings to the table. Both are advancing Facebook’s ability to answer questions and, in doing so, adding tremendous business value.
Namit RaiSurana, the Data Product Manager at Facebook, says there is “never a dependency on any of us to answer these questions,” he said. “Users can discover for themselves” what their data has to offer. “We are opening up Tableau to the entire company,” he said. Business intelligence dashboards are possible “without having to spend weeks programming.”
This is all made possible by what business users don’t see: the data sources that have been set up and managed by IT. This is a key concept: to make the most of a self-service analytics strategy, you need highly usable, easy to access data. The best analytics implementations are user-created dashboards running on top of IT-managed infrastructure.
People within organizations have traditionally accessed data via static reports from enterprise applications and business intelligence platforms maintained by IT departments. These systems, predominantly designed and built in the 1990’s, are generally heavy, complex, inflexible and expensive. As a result, business users are forced to depend on specialized resources to operate, modify and maintain these systems. This creates a divide between users seeking insight and technical specialists lacking business context. This divide limits the usefulness of these legacy systems. Because most business users lack the time and skills to bridge the divide, they simply didn’t use the analytics systems provided by their companies.
As a result, many knowledge workers today rely on spreadsheets as their primary analytical tool. Much of this was a failing of technology and much of it was a failing of process.
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