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Big Analytics Redefines Enterprise Decision Making

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There have been many projects encouraging self-service in recent years, but adoption of self-service tools at all levels of organizations “is now so widespread that they can no longer be ignored as guerilla initiatives,” said Jacob Saunders, national solutions director of business intelligence for Neudesic. “Maintaining accessibility, ease of use, and informality while ensuring that underlying data is accurate, timely, and semantically consistent will drive a redefinition of traditional data warehouses into data service layers, and an acceptance of bring-your-own-tool visualization.”

Self-service can take many forms. “For some, it means using predefined reports that can be invoked on a desktop by simply pushing a button,” said Rado Kotorov, chief innovation officer and VP of global product marketing at Information Builders. “For others, it involves a highly dynamic reporting environment that allows them to study data from every angle and on each level of detail. Businesses that can access and integrate the most data—and ensure data quality—are those that will allow a wide range of users to creatively work with it. This is why BI in the enterprise is becoming a big competitive advantage.”

The demand for greater ease of use and self-service is driving new types of tools, observers agree. “Data visualization has become an industry unto itself,” said Akhilesh Tiwari, global head of the SAP Practice at Tata Consultancy Services. “The reason for this is an increased demand for self-service tools available on mobile platforms. As a result, we’re seeing new sets of tools emerge to help business users analyze data themselves without having to depend on IT.

Throw Out the Old Model of Business Analytics

The bottom line is that the time has come to throw out the old model of business analytics, in place for decades, in which a few analysts had powerful desktop tools,while the rest of the organization made do with spreadsheets. “Traditionally, business intelligence was either limited to usage of a few real-time reports or heavily dependent on huge data warehouses,” said Tiwari. The processes were time-consuming, but that is changing now. “Competition is forcing corporations to drive business strategy and objectives through insights derived from data. And this data is often hidden in massive piles of unstructured and structured information, ranging from customer behavior and buying preferences, to product and supply chain performance. So today, immediate and executive-worthy insights have become critical,” said Tiwari.

In many ways, the big data explosion has breathed new innovation into analytics. We are entering a period that can be called the era of “big analytics,” said Haxholdt. “Over the past year, we’ve seen an unprecedented growth of data, not just the volume of data but the variety and speed at which it’s hitting.” Big analytics isn’t just analytics with more data—it introduces new ways of handling and looking at information, and, in turn, calls for new data architectures. “With deeper pools of data, predictive and analytical assessment of data has the potential to be more powerful than ever,” said Mathias Golombek, CTO for Exasol. These new analytics “require a completely new take on database and statistical software.” The old mode—a BI tool sitting on a transactional database or data warehouse—is not adequate for this approach. Computational power combined with real-time processing have given rise to in-memory analytic databases.

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