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The field of business intelligence and analytics keeps evolving, and keeps booming in new ways. Never before has there been so expansive a choice of solutions, and so many ways to gain insights into customers, sales, markets, and processes.
The challenge is packaging the information being generated from all corners of the enterprise and beyond as rapidly as possible into formats and presentations that can guide decision makers at multiple levels. The challenge in today’s BI and analytics initiatives is delivering real-time data in self-service mode to anyone who needs it.
The business need is pressing—decision makers need up-to-the-minute situational awareness in a volatile global economy. “With massive amounts of complex, fast-moving data, companies are struggling to determine the relevance of various data to specific business problems,” said Malene Haxholdt, SAS senior manager for global analytics strategy. “We expect companies to know and react to our needs instantly, no matter if we are dealing with a bank or our plumber.”
Organizations have been collecting data for years, but never before has there been such urgency to have it immediately available. “This data has typically been historic information—things that can tell us what has happened,” said Pradeep Amladi, SAP vice president of marketing for discrete manufacturing and energy and natural resource industries. “Businesses aren’t interested in what was going on weeks or even days ago; they want to know what’s happening now and what’s needed tomorrow.”
The Internet of Things (IoT) is accelerating this drive to real-time analytics, he continued. “As products and machines get smarter—embedded with intelligent sensors—decision makers at all levels are getting more accurate, real-time understanding of customer utilization and behavior. This real-time analysis enables us to identify new and innovative services from analyses of data.” An example, Amladi said, is operational data providing an up-to-the minute picture of what’s happening in a factory. Real-time analysis can assist in “preventative maintenance or replacements based on propensity for plant asset breakdown, just-in-time product upgrades, and recommendations on how to optimize product utilization.”
The need for real-time analytics has changed the BI market entirely. “Traditional BI tools have been constrained by the need to do ETL (extract, transform, load) and to have a pre-defined schema,” said Tapan Bhatt, vice president, business analytics for Splunk. Both of these requirements introduce latency in analytics. “With real-time analytics, businesses can respond much faster to refine a marketing campaign, improve customer experience, and prevent security threats.”
The BI and analytics market is also being roiled by a dramatic movement toward self-service systems. The ideal is that decision makers—even those with relatively scant programming or statistical analysis skills—should be able to pull their own data sources and assemble their own dashboards or reports. “The results of both descriptive BI and predictive analytics need to be easily available to more people in an organization,” said Haxholdt. “Those people, often business people without the skills of a data scientist, need constant and easy access to the information in what is becoming an always-on, always connected world.”