BI and Analytics Reach the Tipping Point

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However, Thiagarajan of NTT Data cautioned, “In the context of BI and analytics, cloud should be well thought-out. Don’t stereotype cloud as a panacea for all maladies. The nature of the solution and its impact on the business is more critical than where the solution is being hosted. All the advantages of the cloud in terms of reduced footprint, enhanced access, and better reach have to be weighed carefully in the context of what the business wants.”

IoT is IT

The Internet of Things also looms large as a force to be reckoned with, though many enterprises are still trying to assess what it actually means and requires. The IoT “is not just another buzzword; it’s another point of data for analytics to integrate with other key metrics,” according to Guy Yehaiv, CEO of Profitect, a provider of analytics for retailers. “For example, prescriptive analytics solutions use data points generated from beacon technology. Whether it’s through the use of online consumer dwell time, traffic, or congestion, it’s important to consider data generated from technology like beacons to develop accurate figures.”

The Internet of Things phenomenon will move from the initial hype stage to many more real-world use cases and success stories, predicted Anand Venugopal, head of product for StreamAnalytix at Impetus Technologies, a product development, software services, and solutions company focused on business data analysis. IoT is rapidly evolving in a number of ways, including the deployment of “electronic sensors which constantly measure their subject environment or parameters and emit data wirelessly . . . [and] wireless and data networks to carry the data from field-based sensors to first-level aggregation points and then to the back-office data center,” Venugopal said. He also predicted the rise of “streaming analytics platforms to decode and analyze the data in real time and initiate actions based on insights gained, and data stores to rapidly ingest large amounts of these new data feeds.”

The movement toward data as a service may help promote greater adoption of IoT. “As companies become content creators through data creation, DaaS solutions will be able to categorize both structured and unstructured data and provide it as a service to other companies or departments,” observed FirstRain’s Herscher.

Ultimately, as IoT grows and becomes essential to enterprises, there will be more work for data managers and professionals, and additional investments will need to be made in new types of data management systems. IoT solutions “need to be able to collect, store, and process the data being generated by all of the things—sensors, devices, endpoints,” said Bob Muglia, CEO of Snowflake Computing, a cloud data warehousing company. “For example, collecting the data from all of the sensors and systems in modern vehicles has become much easier due to offerings that collect, cleanse, and stream the data from cars to a central repository.”

Death of the Power User

One pronounced trend is the end of analytics being the exclusive domain of “power users”—technically proficient analysts and specialists who know how to slice and dice data to build their own reports. “Being a data-driven company means that you want everyone to be able to use analytics, not only power users,” said Tomer Ben Moshe, CEO of CoolaData, a provider of big data analytics as a service.

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