Getting the most out of big data is becoming less challenging as infrastructure has matured with respect to storage capacities, processing power, and connectivity. Still there are important aspects to extracting the benefits from growing data stores.
Dan Pickett, CEO of networking infrastructure experts nfrastructure.com, sees several areas that make a difference between simply collecting data and attaining meaningful ROI from big data efforts. Pickett explains, “Back-end network infrastructure is what it takes to truly experience the benefits of big data. It is not enough to simply collect data from customer use cases, call centers, connected devices or applications – you have to be able to use it strategically for the most possible business benefits and have it positively impact operations and ROI. This cannot be done if the network infrastructure cannot handle the bandwidth required to collect, mine, sort and apply the influx of big data.
“Key aspects of network infrastructure that best support big data collection, storage and its use include taking advantage of virtualization, distribution and management capabilities to create complete end-to-end services that align to deliver business results. Instead of the old model of rigid networks, networks today must be highly flexible and scalable to keep up with changing data demands.
“It is also critical that these networks remain as secure as possible and are up-to-date with rapidly changing compliance issues. Other important aspects of the network that should not be overlooked to make the most out of big data include system monitoring and management, the design and support of business-critical applications and a disaster recovery plan.”
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Tom Davenport is the President's Distinguished Professor of IT and Management at Babson College, a research fellow at the MIT Center for Digital Business, co-founder of the International Institute for Analytics, senior advisor to Deloitte Analytics, author of the new book Big Data at Work and the best-selling Competing on Analytics. He states that, "knowing that there are at least ten ways to tell analytical stories is much more useful than knowing only that you should tell one." In this brief, two page article, he outlines the four key dimensions that determine the type of story you can tell with data and analytics.
