The Right Big Data Infrastructure Is Essential to Effective Analytics

By: Chris Nerney| - Leave a comment


Big data and analytics offer enterprises the opportunity to gain genuine and actionable insights into their customers, products and business processes that can result in revenue growth, higher market share and greater efficiency.

Without the right IT infrastructure in place, however, data initiatives can fail to deliver the benefits anticipated by enterprise decision-makers. As consulting firm Bain Insights wrote in Forbes, “Companies can’t reap the full benefits of their analytics efforts unless their technology organization is up to the challenge of managing the data that makes it possible.”

While CIOs may recognize the importance of a robust big data infrastructure, a Bain survey from 2015 showed that nearly six in 10 global enterprise IT leaders doubt the ability of their existing technologies to leverage data for business insights.

For CIOs and other senior IT leaders whose enterprises still rely on legacy systems, this lack of confidence simply reflects reality: Older IT equipment and processes weren’t built to handle the exponential volume of data generated in recent years, particularly by apps and mobile devices. They are even less suited for the avalanche of data expected in the near future from the IoT, which Gartner predicted last November would jump from 6.4 billion connected “things” in 2016 to nearly 21 billion in 2020.

To ensure that enterprise big data infrastructure can support business-driven analytics, The CIO should realistically assess existing IT assets and processes to determine whether upgrades are necessary. Here are some of the major IT infrastructure considerations every CIO needs to take into account when assessing what is required to support big data programs.

Scalable, Smart and Cost-Effective Storage

IBM Research estimates that 90 percent of existing data was created in the past two years. This exponential growth can quickly overwhelm even the largest premises-based data warehouse. To overcome legacy storage limitations, many enterprises are turning to cloud-based solutions that can scale to meet data storage demands. Cloud storage also enables enterprises to avoid large capital investments and typically uses a utility-style pricing model, both of which can result in lower long-term storage costs.

Storage not only must be scalable and cost-effective; it also must be smart. Enterprises seeking premises-based storage solutions to handle the data deluge need modern tools such as virtualization and real-time data compression.

Data Management and Quality

Storage capacity and scalability may be the first prerequisites for a big data infrastructure that can support enterprise analytics initiatives, but without tools for effectively managing and providing access to that data, storage has little long-term influence when companies want to analyze that information.

Because the vast majority of data generated from digital sources is unstructured, enterprises need to collect, store and analyze this data in a way that extracts business value in order to remain competitive in the digital economy. CIOs should assess whether their legacy infrastructure and current software tools are able to deliver cost-effective data management and sufficient data quality.

Data Accessibility at the Speed of Business

Data analytics is no longer the sole province of IT and the data science team. Line-of-business employees are increasingly accessing data to analyze marketing campaigns, sales trends, process improvement initiatives and more. Enterprise IT leaders must ensure that the right people have access to the data they need, when they need it. Cloud-based storage typically provides more flexible, fast and secure access to data than on-premises data centers.

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About The Author

Chris Nerney

Freelance Writer

Chris Nerney writes about technology, science and health care for a number of websites and enterprises. He has written extensively about mobile technology, cloud computing, big data and analytics, health care finance and IT, data centers and space technology. His work has appeared in Computerworld,, Data-Informed, Revenue Cycle Insights, Network World and numerous other sites. Chris also writes technology white papers and is an experienced editor.

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