Modernizing Big Data Platforms
Many organizations are re-examining and evolving their big data strategies and systems to further imbue the business with data-derived insights.
Organizations today face exploding volumes of information generated both within and outside their walls. Harnessing the power of big data can be critical to business competitiveness, and CIOs frequently receive requests from business-line leaders for faster, more timely, and more complete information that can enable insights and strengthen enterprisewide decision-making. These factors highlight the need for cohesive strategy and powerful technical capabilities for gathering, storing, and analyzing structured and unstructured data sets.
A strategic approach to big data platform modernization can help organizations reduce infrastructure costs and achieve greater capabilities, flexibility, and collaboration. This can better position them to curate and process disparate data streams to deliver real-time analytics insights, said Ashish Verma, a managing director at Deloitte Consulting LLP who leads big data analytics, innovation, and architecture initiatives, during a talk at Deloitte’s 2017 Analytics Symposium. Accordingly, many CIOs are evolving their current big data ecosystems, which may involve revisiting current-state systems and moving some analytics workloads into the cloud.
CIOs—who might look to derive insights from streaming internet of things data, for instance—are on the front lines of establishing big data platform strategies, but the desire for deep insights enabled by modernized platforms goes well beyond the IT department. CFOs at large banks, for example, might want to improve spending, pricing, margin erosion, and vendor management analysis. CMOs at top retailers, meanwhile, may seek a single view of their customers and prospects to better analyze brand sentiment and customer profitability, and to optimize e-commerce and order management.
Despite the myriad possibilities big data introduces, the sheer volume of information means many organizations struggle to accurately predict information storage needs and gauge the computing power required to crunch ever-expanding analytics workloads. Moreover, existing on-premise infrastructure often cannot scale storage and analysis to the degree demanded in today’s business environment. It can also be difficult—not to mention cost-prohibitive—to possess in-house all the capabilities needed to extract strategic business value from often-disjointed data sets.
There are many offerings designed to help organizations reimagine their current-state systems to handle and derive faster insights, but determining the most appropriate approach calls for upfront self-examination, Verma said.
