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Gartner Says Big Data Disruptions Can Be Tamed With Enterprise Architecture
Analysts to Discuss the Role of Enterprise Architecture in the Age of Big Data at Gartner Enterprise Architecture Summit 2012, 14-15 May 2012 in London, UK(PresseBox) ( Egham, UK, )
Big data makes organisations smarter and more productive by enabling people to harness diverse data types previously unavailable, and to find previously unseen opportunities. However, Gartner analysts said big data poses big challenges as well - and that's where enterprise architects can help. As navigators of strategic change, the task for EA practitioners is to chart the right course for big data across the most critical dimensions of the organisation: business, culture, talent and technology.
"Traditional approaches to EA are significantly impacted by big data," said David Newman, research vice president at Gartner. "For the EA practitioner, the balance shifts from a focus on optimisation and standardisation within the organisation, to lightweight approaches that focus on harmonisation and externalisation across the broader enterprise ecosystem. Big data disrupts traditional information architectures - from a focus on data warehousing (data storage and compression) toward data pooling (flows, links, and information shareability). In the age of big data, the task for the EA practitioner is clear: Design business outcomes that exploit big data opportunities inside and outside the organisation.
Gartner has identified four critical impacts of big data, and how enterprise architects can address these issues:
Impact: Big data enables decision makers to spot patterns quickly across different data types, but requires a data-savvy business strategy to achieve competitive advantage
Enterprise architects should educate leaders about potential big data opportunities now readily available through start-small, cost-effective analytics and pattern recognition tools and techniques, but also explain the risk factors (such as data privacy, regulatory and legal challenges). Practitioners should also explore the increasing number of public datasets now available through open APIs, and use these for sentiment analysis (e.g., mining social media feeds), location-based services (using publicly available telemetry data) or to design context-aware applications.
Impact: Big data opportunities expose internal silos that leaders must address through proper incentives and metrics which encourage data sharing and improve trust
Organisations may have the best technology and the best people. But if the internal culture is plagued by silos and lack of data sharing, they are less likely to achieve success with big data. Addressing cultural challenges requires creating the right incentives to build trusted sources of enterprise information. Enterprise architects should conduct stakeholder analyses to identify the cultural roadblocks to data sharing, and prepare mitigation strategies and communications that overcome perceived obstacles. For instance, EA practitioners can advocate open innovation efforts that will enable customers to participate directly in product development, which will begin to overcome silo-centric behaviours and force more cross-team data sharing.
Impact: Big data exposes talent gaps, introduces new interdisciplinary roles, and forces organisations to attract and retain data-savvy business specialists and managers with deep analytical skills
A major challenge is how organisations will attract and retain the right talent that exploits big data. Among the most sought-after role is the data scientist - a role that combines domain skills in computer science, mathematics and statistical expertise. EA practitioners can help their organisation address this challenge by producing a resource planning deliverable that identifies big data skill gaps across business teams. Practitioners should also assess resource needs among information infrastructure teams, and identify technical gaps when supporting big data solutions.
Impact: Big data requires technology specialists to acquire and apply tools, techniques and architectures for analysing, visualising, linking and managing big datasets
EA practitioners must help their organisation understand how best to design and implement big data solutions. Careful planning must be undertaken to determine the best tools and techniques for analysing complex datasets. These include skills in statistics, machine learning, natural-language processing and predictive modelling. In addition, practitioners must help teams understand how to use big data visualisations techniques, such as tag clouds, clustergrams, history flows, animations and infographics. Teams should use low-cost, open-source tools in early pilots to demonstrate the feasibility of big data projects.
Additional information is provided in the Gartner report "Big Data Disruptions Tamed With Enterprise Architecture." The report is available on Gartner's web site at http://www.gartner.com/....
Gartner analysts will discuss how to manage EA in the nexus of change at the Gartner Enterprise Architecture Summit 2012, taking place from 14 to 15 May in the UK, London, and in the US from 23 to 24 May in National Harbor, MD. For further information about the Summit in London, please visit www.gartner.com/eu/ea. For further information about the Summit in Maryland, please visit www.gartner.com/us/ea Additional information from the event will be shared on Twitter at http://twitter.com/... and using #GartnerEA.
About Gartner Enterprise Architecture Summit 2012
A unique confluence of disruptive trends from the nexus of cloud, social, big data and mobile technologies provide an unprecedented opportunity for EA practitioners to deliver business value from EA differently than ever before. The Summit can help organisations navigate the nexus of change to deliver business value, growth and transformation, but also master their core EA competencies that are essential for EA success. Gartner analysts will also discuss the future of EA and how to leverage leading edge EA practices in the organisation.
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