Enterprise architecture (EA) practitioners have a major role in ensuring their organizations maximize the business opportunities posed by big data, according to Gartner.
Big data makes organizations 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 organization: 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 optimization and standardization within the organization, to lightweight approaches that focus on harmonization and externalization 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 organization.
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
Organizations 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 behaviors and force more cross-team data sharing.
Impact: Big data exposes talent gaps, introduces new interdisciplinary roles, and forces organizations to attract and retain data-savvy business specialists and managers with deep analytical skills
A major challenge is how organizations 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 organization 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 analyzing, visualizing, linking and managing big datasets
A practitioners must help their organization understand how best to design and implement big data solutions. Careful planning must be undertaken to determine the best tools and techniques for analyzing complex datasets. These include skills in statistics, machine learning, natural-language processing and predictive modeling. In addition, practitioners must help teams understand how to use big data visualizations 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.