Vendor Highlight Archive

Resolving Data Disruption 

Enterprise architecture (EA) practitioners have a major role in ensuring their organisations maximise the business opportunities posed by big data. According to market analyst, Gartner, big data makes firms productive by enabling people to harness diverse data types previously unavailable. IT also helps firms explore unseen opportunities.

 However, with big data comes 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,” says David Newman, research vice president at Gartner.

“For the EA practitioner, the balance shifts from a focus on optimisation and standardisation 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 using (data storage and compression) toward data pooling (flows, links, and information shareability).

In the era 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:

Big data enables decision makers to spot patterns quickly across different data types. However, it requires a data-savvy business strategy to achieve competitive advantage.

Enterprise architects should educate leaders about potential big data opportunities through start-small, cost-effective analytics and pattern recognition tools and techniques.

They should also explain the risk factors (such as data privacy, regulatory and legal challenges).
Practitioners should also explore the increasing number of public datasets 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.

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.

However, 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. This will overcome silo-centric behaviors and force more cross-team data sharing.


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. EA practitioners can help their firms 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.


Big data requires technology specialists to acquire and apply tools, techniques and architectures for analysing, visualising, linking and managing big data sets.

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 data sets.

These include skills in statistics, machine learning, natural-language processing and predictive modeling.

Furthermore, practitioners must help teams understand how to use big data visualisations techniques, such as tag clouds, cluster grams, history flows, animations and infographics.

Teams should use low-cost, open-source tools in early pilots to demonstrate the feasibility of big data projects.


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