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Agile Enterprise Architecture for Big Data

by Zak Cole       • September 29, 2016

Big Data is a huge enabler for business. It provides business leaders and analysts with a depth of information and insight that had previously been impossible to understand.

But for many businesses, this depth isn’t always as inviting as one might hope and so the scope of big data, often becomes a catch 22. Big data’s greatest asset – namely, masses of information – can easily become it’s biggest challenge. Without proper direction, useful information in big data is actually more barren than its name suggests.

Yes, there is a lot of information there, but without the proper approach, sifting through the useful information can undo much of the productivity big data seeks to improve.

This is where Enterprise Architecture comes in …

Enterprise Architecture (EA) helps organizations identify and capitalize on new business opportunities uncovered by this new influx of information, by acting as the guiding rope for the strategic changes required to handle it. EA helps facilitate big data processing, and helps uncover and prioritize exactly which data can benefit the organization.

Enterprise Architecture has already changed a lot over the last decade or so, and architects are now expected to be far more business outcome orientated, and meet disruptions and opportunities head on, rather than acting primarily on optimization and standardization.

With big data, the role of Enterprise Architecture needs revising again. Too much happens too quickly for the old idea of Enterprise Architecture, one that involves carefully perfecting projects and pouring over detail, to still apply. Big data benefits from the “Just Enough” and “Just in Time” approach to EA, and that’s why …

Big Data requires an Agile approach

Big Data is a product of the mass information, digital business age, whereby opportunities are more plentiful, but have much smaller windows in which they can be capitalized upon.

The constantly changing landscape of modern business is directly reflected in big data and EAs will often have to react in real-time as the variables that dictate the data continue to evolve.

David Newman, research vice president at Gartner, spoke on this very topic. “For the EA practitioner, the balance shifts from a focus on optimization and standardization within the organization, to lightweight approaches,” he said.

“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.”

Therefore, just having an Enterprise Architecture initiative isn’t necessarily enough to properly leverage big data. EAs that are yet to focus on agility won’t find as much success as those that have.

One of the key best practices in transitioning to a more Agile EA initiative, and maintaining this Agility is heavily linked with the perception of EA itself. To truly be effective as an agile arm of the business that meets change and disruption head on, EA must step up from building business and IT architecture models to deliver business focused outcomes.

This is something that analysts and influencers all seem to agree on, as many have championed the business outcome approach to Enterprise Architecture now, for some time.

This shift from IT-system focus to business focus, arguably happened when the concept of a Vanguard Enterprise Architect was introduced, making a clear distinction between Foundational EA (responsible for ensuring “business as usual”) and the innovation focussed Vanguard EA.

In fact, Forrester even placed “assisting the business in opportunity recognition” at number one, in their list of ways enterprise architects lead their organization’s thinking.

One way in which Enterprise Architecture can seek to properly leverage big data to recognize new opportunities is by using a business capability map. Business capability maps can make it far easier to extract the relevant data, when the raw data itself is too large to effectively digest.

Enterprise Architecture can also indicate when an organization’s own data isn’t quite big enough. Often, organizations find themselves held back by inter-departmental walls and silos. Enterprise Architecture can help point out these areas where data sharing is lacking, and work on bridging the gap.

This makes the data provided in big data far more complete, and in turn, more useful in the decision making process.

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