Posts Tagged Part 2

Taming the Beast

The Enterprise Data Architecture, Part 2

So we know that Big data is a big problem.  What can we do about it and what can we do with it?

First of all, we need to have an understanding of the content of the big data and how we process this information.  Gandomi & Haider (2015) state that “the over all process of extracting insights from big data can be broken down into five stages.  These five stages form the two main sub-processes: data management and analytics. Data management involves processes and supporting technologies to acquire and store data and to prepare and retrieve it for analysis. Analytics, on the other hand, refers to techniques used to analyze and acquire intelligence from big data.”

Image source: Gandomi & Haider, 2015


Once we have a better understanding of the how to process Big Data, how can we efficiently leverage the information we gain?

The growth of data metrics over time in both quantity and quality is nothing new.  Even in the 80s and 90s, articles were published regarding the exponential growth of data and the challenges caused by this growth (Press, 2013).  The challenge that has been continually fought in all those years has been to make the available data meaningful.  Big data, as we have defined it, provides many opportunities for organizations through “improved operational excellence, a better understanding of customer relations, improved risk management and new ways to drive business innovation.  The business value is clear” (Buytendijk & Oestreich, 2016).  Yet, investment in big data tools and initiatives are slow to materialize.

Organizations can incorporate big data initiatives into their existing business process components and artifacts.  This will help establish big data as part of their Enterprise Architecture Implementation Plan and incorporate these new business strategies into the future architecture.  Educating business leaders and stakeholders as to the benefits of big data initiatives will help overcome any concerns they may have (Albright, Miller & Velumani, 2016).  Back in 2013, Gartner conducted a study on Big Data which showed that many leading organizations had already adopted big data initiatives to various degrees, and were starting to use the results in both innovation initiatives as well as sources of business strategy.  Included in their results from that study, they found that “42% of them are developing new products and business models, while 23% are monetizing their information directly” (Walker, 2014).

This is where Enterprise Architecture comes into play.  EA is uniquely equipped, with it’s holistic view of strategy, business and technology, to not only create a strategic plan to address the potential big data opportunities, but to create a road map which positions organizations to improve operations, enable growth, and foster innovation.  “Organizations that leverage EA in their big data initiatives are able to identify strategic business goals and priorities, reduce risk and maximize business value” (Walker, 2014).

Image source: Walker, 2014


Through the various efforts of EA, new technologies and new approaches to processing, storing and analyzing Big Data, organizations are gradually finding ways to uncover valuable insights.  Obviously, leading the charge are leaders such as Facebook, LinkedIn and Amazon, but many other companies have joined the effort as well.  “From marketing campaign analysis and social graph analysis to network monitoring, fraud detection and risk modeling, there’s unquestionably a Big Data use case out there with your company’s name on it” (Wikibon. 2012).  This infographic below was put together by Wikibon to show how various business leaders have overcome their challenges and have tamed the Big Data beast.




Albright, R., Miller, S. & Velumani, M. (August 2016).  Internet of Things & Big Data.  EA872, World Campus.  Penn State University.

Buytendijk, F. & Oestreich, T. (2016) Organizing for Big Data Through Better Process and Governance. (G00274498). Gartner.

Gandomi, A. & Haider, M. (2015). Beyond the Hype: Big data concepts, methods, and analytics, International Journal of Information Management, Volume 35, Issue 2, April 2015, Pages 137-144, ISSN 0268-4012,

Press, G. (2013) A Very Short History of Big Data. Retrieved from

Walker, M. (2014). Best Practices for Successfully Leveraging Enterprise Architecture in Big Data Initiatives (G00267056). Gartner.

Wikibon. (2012). Taming Big Data. Wikibon. Retrieved September 20, 2017 from


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Service Oriented Anarchy

The Enterprise Application Architecture, Part 2

SOA.  Sons of A….   I mean Service-Oriented Architecture.  What exactly is this?  And what’s the big deal?

In Part 1, I talked about Software as a Service, and mentioned Platform as a Service as well as Infrastructure as a Service.  Each of those strategies is taking a commonly used aspect of technology and converting it to a service that can be accessed over the Internet.  SaaS, as I mentioned in Part 1, is about delivering an application over the Internet to an end consumer, or simply, a distribution method. SOA, on the other hand, is architectural approach for application development, or a development method.  By converting all parts of a system to act as independent services, then those same pieces can be used by multiple applications, all having a common framework for communication, referred to as a service bus.  These pieces become the building blocks for a larger system, enabling the parts to easily interact with one another.

IBM uses a combination of multiple definitions to elaborate on SOA :

The more you search around the Internet, the more varied and diverse definitions you find for SOA.  As Martin Fowler, software developer, author and Chief Scientist for ThoughtWorks (a technology consulting company), says in his 2005 article Service Oriented Ambiguity:

  • For some SOA is about exposing software through web services. This crowd further sub-divides into those that expect the various WS-* standards and those that will accept any form of XML over http (and maybe not even XML).
  • For some SOA implies an architecture where applications disappear. Instead you have core services that supply business functionality and data separated by UI aggregators that apply presentations that aggregate together the stuff that core services provide.
  • For some SOA is about allowing systems to communicate over some form of standard structure (usually XML based) with other applications. In it’s worse form this is “CORBA with angle brackets”. In more sophisticated forms this involves coming up with some form of standard backbone for an organization and getting applications to work with this. This backbone may or may not involve http.
  • For some SOA is all about using (mostly) asynchronous messaging to transfer documents between different systems. Essentially this is EAI without all the expensive EAI vendors locking you in.

So do we have a better understanding of SOA?  Or is it chaos and anarchy?  I think his definition by Oracle was simple enough for a begining understanding: “A service-oriented architecture is a way of sharing functions (typically business functions) in a widespread and flexible way.”


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Disruptors set to Maximum….

Digital Disruption, Part 2

(for the non Star Trek fans in the room, Klingons typically use a weapon called a disruptor)


So what do we mean when we say that technology is disruptive?  A simple definition, as provided by Technopedia, is “Disruptive Technology refers to any enhanced or completely new technology that replaces and disrupts an existing technology, rendering it obsolete.”  Disruptive technology can be hardware, software, networks, new processes and any combination of those as well.  Typically, the success of a disruptive technology is unexpected.  This is due to the fact that most disruptive technologies lack refinement, have some performance problems, are only known to a limited audience and may not yet have practical applications at the time of their creation (Christensen, 1997).   It’s not until later, once some of those issues are addressed, that the existing technology starts to be impacted and potentially threatened.

Another aspect of disruptive technology is that once it starts being adopted, it has the potential to transform the way we live and work, enable new business models, and provide a way for new companies to upset or disrupt the established order (Manyika, 2013).  At first glance, that may seem exaggerated, yet consider this list of technologies that were/are considered as disruptive at their time in history:

  • Steam Power
  • Assembly Line Manufacturing
  • Email
  • World Wide Web / Internet
  • Personal Computer
  • Mobile Phones / Smartphones
  • Video Streaming
  • Cloud Technology

There are hundreds more examples of technology that we take for granted today, which when it became public, it may not have been well accepted.  I doubt any of the original creators of these ideas had a concept of how great an impact their creation would have on the future society.  And the technologies that were replaced were firmly entrenched as the “leaders” of their realm.  Yet, can we image a world today without any of these technologies?   And if these technologies alone had such a huge impact on our society, then what is in store in the future?  There are emerging technologies that we see today such as 3D printing, self-driving vehicles, robotics & AI, the Internet of Things, and virtual reality.  We can already begin to see the impact these technologies have on consumers and companies are starting to be impacted as well.

What’s the next big thing around the corner and how much will it change our society?  And how do we adopt these new technologies?



Christensen, C. (1997). The Innovator’s Dilemma: Management of Innovation and Change. Harvard Business School Press.

Manyika, J., Chui, M., Bughin, J., Dobbs, R., Bisson, P., & Mars, A. (2013). Disruptive Technologies: Advances that will transform life, business and the global economy.  McKinsey Global Institute.  Retrieved August 29, 2017 from



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