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  • September 1, 2015

Evolution of Your Information Architecture

A Little Background

Information quality is the single most important benefit of an information architecture. If information cannot be trusted, then it is useless. If untrusted information is part of an operational process, then the process is flawed and must be mitigated. If untrusted information is part of an analytical process, then the decisions will be wrong. Architects work hard to create a trustworthy architecture.

Furthermore, most architects would agree that regardless of data source, data type, and the data itself, data quality is enhanced by having standardized, auditable processes and a supporting architecture. In the strictest enterprise sense, it is more accurate to say that an information architecture needs to manage ALL data – not just one subset of data.

Big Data is not an exception to this core principle. The processing challenges for large, real-time, and differing data sets (aka Volume, Velocity, and Variety) do not diminish the need to ensure trustworthiness. The key task in Big Data is to discover, ‘the value in there somewhere.’ But we cannot expect to find value before the data can be trusted.

The risk is that treated separately, Big Data can easily add to the complexity of a corporate IT environment as it continues to evolve through frequent open source contributions, expanding cloud services, and true innovation in analytic strategies.

Oracle’s perspective is that Big Data is not an island. Nearly every use case ultimately blends new data and data pipelines with old data and tools, and you end up with an integration, orchestration, transformation project. Therefore, the more streamlined approach is to think of Big Data as merely the latest aspect of an integrated enterprise-class information management capability.

It is also important to adopt an enterprise architecture approach to navigate your way to the safest and most successful future state. By taking an enterprise architecture approach, both technology and non-technology decisions can be made ensuring business alignment, a value centric roadmap, and ongoing governance. Learn more about Oracle’s EA approach here.

A New White Paper

So, in thinking about coordinated, pragmatic enterprise approaches to Big Data, Oracle commissioned IDC to do a study that illustrates how Oracle customers are approaching Big Data in the context of their existing and planned larger enterprise information architectures. The study was led by Dan Vesset, head of business analytics and big data research at IDC, who authored the paper, titled Six Patterns of Big Data and Analytics Adoption: The Importance of the Information Architecture, and you can get it here.

Highlights - Three Excerpts from the Paper

Patterns of Adoption

The paper explores six Big Data use cases across industries that illustrate various architectural approaches for modernizing their information management platforms. The use cases differ in terms of goals, approaches, and outcomes, but they are united in that each company highlighted has a Big Data strategy based on clear business objectives and an information technology architecture that allows it to stay focused on moving from that strategy to execution.

Case Industry Project Motivation Scope
1 Banking Transformational modernization Transform core business processes to improve decision-making agility and transform and modernize supporting information architecture and technology.
2 Retail Agility and resiliency Develop a two-layer architecture that includes a business process–neutral canonical data model and a separate layer that allows agile addition of any type of business interpretation or optimization.
3 Investment Banking Complementary expansion Complement the existing relational data warehouse with a Hadoop-based data store to address a near-real-time financial consolidation and risk assessment.
4 Travel Targeted enablement Improve a personalized sales process by deploying a specific, targeted solution based on real-time decision management while ensuring minimal impact on the rest of the information architecture.
5 Consumer Packaged Goods Optimized exploration Enable the ingestion, integration, exploration, and discovery of structured, semi-structured, and unstructured data coupled with advanced analytic techniques to better understand the buying patterns and profiles of customers.
6 Higher Education Vision development Guarantee architectural readiness for new requirements that would ensure a much higher satisfaction level from end users as they seek to leverage new data and new analytics to improve decision making.



Copyright IDC, 2015

Oracle in the Big Data Market

Oracle offers a range of Big Data technology components and solutions that its customers are using to address their Big Data needs. In addition, the company offers Big Data architecture design and other professional services that can assist organizations on their path to addressing evolving Big Data needs. The following figure shows Oracle’s Big Data Platform aligned with IDC’s conceptual architecture model.


Copyright IDC, 2015

Lessons Learned

Henry David Thoreau said, "If you have built castles in the air, your work need not be lost; that's where they should be. Now put the foundations under them." The information foundation and architecture on which it is based is a key building block of these capabilities. In conducting IDC's research through interviews and surveys with customers highlighted in this white paper and others, we have found the following best practices related to the information architecture for successful Big Data initiatives:

  • Secure executive sponsorship that emphasizes the strategic importance of the information architecture and ensure that the information architecture is driven by business goals.
  • Develop the information architecture in the context of the business architecture, application architecture, and technology architecture — they are all related.
  • Create an architecture board with representation from the IT, analytics, and business groups, with authority to govern and monitor progress and to participate in change management efforts.
  • Design a logical architecture distinct from the physical architecture to protect the organization from frequent changes in many of the emerging technologies. This enables the organization to maintain a stable logical architecture in the face of a changing physical architecture.
  • Consider the range of big use cases and end-user requirements of Big Data. Big Data is not only about exploration of large volumes of log data by data scientists.
  • Even at the early stages of a project when evaluating technologies, always consider the full range of functional and nonfunctional requirements that will most likely be required in any eventual deployment. Bolting them on later will drive costs and delays and may require a technology reevaluation. This is yet another reason why an architecture-led approach is important.

Oracle also has a variety of business and technical approaches to discussing Big Data and Information Architecture. Here are a few:

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