Tuesday Sep 01, 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.

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.

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. 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, non-technology-oriented 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:

Thursday May 21, 2015

Big Data and the Future of Privacy - paper review (Part 3 of 3)

An October 2014 paper from the law school at Washington University in St. Louis makes the case that privacy rules are really information management rules and that privacy can and must be secured in our big data future.

[Read More]

Tuesday May 19, 2015

Big Data and the Future of Privacy - paper review (Part 2 of 3)

An October 2014 paper from the law school at Washington University in St. Louis makes the case that privacy rules are really information management rules and that privacy can and must be secured in our big data future.

[Read More]

Big Data = Bigger Responsibility

“Companies of all sizes and in virtually every industry are struggling to manage the exploding amounts of data,” says Neil Mendelson, vice president for big data and advanced analytics at Oracle. “But as both business and IT executives know all too well, managing big data involves far more than just dealing with storage and retrieval challenges—it requires addressing a variety of privacy and security issues as well.”

In a talk at the Technology Policy Institute’s 2013 Aspen Forum, Federal Trade Commission chairwoman Edith Ramirez described some big data pitfalls to be avoided. Though many organizations use big data for collecting non-personal information, there are others that use it “in ways that implicate individual privacy.”

With big data, comes bigger responsibility. A new joint Oracle and MIT Technology Review paper drills into these pitfalls and how organizations can mitigate them using Oracle's Big Data solution.  

Get the paper, Securing the Big Data Life Cycle and learn more here.

Thursday May 14, 2015

Big Data and the Future of Privacy - paper review (Part 1 of 3)

An October 2014 paper from the law school at Washington University in St. Louis makes the case that privacy rules are really information management rules and that privacy can and must be secured in our big data future.

[Read More]

Tuesday Apr 21, 2015

Oracle Named World Leader in the Decision Management Platform

Oracle Real Time Decisions is recognized as first to market and market share leader by IDC for the Decision Management platformBy operationalizing big data analytics at the point of interaction, organizations of all sizes can feel confident that they can see real and impressive business benefits from these solutions today. 


[Read More]

Monday Apr 20, 2015

Big Data Privacy and the Law

At Strata+Hadoop (Feb 2015), two attorneys from Kelley Drye & Warren LLP make recommendations on dealing with the legal implications of working with personal data.

[Read More]

Monday Apr 06, 2015

Announcing Oracle Data Integrator for Big Data

Proudly announcing the availability of Oracle Data Integrator for Big Data. This release is the latest in the series of advanced Big Data updates and features that Oracle Data Integration is rolling out for customers to help take their Hadoop projects to the next level. [Read More]

Tuesday Mar 31, 2015

Big Data and Privacy

Personal data is a component of many big data use cases. What should be the guiding principle for protecting personal data when building big data solutions?[Read More]

Wednesday Mar 18, 2015

You've just got to be prepared to pay less. (Part 3)

ESG, de Persgroep and Cloudera compare the BDA with a DIY cluster.[Read More]

Monday Mar 16, 2015

You've just got to be prepared to pay less. (Part 2)

Oracle Big Data Appliance is 38% cheaper than a comparable DIY cluster.[Read More]

Wednesday Mar 11, 2015

You've just got to be prepared to pay less. (Part 1)

It's cheaper to buy Oracle Big Data Appliance than build your own Hadoop cluster.[Read More]

Monday Feb 23, 2015

Introducing Oracle GoldenGate for Big Data!

Announcing the general availability of Oracle GoldenGate for Big Data product, which offers a real-time transactional data streaming platform into big data systems.[Read More]

Thursday Feb 19, 2015

Self-service discovery will come to big data in 2015

Oracle predicts 2015 is the year self-service discovery and visualization tools for big data analytics 
will finally be practical for business. Available now, Oracle Big Data Discovery is designed to be "the visual face of Hadoop."  
  


[Read More]

Monday Dec 08, 2014

Big Data Governance– Balancing Big Risks and Bigger Profits

Strike the right balance between minimizing risks and maximizing profits when dealing with Big Data by adopting a governance program.[Read More]
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The place to get informed about big data ideas, trends, and news and how it impacts your success. Authors represent Oracle big data management, integration, analytics, and applications. https://www.oracle.com/big-data/index.html

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