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Kelle O'Neal

Thanks for joining our data conversation! This blog is an opportunity to share the real life challenges, opportunities and approaches to improving the quality and value of data in your organization. We will write about everything data related from translating "data" speak into "business" speak, to governance models, to the real differences among the myriad software tools available. But there's one catch: we all have to agree to toss out the fluff. That's right, no 30,000 foot, theoretical strategies that leave you wondering how to execute and actually improve performance. Visit regularly to learn from peers and partners on how they are managing and improving data, and we hope you'll also share your views and experiences.

About the author >

As Founder and Managing Partner of First San Francisco Partners, Kelle O’Neal manages specialist data governance and data management consulting services to complex organizations that deliver faster time to results. Kelle can be reached at kelle@firstsanfranciscopartners.com or through the First San Francisco Partners website.

Follow First San Francisco Partners on Twitter at @1stSanFrancsico.

Editor's Note: Find more articles and resources in Kelle's BeyeNETWORK Expert Channel. Be sure to visit today!


October 2012 Archives

Now that we have shared our definition of big data and discussed the value of big data to the enterprise, we'd like to explore how to harness big data to help your business.

A company's data -- including its big data -- is an important proprietary asset. Every company has unique data on its markets, customers and competitors that can be used to help guide product innovation and to gain competitive advantage. Access to this data is critical.

Being able to accurately and securely ensure data is available to all parts of a company is essential. However, as big data volumes continue to increase, many enterprises are struggling to determine how to manage this data. Finding effective strategies to manage big data and extracting value from this data, without incurring unnecessary costs, is now becoming an important corporate and IT priority.

But, if traditional tools can't easily manage big data, how do you begin to capture and manage this data?

Big data is just another data type that needs to be considered along with master data, reference data, transactional data and metadata. To start harnessing big data along with other enterprise data, it is important to expand the scope of your data strategy to include this type of data.

The management and governance of big data should be included in your overall data management strategy. Data management ensures that the data used by an organization is available, accurate, complete and secure. It establishes the policies, practices and procedures required to manage the full data lifecycle and guide the architecture strategy to ensure the data is properly utilized. Data management applies to big data as well.

As seen in the diagram below, Enterprise Data Management includes Big Data Management (BDM) and Big Data Governance (BDG). Including these two components into your Enterprise Data Management framework enables big data to be properly managed and governed using defined organizational roles, processes and technology. BDG ensures control over the structure, processing, delivery and usage of information required for fact-based decision making and ensures that ownership and accountability of data objects is defined and reinforced. Because big data comes from external sources that may contain important information, managing and governing big data just like any other data type is critical. With BDM and BDG business stakeholders can trust the data.

BDM v3.png

The data governance organization should provide guidance and develop a big data management strategy to be implemented using a step-by-step roadmap. The big data management strategy should articulate the big data vision, business case, goals and objectives, scope priorities, expected return on investment and risk management. It should also describe the lifecycle management of big data and how it will flow across all systems for use by the company.

Other key considerations of a big data management strategy include:


  • Technology required to efficiently process and capture value from large quantities of data

  • New techniques and processes used to aggregate, manipulate and analyze the data

  • Big data infrastructure and architecture approaches

  • Lifecycle process for big data - requirements, capture/store, process, integrate/organize and consume

The end goal is to easily integrate big data with enterprise data to allow complex and deep analytics.

Developing a big data management strategy and roadmap is the first step towards harnessing the value of big data. The roadmap is both a vision for developing the future state of the management and governance of big data and a model that defines the prioritized transitions to get there. The roadmap also identifies changes that must be made to organizational roles and responsibilities, processes and technology. It provides the mechanism to manage big data as an asset--especially since big data is now one of your most important corporate assets.

Ultimately, big data should be treated with the same importance as other assets by ensuring that it is properly stored, accessible and kept secure. It should be defined in a fashion that makes it easy for users to access and understand.

In the next blog post, we'll review the key similarities and differences between big data governance and "regular" data governance. We'll also describe how to create a data governance program for your big data or how you might incorporate big data into your existing data governance program.

How are you currently governing your big data?


Posted October 22, 2012 2:15 PM
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