Tag Archives: Governance

The Carrot and Stick Are Must-Haves in the Data Governance Toolbox

The next facet of our Data Governance Framework recognizes the key dependency any data governance-related effort has on change management.  No matter how compelling the vision and business case, making data a trusted corporate asset on par with your financial and people assets is a major culture shift for most organizations.


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The Process Stages of Data Governance

To truly manage data as a valued enterprise asset, data governance must be managed as a business function like finance or human resources.  A finance function is comprised of multiple core business processes such as accounts payable, accounts receivable, payroll and financial planning.  So to manage data governance as a function, what are the core business processes that comprise data governance? (more…)

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Data Governance Policies Shape Organizational Behaviors

Continuing the tour of our Data Governance Framework, it’s time to discuss the corporate policies that must be documented to form the foundation of your data governance efforts. When defined, approved, evangelized and enforced appropriately, these policies have the power to accomplish a feat that grassroots data governance efforts fail at repeatedly: Evolving your corporate culture to one that actually does manage data as an asset.


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Data Governance Framework Walkthrough: By The People, For The People

The next stop on the tour of our data governance framework focuses on the people investments that your organization must make to build out data governance capabilities.  The right people are required to support, sponsor, steward, operationalize and ultimately deliver a positive return on your data assets.  As you can imagine, the challenge of defining the right roles and responsibilities, job descriptions, career paths, and incentive plans for the people needed to make data governance a success will not be solved in a short blog post.  So my goal here is to share my thoughts and open the discussion to identify the most relevant areas for consideration. (more…)

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Embrace the Oxymoron: Data Governance Requires Agile Bureaucracy

Last year, while still an analyst with Forrester Research, OCDQ “Blogger in Chief” Jim Harris and I coordinated dueling blogs taking polar-opposite stances on the debate over whether Data Governance initiatives should embrace an approach to optimize their agility or an approach to formalize the necessary bureaucracy. (You can read Jim’s blog here and my blog here.)

That was a fun exercise, but our clear conclusion was that aspects of both agility and bureaucracy are necessary to some extent for data governance to deliver real business value. (more…)

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Data Quality and Big Data

In the world of big data, getting access to data and making sense of it is often times a more important consideration than managing sheer volume itself.  Companies that are successful in unlocking true value from big data open themselves up to a world of insight for better understanding of things like customer preferences, satisfaction and regional purchasing differences. Doing this obviously is often harder than it seems due to the variety of information itself, leading to standardization and duplication issues.  Ownership is often an issue as well, with departmental lines being the most common constraint to sharing important data across the enterprise. (more…)

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Structure, Semantics and Master Data Models – Part 1

Looking back at some of my Informatica Perspectives posts over the past year or so, I reflected on some common themes about data management and data governance, especially in the context of master data management and particularly, master data models. As both the tools and the practices around MDM mature, we have seen some disillusionment in attempts to deploy an MDM solution, with our customers noting that they continue to hit bumps in the road in the technical implementation associated with both master data consolidation and then with publication of shared master data.

Almost every issue we see can be characterized into one of three buckets: (more…)

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Data Management Issue Categories

In my last post I started to talk about ideas for classifying the data management issues, with the reasoning that it will help to determine the feasibility that the expectation that acquiring a particular solution will actually address the core issues. I actually have used this categorization with some of our customers, and the process of classification does lend some clarity when considering solutions. There are five categories: (more…)

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Data Storage Is So Cheap Its Expensive

The cost for 1GB of magnetic disk storage 20 years ago was $1,000 – now it’s eight cents. 1GB is enough to store about 20 thousand letter-size scanned documents. To store the same number of paper documents would require two four-drawer filing cabinets which would cost about $400. The cost of electronic data storage is five thousand times less than paper storage.

Costs have dropped consistently 40% per year which accounts for the more than 12,000 times reduction in cost since 1992.  The cost for RAID or mainframe disk storage is somewhat greater, but the historical trend for other storage devices has been similar and the forecast for the foreseeable future is that costs will continue to decrease at the same rate. Twenty years from now we will be able to buy one tera-byte of storage for a penny. (more…)

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Dodd-Frank Legislation and Structured Data Retention

The “Dodd-Frank Wall Street Reform and Consumer Protection Act” has recently been passed by the US federal government to regulate financial institutions. Per this legislation, there will be more “watchdog” agencies that will be auditing banks, lending and investment institutions to ensure compliance. As an example, there will be an Office of Financial Research within the Federal Treasury responsible for collecting and analyzing data. This legislation brings with it a higher risk of fines for non-compliance. (more…)

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