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The Myths about Meta Data

  Article published in DM Direct Newsletter
April 8, 2005 Issue
 
  By Vivek Anand and Mark Robinson

In business, the devil is in the details. That is why companies consider meta data (data about data) as a way to get a handle on the details of their business. However, if it isn't done right, meta data might as well be meta-Seinfield - data about nothing. A serious investment can be wasted without really finding out if the data you don't know can indeed make the data you do know better.

By definition, meta data is selected or summary information about data i.e., name, length, valid values or description of a data element. Meta data is stored in a data dictionary and repository. It insulates the data warehouse from changes in the schema of operational systems.

The adoption of meta data by business has been slower than expected. Intuitively, one would think mining and categorizing data about data would be a no-brainier, especially for organizations that rely on enterprise applications to run a business.

It doesn't help that the whole concept keeps getting recategorized each time an organization defines a different type of meta data. The challenges in the proper definition have arisen because attempts to categorize and define metadata have so far been one dimensional. It is ironic that meta data, one of the functions of which is to define data, should go through changes in its own definition.

Yet, even where meta data is being considered, it is still not well understood in terms of its scope, implementation and benefits. Listed below are some of the myths surrounding meta data that have adversely affected its importance within corporate culture.

Meta Data Myths

Meta data is all about data warehousing.
Most implementations of meta data target the data within a data warehouse. There is no reason that the implementation of meta data should be limited to a data warehouse. Depending on the scope of implementation, meta data can be maintained on organizational processes, business indicators and metrics. Enterprise-level meta data is more useful than a solution that is data warehouse centric.

Meta data should target data origination sources throughout the enterprise, such as transaction systems.

Meta data is someone else's business.
Information technology, always operating under the pressures of time and money, places little or no importance on developing meta data. It is viewed as a maintenance tool, not as part and parcel of the development processes. The reason is the fixed cost to create the meta data infrastructure within an organization. This is shortsighted.

The enterprise application portfolio of a company is akin to a network of local streets. But without meta data, there is no highway system to link neighborhoods and villages. Local streets are easy to justify and build, but their implementation and, therefore, their benefits are restricted to a limited population. Companies that create meta data repositories in an unconnected, stovepipe fashion end up with redundant and passive meta data.

I cannot afford meta data.
Yes, you can, if there is total corporate buy-in. A policy decision about meta data should be made at the highest echelons of management. You do have to swallow the up-front fixed cost of creating pervasive meta data throughout an organization. But depending on the scope, a meta data solution will pay benefits many times over the cost of development.

Generally, ROI on meta data depends on the current state of meta data availability and its use within the organization. It is directly proportional to:

  1. Scale of implementation
  2. Size of organization
  3. Complexity of business and technical processes
  4. Support from management to affect new process or change existing ones

 

"The more the merrier" is the essential mantra behind a successful implementation of a meta data solution and keeping the ROI as high as possible. For a very large organization ($10 billion in revenue or higher), a ROI of 500 percent is not uncommon. And sources of ROI can be both tactical and strategic.

Meta data has no flexibility.
Most people believe that once meta data is created it changes little. This belief not only obscures the benefits of meta data but can also cause unintended harm to its user community. While it is true that enterprise data changes more frequently than meta data, there are instances when meta data may evolve due to changes in the attributes of data although the data itself remains the same. A passive meta data solution can do more harm than good when the users mistakenly base their decisions on outdated meta data. A successful meta data solution calls for real-time updating of key metrics.

Managers do not need meta data.
Everyone can benefit from meta data whether it is a software developer, business development manager, data modeler or CEO. Unlike most applications, useful "locally" within the information, an enterprise meta data solution has a more "global" reach and provides useful and hard to get information at a glance. This makes meta data useful to the strategic decision-making process. C-level management should have ready access to meta data.

Maintaining meta data is too political.
Meta data provides deep insight into the target data that it represents. For this reason, access to meta data can be the cause of political quandary within a company. A meta data solution, when implemented with proper security and context sensitivity can truly be a "win-win" proposition. Most concerns about data sharing can be dispelled by installing user-sensitive authorization profiles. This would enable the repository to capture and manipulate meta data on tax calculation, for example, but still safeguard this information from a field salesman, who wants to access meta data of a specific part number.

Piece-meal implementation of meta data is feasible.
A long-lasting and successful meta data solution is mostly an all or nothing proposition. Different parts of the meta data solution come together to offer an all-encompassing knowledgebase, which instills confidence within the user community. Most users of meta data are also the providers of meta data. A half-baked solution can quickly turn some users away. When they stop using meta data, they stop populating the meta data repository. An incomplete meta data solution would drive away more users and would result in a vicious circle. Fewer meta data users result in obsolete meta data, which erodes its accuracy and completeness, which, in turn, drives more users away.

Proper Components of a Meta Data Solution

Attempts by firms to erect home-brewed meta data solutions have failed; primarily because of they underestimated the requirements of a successful meta data solution. It is true that a central repository is the most obvious component of a meta data solution. But to keep the meta data active, complete and accurate, a successful implementation needs other components such as triggers and alarms, data profilers, data crawlers, security administration, meta data administration, user interface and ability to interface with other tools and meta data repositories. Utility of a meta data solution can rapidly degrade in the absence of one or more of these components.

Meta data is the glue that binds enterprise information together. The arteries of a successful meta data solution will reach across most of an organization's functions. With numerous beneficiaries, meta data will have many champions throughout an organization. Although, a meta data implementation needs a dedicated administrator to coordinate the interests of multiple stakeholders and maintain the repository, upper management should champion its active maintenance to assure its success. If such coordination is possible, then everyone in the organization will become the masters of their data's domain.

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For more information on related topics visit the following related portals...
Meta Data.

Vivek Anand is a project manager within the Business Intelligence practice at Greenbrier & Russel. He has more than 18 years of experience in applying information technology solutions to achieve business goals. As a consultant, Anand has helped Fortune 500 firms with their technology strategy, change management, data warehouse and business intelligence solutions and meta data management. You can reach him at vanand@gr.com.

 

Mark Robinson manages Greenbrier & Russel's Business Intelligence practice. With more than 20 years of experience in business and technology fields, Robinson has performed traditional leadership roles in IT management, product management, practice management and solutions delivery as well as leading companies in strategic transformation efforts through investments in business intelligence.  As a consultant, he has been involved in business transformation efforts that have begun with critical success factors studies, assessments and discovery workshops that focus on the value of business intelligence as it aligns with the business strategy. In addition to teaching, Robinson has been a speaker/educator on various topics in business intelligence with industry associations including The Data Warehouse Institute and the Finance Executives Institute. You can reach him at mrobinson@gr.com.

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