Meta Data Management

As information technology becomes more complex and the need for data integration becomes more imperative, organizations require the ability to manage the meta data for each application / environment and subject area. Since meta data is information about data and gives meaning to data, meta data management is a critical component to any successful organization’s information technology competency. The Meta Data Portal will enable you to remain informed about the current trends in environments, repositories, ontologies and taxonomies, and will give you insight into best practices for meta data management across the enterprise with excellent columns, articles and white papers.

A Meta Data Repository Is The Key To Knowledge Management: Benjamin Franklin once said, "An investment in knowledge pays the best interest." Something tells me that Ben didn’t have knowledge management on his mind…but Ben was a pretty smart guy so maybe he did. Corporations are beginning to understand what Ben Franklin knew all those years ago: knowledge is their most valuable asset. Much of the push for knowledge is coming directly from the senior executives in our businesses. This tremendous desire to improve and maintain a corporation’s intellectual capital has triggered the field of study and vendor applications that we know as knowledge management.
Advanced Meta Data Architecture: Corporations are demanding more and more functionality from all of their IT (information technology) systems and meta data repositories are no exception to this rule. This article will address the more complexed architectural challenges that arise with implementing a meta data repository that requires more advanced functionality.
Designing the Optimal Meta Data Tool (part 1 of 3): Many government agencies and corporations are currently examining the meta data tools on the marketplace to decide which of these tools, if any, meet the requirements for their meta data management solutions. Often times these same organizations want to know what types of functionality and features they should be looking for in this tool category.
Designing the Optimal Meta Data Tool (part 2 of 3): In part one I walked through the Meta Data Sourcing and Meta Data Integration Layers. This month I will present the key functions and features of my optimal meta data tool in the MME categories of Meta Data Repository and Meta Data Management Layer.
Designing the Optimal Meta Data Tool (part 3 of 3): In part 3, I will conclude this series by presenting the key functions and features of my optimal meta data tool in the MME categories of Meta Data Marts and Meta Data Delivery Layer.
Managed Meta Data Environment: A Complete Walkthrough (part 1 of 8): Almost every corporation and government agency has already built, is in the process of building, or is looking to build a Managed Meta Data Environment (MME). Many organizations, however, are making fundamental mistakes. An enterprise may build many meta data repositories, or “islands of meta data” that are not linked together, and as a result do not provide as much value.
Managed Meta Data Environment: A Complete Walkthrough (part 2 of 8): In the second part of this series on the MME I will discuss the Meta Data Sourcing Layer and begin to discuss the most common sources of meta data that this layer targets.
Managed Meta Data Environment: A Complete Walkthrough (part 3 of 8): In part 2 of this series, the Meta Data Sourcing Layer of a Managed Meta Data Environment (MME) was presented, along with a walkthrough of one of the most common sources of meta data: Software tools. In part 3 month’s column I will walkthrough two additional common meta data sources: End users and Documents and spreadsheets
Managed Meta Data Environment: A Complete Walkthrough (part 4 of 8): In part 3 of this series, the Meta Data Sourcing Layer of a Managed Meta Data Environment (MME) was presented, along with a walkthrough of two of the most common sources of meta data: End Users, and Documents and Spreadsheets. In part 4, I will walk through the remaining common meta data sources: Messaging and transactions, Applications, Web sites and E-commerce, and Third parties.
Managed Meta Data Environment: A Complete Walkthrough (part 5 of 8): Over the last several parts of this series, I presented the first component of a Managed Meta Data Environment (MME), the Meta Data Sourcing Layer. The fifth installment on the six architectural components of a MME will walkthrough the second and third major components of a MME: Meta Data Repository and Meta Data Integration Layer.
Managed Meta Data Environment: A Complete Walkthrough (part 6 of 8): In part 5 of this series I presented the Meta Data Repository and Meta data Integration Layers. This installment will discuss the MME’s fourth component the Meta Data Management Layer.
Managed Meta Data Environment: A Complete Walkthrough (part 7 of 8): In part 7 I will complete my walkthrough of the Meta Data Management Layer by discussing the remaining functions: Purging, Query statistics, Query and report generation, RecoverySecurity processes, Source mapping and movement, User interface management, and Versioning
Evaluating Meta Data Tools: Today with so much emphasis being placed on empowering the business users to do more with less Information Technology (IT) help and enabling the IT organization to produce better results faster, it is no surprise that meta data has become one of the hottest issues of the day. In order to leverage all of the meta data from the disparate sources that exists in your organization, you need a way to gather it into a central location (repository). This is where meta data integration tools come in to the picture.
Implementing Data Quality Through Meta Data (part 1 of 2): How are you addressing the single most difficult problem facing data warehouses today? Data Quality. When the quality of data is compromised incorrect interpretation and use of information from your data warehouse can destroy the confidence level of its customers, YOUR users. Once the user’s confidence in your warehouse is eroded it is a question of time before your system will no longer exist.
Implementing Data Quality Through Meta Data (Part 2 of 2): This article is the concluding portion of a two-part series on implementing data quality through meta data. The first installment examined the role meta data can have in the data warehouse model and data acquisition designs for information content and quality. This segment will examine real world examples of technical meta data tags that can be incorporated into your designs to facilitate measurement of data quality and promote user confidence in the informational content of the warehouse. This meta data provides a semantic layer of knowledge about the information in your warehouse that is highly valuable to both business users and IT (information technology) development staff.
Managed Meta Data Enviornmnet: A Complete Walkthrough (part 8 of 8): This eight and final installment will discuss the MME’s fifth and sixth components; Meta Data Marts and the Meta Data Delivery Layer.
Meta Data Management and Enterprise Architecture: Almost every large government agency or Global 2000 company is struggling to properly manage their enterprise information technology (IT) architecture. This difficulty is the direct result of the highly distributed, disjoined and overly expensive IT environments which currently exist throughout our industry. This situation has resulted in the reemergence of corporations looking to establish truly proactive Enterprise Architecture organizations. In this month’s column I will discuss enterprise architecture and how it is tied to an enterprise meta data management initiative.
Meta Data ROI: The Evolution of Business (part 1 of 3): This is the first part of a three part series on meta data return on investment (ROI). In order to fully understand the value that meta data provides it is critical to by clear on the forces triggering the evolutionary development of corporation competing in today’s business marketplace. In part two of this series we will examine the technical challenges that arise as a result of this evolutionary process. In the final and concluding portion we will look at the remedies that meta data provides to these technical challenges.
Meta Data ROI: The Evolution of Technology (part 2 of 3): In this our second installment on meta data return on investment (ROI) we will examine the technical challenges that arise as a result of these evolutionary corporate changes. In part 3 of this series we will present the remedies that meta data provides to these technical and business challenges.
Meta Data ROI: Meta Data Solutions (part 3 of 3): In this our third and final installment on meta data return on investment (ROI) we will examine the solutions that a meta data repository provides to these challenges.
Top 10 Mistakes to Avoid When Developing a Meta Data Repository (part 1 of 2): Building a meta data repository is critical for accessing, maintaining, and controlling the vital information stored in our decision support systems (DSS). While meta data has always been a central covenant of data warehousing, over the last couple of years it has been brought further into the spotlight as most Fortune 1000 companies have some sort of DSS system currently in place, most for several years.
Top 10 Mistakes to Avoid When Developing a Meta Data Repository (part 2 of 2): Part 2 is the second and concluding article on the top ten mistakes to avoid when developing a meta data repository. In part 1 we examined mistakes 1 through 5. In this concluding installment we will present mistakes 6 through 10.
To Buy a Repository or Not to Buy a Repository? That is the Question…Here is the Answer: More companies than ever before are reducing their IT (information technology) costs and delivering a competitive business advantage by building successful meta data repositories. The companies that are building these repositories are faced with a fundamental question. Should we buy a meta data integration tool and use that tool to build our repository or should we build a custom meta data repository? This article will address the four essential questions that will guide your decision whether to build or buy.
Which Should Come First the Chicken or the Egg (Meta Data Repository or the Data Warehouse)?: Over the years I’ve given more than a hundred keynotes/seminars on data warehousing and meta data. During these talks I’ve been asked the question of which should you build first many times. After giving it careful thought I have come to the conclusion that a corporation’s optimal approach is to FIRST build their meta repository. Let’s examine the reasons why.
Meta Data Architecture Fundamentals: Over the next few years many companies will have the unenviable task of completely rebuilding their decision support systems. This is occurring because many of these systems were built with flawed architectures. The architecture used to build the meta data repository is every bit as critical to its long-term viability as the architecture for the decision support system is. By taking the time to build a sound architecture your repository effort will be able to grow and mature over time to support all of your company’s meta data needs.
Meta Data: The Key To Decision Support: This article marks the first monthly column dedicated to meta data and data administration. During the coming months we will examine such topics as meta data ROI, architecture (distributed vs. centralized vs. decentralized), data administration staffing and organizing, meta data delivery, and the future trends shaping the meta data and data administration arena. In addition, we will examine some “best practice” case studies of companies that are doing meta data well.
Meta Data Repository Project Plan: The Construction Phase: This column is the fifth installment in a walk through of the five fundamental phases of a project plan to build a meta data repository: Orientation, Feasibility, Design, Construction, and Rollout. This month we will complete the Construction phase.
Meta Data Repository Project Plan: The Design Phase, Part 1 of 2: This column is the third installment in a walk through of the key tasks in each of the five fundamental phases of a project plan to build a meta data repository: Orientation, Feasibility, Design, Construction, and Rollout. This month we will target the first two key deliverables of the Design phase: “Meta Data Tool Evaluation & Selection” and the “Construct Integration Architecture Document”. We will cover the “Create Detailed Design Documents” and Train Development Staff” tasks in next month’s column.
Meta Data Repository Project Plan: The Design Phase, Part 2 of 2: This column is the fourth installment in a walk through of the key tasks in each of the five fundamental phases of a project plan to build a meta data repository: Orientation, Feasibility, Design, Construction, and Rollout.
Meta Data Repository Project Plan: The Feasibility Phase: The key to your company’s prosperity is how well you gather, retain and disseminate knowledge. To this end the meta data repository is the key gathering, retaining, and disseminating knowledge. This column is the second installment in a walk through of the key tasks in each of the five fundamental phases of a project plan to build a meta data repository: Orientation, Feasibility, Design, Construction, and Rollout. This month we will zoom in on the Feasibility phase.
Meta Data Repository Project Plan: The Rollout Phase: This column marks the sixth and final installment of the five fundamental phases of a project plan to build a meta data repository. This month we will complete the Rollout phase.
Meta Data Repositories: Where We’ve Been And Where We’re Going: I thought that it would be valuable to take a look back and see where meta data management and repositories have come from and where the meta data management and repository industry are headed. As the old saying goes, "We’ve come a long way baby!"
Meta Data Repository: A System That Manages Our Systems: If you were to look at the balance sheet of most any Global 2000 company, you would see many different and varied entries for such assets as property, cash, equipment, accounts receivable and my favorite category, Goodwill. Unfortunately, one item that is not seen in the asset section of the balance sheet is Data. In the Information Age, data is every bit as valuable as property, equipment and Goodwill.
Meta Data ROI: A Competitive Advantage to Your Business: This article marks the second in a series of articles on specifically defining the value that meta data provides to a corporation. In this month’s column I will discuss the value that meta data can provide to the business users of your company.
Meta Data ROI: Making Your IT Department Better, Stronger, Faster: When selling in the concept of meta data to your corporation’s senior management there is only two things that they understand; Increasing Revenues or Decreasing Expenses. If you are not talking about increasing revenues or decreasing expenses you are the teacher on the old Peanuts cartoon, blah, blah, blah, blah, blah. This article marks the first in a series of articles on specifically defining how meta data can increase revenue and decrease expenses at your corporation’s IT (information technology) department.
Meta Data ROI: Specific Guidelines for Your Company: This article represents the third and concluding portion of my three part series on defining the return on investment (ROI) that a meta data repository can provide to your company. The formulas that I will present in this article are meant to be used as guidelines; the results that you experience on your project will differ.
Evaluating Repository Technology: The mission of a repository is to provide an efficient method for controlling the definition, access and use of metadata so this information can be used to provide meaning to corporate data. "Metadata" is "data about data" - information that can make the actual instance data in files or databases understandable to both technical staff (system of record, table or file where the data element is located, data type and size of the element) and to the business staff (business name of the cryptic system name, source the data, algorithm or calculation upon which a derived data is based, etc.) All this information can be stored in a repository, providing a central point of control for the management of metadata throughout an organization.
Meta Data Repository Myths: As the meta data repository industry continues to grow the myths and misunderstandings around this market segment also continue to grow. I am fortunate enough to have the opportunity to meet and speak to thousands of meta data professionals every year. During these times, I am often asked questions which reveal that there is a good deal of inaccurate meta data information being disseminated. In this month’s column I am going to address the most common meta data myths and set the record straight.
Meta Data Repository Project Plan: The Orientation Phase: In this article series, I will be walking through the key tasks in each of the five fundamental phases of a project plan to build a meta data repository: Orientation, Feasibility, Design, Construction, and Rollout. Hey wait a minute…some people may be saying that these are the same phases they use for all of their IT (information technology) projects. This observation is 100% correct.
Meta Data Silos (part 1): Meta data management and its use in enterprise data management have become one of critical information technology (IT) focuses for both global 2000 corporations and large government agencies. As these entities look to reduce their IT portfolio and control their escalating IT costs they are turning to the technical functionality that a meta data repository can provide them. This approach is very sound and the organizations that have built well-architected enterprise-wide meta data repositories have achieved a tremendous amount of success.
Meta Data Silos (part 2): In the first part of this series, I discussed the issue of disparate meta data repositories. During this column I listed the four most common problems created by this phenomenon and I discussed, in detail the first two issues:
Missing Meta Data Relationships, Typically Building By Non-Meta Data Professionals, Costly Implementation and Maintenance, Poor Technology Selections
Revisiting the Top 10 Mistakes To Avoid When Building a Meta Data Repository: In 1999 I wrote a two-part column for DM Review (March and April 1999 editions) on the top ten mistakes to avoid when building a meta data repository. Now that we are in 2002 it is time to update this list to reflect the changes that have occurred in the meta data repository marketspace. This month we will examine the first five mistakes on our list.
Revisiting the Top 10 Mistakes To Avoid When Building a Meta Data Repository (part 2): This article marks the second part in a two-part series revisiting the top ten mistakes to avoid when building a meta data repository. This month we will examine the final five mistakes on our list.
Modeling Meta Data: Building a meta data repository is no longer an option for corporations, but an absolute requirement. One of the most important tasks in the development of a repository is the construction of the meta model (a physical database that holds meta data). Building the meta model can be a difficult task at the best of times. There are many factors to consider, such as what types of meta data you need to store, how you are going to store it, who has access to it, and who is going to build it. As we go through this article we'll discuss the types of information that you need to start designing your meta model.
Waasszzup!?! XML and Data Warehousing That’s What!: XML and data warehousing are hotter than the beer commercial that spawned the wasszzup catch-phase. Data warehousing is a proven technology, while XML offers the hope of making the Internet a much easier world to live and develop in. There has been a great deal written about the potential uses of XML, however there are few articles that discussed its uses in data warehousing. Last month we discussed how XML could be used to bring data into a data warehouse environment. This month we will present how XML is used to get data out of the data warehouse and send it to other corporations, websites, and wireless devices.
XML: The Global Meta Data Standard: XML (eXtensible Markup Language) is one of the hottest areas in all of technology today. In fact it’s difficult to find a magazine that doesn’t have one or more articles addressing this topic in one manner or another. In this month’s column we will look at how XML is impacting the meta data industry and the reasons why XML will impact every corporation in the world.
Deborah Poindexter: Meta Data Driven Enterprise Data Management – Future or Fantasy?: Is your data out of control? Is your enterprise data management fragmented and inconsistent? Are you unable to answer these questions? If the answer to any of these questions is “Yes”, then Enterprise Data Management may be a discipline you should investigate. Enterprise Data Management (EDM) is continually gaining acceptance as a critical function of IT, since data is the foundation of all business decisions.
Meta Data Repository: Key to Re-Usability: The cornerstone of every commercial business is profitability. If market forces do not permit a great deal of leeway in the pricing of goods and services, other ways must be found to generate adequate returns. Reducing expenses is one of the most obvious ways to expand profit margins, and positioning the organization to react to new business opportunities quickly with flexibility and accuracy is another.

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