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  • 12/11/2024 7:00 AM | Anonymous member (Administrator)


    A Metadata Management system must be capable of extracting Metadata from many sources. Design the architecture to be capable of scanning the various Metadata sources and periodically updating the repository. The system must support the manual updates of Metadata, requests, searches, and lookups of Metadata by various user groups.

    A managed Metadata environment should isolate the end user from the various and disparate Metadata sources. The architecture should provide a single access point for the Metadata repository. The access point must supply all related Metadata resources transparently to the user. Users should be able to access Metadata without being aware of the differing environments of the data sources. In analytics and Big Data solutions, the interface may have largely user-defined functions (UDF) to draw on various data sets, and the Metadata exposure to the end user is inherent to those customizations. With less reliance on UDF in solutions, end users will be gathering, inspecting, and using data sets more directly and various supporting Metadata is usually more exposed.

    Design of the architecture depends on the specific requirements of the organization. Three technical architectural approaches to building a common Metadata repository mimic the approaches to designing data warehouses: centralized, distributed, and hybrid (see Section 1.3.6). These approaches all take into account implementation of the repository, and how the update mechanisms operate.

    Create a data model for the Metadata repository, or metamodel, as one of the first design steps after the Metadata strategy is complete and the business requirements are understood. Different levels of metamodel may be developed as needed; a high-level conceptual model, that explains the relationships between systems, and a lower level metamodel that details the attributions, to describe the elements and processes of a model. In addition to being a planning tool and a means of articulating requirements, the metamodel is in itself a valuable source of Metadata. this figure depicts a sample Metadata repository metamodel. The boxes represent the high-level major entities, which contain the data.

  • 12/04/2024 1:04 PM | Anonymous member (Administrator)


    Another advanced architectural approach is bi-directional Metadata Architecture, which allows Metadata to change in any part of the architecture (source, data integration, user interface), and then feedback is coordinated from the repository (broker) into its original source.

    Various challenges are apparent in this approach. The design forces the Metadata repository to contain the latest version of the Metadata source and forces it to manage changes to the source, as well. Changes must be trapped systematically, and then resolved. Additional sets of process interfaces to tie the repository back to the Metadata source(s) must be built and maintained.

    This figure illustrates how common Metadata from different sources is collected in a centralized Metadata store. Users submit their queries to the Metadata portal, which passes the request to a centralized repository. The centralized repository will try to fulfill the user request from the common Metadata collected initially from the various sources. As the request becomes more specific or the user needs more detailed Metadata then the centralized repository will delegate down to the specific source to research the specific details. Global search across the various tools is available due to the common Metadata collected in the centralized repository.

  • 11/27/2024 7:00 AM | Anonymous member (Administrator)


    A completely distributed architecture maintains a single access point. The Metadata retrieval engine responds to user requests by retrieving data from source systems in real time; there is no persistent repository. In this architecture, the Metadata management environment maintains the necessary source system catalogs and lookup information needed to process user queries and searches effectively. A common object request broker or similar middleware protocol accesses these source systems.

    Advantages of distributed Metadata Architecture include:

    • Metadata is always as current and valid as possible because it is retrieved from its source
    • Queries are distributed, possibly improving response and process time
    • Metadata requests from proprietary systems are limited to query processing rather than requiring a detailed understanding of proprietary data structures, therefore minimizing the implementation and maintenance effort required
    • Development of automated Metadata query processing is likely simpler, requiring minimal manual intervention 
    • Batch processing is reduced, with no Metadata replication or synchronization processes

    Distributed architectures also have limitations:

    • No ability to support user-defined or manually inserted Metadata entries since there is no repository in which to place these additions
    • Standardization of presenting Metadata from various systems
    • Query capabilities are directly affected by the availability of the participating source systems
    • The quality of Metadata depends solely on the participating source systems

    This figure illustrates a distributed Metadata Architecture. There is no centralized Metadata repository store and the portal passes the users’ requests to the appropriate tool to execute. As there is no centralized store for the Metadata to be collected from the various tools, every request has to be delegated down to the sources; hence, no capability exist for a global search across the various Metadata sources.

  • 11/26/2024 7:00 AM | Anonymous member (Administrator)


    A WARM DAMA Rocky Mountain Chapter welcome to our new members who joined in Q3! We wouldn't be the organization we are without you!

    Professional Members:

    • Adefunke (Funke) B
    • Aileen P
    • Allen H
    • Cheryl B
    • Christopher H
    • John C
    • John L
    • Kris N
    • Nand S
    • Reeves S
    • Robert M
    • Terry T
    • Victoria B
    • Whitney C

    Corporate Members:

    • Diana C with Cambium
    • Charles W with The Doyle Group

    Guest Members:

    • Abdoulie C
    • Adrian C
    • Alex F
    • Allabaksh S
    • Alyssa P
    • Andrew T
    • Andrey S
    • Angelo S
    • Angelo V
    • Audrey S
    • Bikiran M
    • Bob M
    • Bryson T
    • Caleb S
    • Camiya I
    • DuWayne B
    • Ellie N
    • Esteban M
    • Ethan M
    • Gaius M
    • George M
    • Hannah C
    • Hasina R
    • Ian G
    • Jessica T
    • Jorge V
    • Kamal m
    • Karen M
    • Kathryn D
    • Katya K
    • Kevin J
    • Kristen K
    • Kristy T
    • Mantikoe M
    • Mary Kate B
    • Mary N
    • Meghan V
    • Mouyseang A
    • Mubeena K
    • Muhsin K
    • Natalie C
    • Neeraj Kumar J
    • Patrick V
    • Robert C
    • Robert L
    • Robert T
    • Rod G
    • Sagar B
    • Sheetal D
    • Susan H
    • Tarini S
    • Tessa H
    • Tessa K
    • Vanessa S
    • Wes B
    • Wes S
    • YuChen L
    • Yvette F
    • Zachery B

    Thank you also to everyone who renewed their membership in Q3!

  • 11/21/2024 7:00 AM | Anonymous member (Administrator)



    Mandi Albano joins the DAMA-RMC board as the new VP of Data.

    Amanda (Mandi) Albano is a seasoned software and database expert with a passion for leveraging technology to drive business success and improve lives. With a foundation in complex system design and data management, she began her career at StarTek Inc., developing performance-enhancing software supporting major telecommunications companies. Amanda then transitioned to consulting at Sogeti USA, where she led projects for the State of Wyoming, focusing on data integration and reporting. For the past 14 years at Market Perceptions, Inc., she has specialized in creating data-driven solutions centered around strategic and operational insights based on marking research data. Amanda combines her technical expertise with a commitment to building strong, trust-based partnerships, aiming to deliver best-in-class solutions that foster customer growth and advancement.

    Please give Mandi a warm DAMA-RMC welcome.

    Mandi Albano Linked In

  • 11/20/2024 7:00 AM | Anonymous member (Administrator)


    A centralized architecture consists of a single Metadata repository that contains copies of Metadata from the various sources. Organizations with limited IT resources or those seeking to automate as much as possible, may choose to avoid this architecture option. Organizations seeking a high degree of consistency within the common Metadata repository can benefit from a centralized architecture.

    Advantages of a centralized repository include:

    • High availability, since it is independent of the source systems
    • Quick Metadata retrieval, since the repository and the query reside together
    • Resolved database structures not affected by the proprietary nature of third party or commercial systems
    • Extracted Metadata may be transformed, customized, or enhanced with additional Metadata that may not reside in the source system, improving quality

    Some limitations of the centralized approach include:

    • Complex processes are necessary to ensure that changes in source Metadata are quickly replicated into the repository
    • Maintenance of a centralized repository can be costly
    • Extraction could require custom modules or middleware
    • Validation and maintenance of customized code can increase the demands on both internal IT staff and the software vendors

    This figure shows how Metadata is collected in a standalone Metadata repository with its own internal Metadata store.  The internal store is populated through a scheduled import (arrows) of the Metadata from the various tools. In turn, the centralized repository exposes a portal for the end users to submit their queries. The Metadata portal passes the request to the centralized Metadata repository. The centralized repository will fulfill the request from the collected Metadata. In this type of implementation, the capability to pass the request from the user to various tools directly is not supported. Global search across the Metadata collected from the various tool is possible due to the collection of various Metadata in the centralized repository.

  • 11/13/2024 11:03 AM | Anonymous member (Administrator)


    The most common definition of Metadata, “data about data,” is misleadingly simple. The kind of information that can be classified as Metadata is wide-ranging. Metadata includes information about technical and business processes, data rules and constraints, and logical and physical data structures. It describes the data itself (e.g., databases, data elements, data models), the concepts the data represents (e.g., business processes, application systems, software code, technology infrastructure), and the connections (relationships) between the data and concepts. Metadata helps an organization understand its data, its systems, and its workflows. It enables Data Quality assessment and is integral to the management of databases and other applications. It contributes to the ability to process, maintain, integrate, secure, audit, and govern other data.

    To understand Metadata’s vital role in data management, imagine a large library, with hundreds of thousands of books and magazines, but no card catalog. Without a card catalog, readers might not even know how to start looking for a specific book or even a specific topic. The card catalog not only provides the necessary information (which books and materials the library owns and where they are shelved) it also enables patrons to find materials using different starting points (subject area, author, or title). Without the catalog, finding a specific book would be difficult if not impossible. An organization without Metadata is like a library without a card catalog.

    Metadata is essential to data management as well as data usage (see multiple references to Metadata throughout the DAMA-DMBOK). All large organizations produce and use a lot of data. Across an organization, different individuals will have different levels of data knowledge, but no individual will know everything about the data. This information must be documented or the organization risks losing valuable knowledge about itself. Metadata provides the primary means of capturing and managing organizational knowledge about data. However, Metadata management is not only a knowledge management challenge; it is also a risk management necessity. Metadata is necessary to ensure an organization can identify private or sensitive data and that it can manage the data lifecycle for its own benefit and in order to meet compliance requirements and minimize risk exposure.

    Without reliable Metadata, an organization does not know what data it has, what the data represents, where it originates, how it moves through systems, who has access to it, or what it means for the data to be of high quality. Without Metadata, an organization cannot manage its data as an asset. Indeed, without Metadata, an organization may not be able to manage its data at all. As technology has evolved, the speed at which data is generated has also increased. Technical Metadata has become integral to the way in which data is moved and integrated. ISO’s Metadata Registry Standard, ISO/IEC 11179, is intended to enable Metadata-driven exchange of data in a heterogeneous environment, based on exact definitions of data. Metadata present in XML and other formats enables use of the data. Other types of Metadata tagging allow data to be exchanged while retaining signifiers of ownership, security requirements, etc. (See Chapter 8.)

    Like other data, Metadata requires management. As the capacity of organizations to collect and store data increases, the role of Metadata in data management grows in importance. To be data-driven, an organization must be Metadata-driven.

  • 11/06/2024 7:00 AM | Anonymous member (Administrator)


    Release Management is critical to an incremental development processes that grows new capabilities, enhances the production deployment, and ensures provision of regular maintenance across the deployed assets. This process will keep the warehouse up-to-date, clean, and operating at its best. However, this process requires the same alignment between IT and Business as between the Data Warehouse model and the BI capabilities. It is a continual improvement effort.

    This Figure illustrates an example release process, based on a quarterly schedule. Over the year, there are three business-driven releases and one technology-based release (to address requirements internal to the warehouse). The process should enable incremental development of the warehouse and management of the backlog of requirements.

  • 10/30/2024 7:00 AM | Anonymous member (Administrator)


    The data warehouse environment includes a collection of architectural components that need to be organized to meet the needs of the enterprise. Figure 82 depicts the architectural components of the DW/BI and Big Data Environment discussed in this section. The evolution of Big Data has changed the DW/BI landscape by adding another path through which data may be brought into an enterprise.

    This Figure also depicts aspects of the data lifecycle. Data moves from source systems into a staging area where it may be cleansed and enriched as it is integrated and stored in the DW and/or an ODS. From the DW, it may be accessed via marts or cubes and used for various kinds of reporting. Big Data goes through a similar process but with a significant difference: while most warehouses integrate data before landing it in tables, Big Data solutions ingest data before integrating it. Big Data BI may include predictive analytics and data mining, as well as more traditional forms of reporting. (See Chapter 14.)

    Source Systems, on the left side of this Figure, include the operational systems and external data to be brought into the DW/BI environment. These typically include operational systems such as CRM, Accounting, and Human Resources applications, as well as operational systems that differ based on industry. Data from vendors and external sources may also be included, as may DaaS, web content, and any Big Data computation results.

    Data integration covers Extract, Transform, and Load (ETL), data virtualization, and other techniques of getting data into a common form and location. In a SOA environment, the data services layers are part of this component. In this Figure, all the arrows represent data integration processes. (See Chapter 8.) 

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