Bi metadata management
I have been working on Power BI Desktop for a while now. I had few questions on Power BI Metadata. How can we extract Metadata information such as Search engine information if any, data definitions, data lineage, versioning etc. Is there a documentation for the same?
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Bi metadata management
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- Alex is your Enterprise Data Platform for workforce productivity.
- Toward a Better Understanding of Metadata – Governance
- What is Metadata and How is it Used?
- Power BI Metadata
- Data Governance
- Discover, manage, and share community curated metadata
- The Expanding Role of Metadata Management, Data Quality, and Data Governance
- Metadata Management Solutions
- DMBOK: Metadata Management
Alex is your Enterprise Data Platform for workforce productivity.
The successful use of BI deeply depends on effective metadata management, which is often referred to as "data about data". Metadata serves as a roadmap for all BI system data, so that these data can be efficiently managed, controlled, and distributed. Comprehensive metadata management ensures that the BI system has high-quality information and provides sufficient scalability to meet new information needs and data sources.
Metadata implementation is also a part of information integration, and the most important task is to integrate metadata stored in various tools.
Metadata management is the ninth data management function of the data management framework. This article will introduce this function. View Image. Metadata is commonly called "data about data", that is, data used to describe other data. The data can be interpreted in many ways, such as. When we say metadata is "data about data," we need to make sure that we are talking about the background of the data, not the detailed details or related data about the data. Metadata describes the background, content, data structure and life cycle management of the data.
In short, metadata is the "data context". Metadata management panorama includes three parts: 1. Metadata model 2. Metadata topology 3. Metadata management methodology. Metadata is an important component in BI architecture. Metadata includes business rules, data sources, summary levels, data aliases, data conversion rules, technical configuration, data access permissions, and data usage.
A well-designed metadata model can improve the efficiency of management, change control, and distribution of metadata, and achieve seamless, end-to-end traceability.
Here is an example, if "Richard King" is data, the following is metadata:. These metadata can be further abstracted as Meta-Metadata, which represents the background of the metadata. Business entities are connected to technical entities, such as data tables, cubes, and reports, and they obtain information directly from available source tables or data forms.
The most detailed metadata exists in the data element layer. Business users widely use this layer of metadata. BI technical metadata includes all metadata at different levels in the BI environment, and can be further subdivided into three types:. A well-defined metadata management product should ensure the high quality of information, while being able to flexibly expand the new data requirements and data sources of the BI system.
The main purpose of metadata management is to realize the standardization and centralization of metadata based on a flexible and robust architecture. The framework definition involves analyzing the current state and processing of metadata, and providing a blueprint for the development of the metadata management system. It is mainly described from three aspects: long-term goals, specific goals and high-level needs:.
Metadata standardization. Uniform terminology and communication standards within the enterprise :. Use metadata as the only basis for users to ensure that all users use consistent terms to communicate, understand, and explain business issues.
At the same time, it can eliminate ambiguity, ensure the consistency of information within the enterprise, and facilitate the sharing of knowledge and experience. The ETL process, especially the integration process, relies on a variety of data sources and BI systems.
Standardized metadata makes the meaning of the data elements uniform when data from different source systems are integrated into the BI system; in addition, only tools or applications that share metadata through standard methods are allowed to be integrated into the BI system. Centralized metadata. Improve analysis and interaction with BI systems:. Analysis covers a series of technical methods, including simple report query, OLAP analysis, and even complex data mining.
To a large extent, users interact with these technologies through the metadata layer, and all these analyses need to be driven by metadata. Metadata needs to provide users with centralized information, such as the meaning of data, terms and business concepts, and the relationship between them and the data.
Therefore, metadata can support accurate and intuitive queries, reducing the cost of users accessing, evaluating, and using relevant information. Centralized metadata should be non-redundant and non-repetitive.
In addition, data retrospectiveness and consistency are critical to high data quality. The ETL process needs to manage metadata traceability by capturing data inheritance such as source, scheduling information, timestamp, etc. Centralizing all this information will help solve data integration problems in a timely manner and better manage the correctness of data.
Reduce the cost of BI system management. Support new application development:. Metadata provides relevant information about the meaning, structure, and source of the data, which is helpful for output control in the requirements collection and design phase, and can also ensure the reliability of the application development process.
Automated management process :. Metadata should drive a variety of DW processes such as ETL, batch reports , and information about process execution logs, DW data loading status, etc. These metadata-driven processes can automate BI management and reduce manual intervention, thereby reducing the amount of BI system maintenance.
In order to provide a thorough security mechanism, ACL and user information should be managed at the metadata layer. User roles need to be designed to control the permissions of users in different departments and different regions to access data with different granularities, and to perform security inspections on data access through the audit trail process.
Flexible metadata architecture. The scalability and adaptability of metadata:. To adapt to change, metadata must be extensible. For example, the frequently changing semantic layer should be independent of the application and stored in the metadata. On the one hand, it ensures the flexibility of system expansion, and on the other hand, it is easy to add new metadata objects. Moreover, the general metadata model also provides the reusability of a large number of code fragments.
In addition, it is necessary to create a metadata management team from both product and project levels, including metadata administrators, coordinators, data analysts, and DBAs. Once the team is set up, the high-level metadata requirements are established through the understanding of the business and technical beneficiaries.
After the framework definition phase is completed, the next step is to describe the metadata specifications, which mainly include the following activities and sub-activities:. Metadata status list: establish a metadata list, including: functional information requirements, data models, process models, data dictionaries, business term dictionaries, existing metadata environments, system documents, etc.
Metadata interface requirements: metadata database and its content, bridge, owner, system access, metadata blood relationship. The design phase includes determining the following:. Toggle navigation Titan Wolf. Metadata management methodology Metadata model Metadata is an important component in BI architecture.
Here is an example, if "Richard King" is data, the following is metadata: The employee code type is Number 6 -this tells us that the first 6 characters in the data are of numeric type, representing the employee code; The employee name type is Varchar 30 -this tells us that the next 30 characters are long characters, representing the employee name. Middle layer physical layer Business entities are connected to technical entities, such as data tables, cubes, and reports, and they obtain information directly from available source tables or data forms.
Bottom layer element layer The most detailed metadata exists in the data element layer. BI technical metadata BI technical metadata includes all metadata at different levels in the BI environment, and can be further subdivided into three types: Information integration-ETL data extraction, conversion and loading metadata Information storage-data warehouse metadata Information release-report metadata View Image View Image BIDS Metadata Management Methodology A well-defined metadata management product should ensure the high quality of information, while being able to flexibly expand the new data requirements and data sources of the BI system.
It is mainly described from three aspects: long-term goals, specific goals and high-level needs: Long-term goals The overall goals of the metadata management system are as follows: Standardized metadata and data processing Centralization of metadata management Metadata information deduplication Metadata architecture that adapts to changes Specific purpose The purpose of the metadata management system is as follows: Develop metadata and data standardization Management and application of centralized BI system Improve data integrity and accuracy through non-redundant and non-repetitive metadata information Reduce the cost of BI system component development, implementation, improvement and maintenance Establish a flexible metadata architecture to make the BI architecture adapt to changes High-level requirements The high-level requirements for metadata creation and management can be understood through the content in the table below.
Serial number demand 1. Metadata standardization 1. Serial number. Uniform terminology and communication standards within the enterprise : Use metadata as the only basis for users to ensure that all users use consistent terms to communicate, understand, and explain business issues.
Seamless system integration: The ETL process, especially the integration process, relies on a variety of data sources and BI systems. Data quality improvement: Defining data quality verification rules is an integral part of ETL metadata. Improve analysis and interaction with BI systems: Analysis covers a series of technical methods, including simple report query, OLAP analysis, and even complex data mining. Data completeness and accuracy: Centralized metadata should be non-redundant and non-repetitive.
Support new application development: Metadata provides relevant information about the meaning, structure, and source of the data, which is helpful for output control in the requirements collection and design phase, and can also ensure the reliability of the application development process. Automated management process : Metadata should drive a variety of DW processes such as ETL, batch reports , and information about process execution logs, DW data loading status, etc.
Careful safety mechanism: In order to provide a thorough security mechanism, ACL and user information should be managed at the metadata layer. The scalability and adaptability of metadata: To adapt to change, metadata must be extensible.
Toward a Better Understanding of Metadata – Governance
One question we get from prospects evaluating our software is whether or not we provide a full data catalog service. This is an important question and requires that we first define what types of metadata are relevant for data lakes and data access solutions. Okera deals primarily with two types: technical metadata and business metadata. Technical metadata defines technical attributes that are necessary for data presentation, manipulation, and analysis, such as data type, field length, content profiling, lineage, and more.
What is Metadata and How is it Used?
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Power BI Metadata
Metadata is " data that provides information about other data", [1] but not the content of the data, such as the text of a message or the image itself. There are many distinct types of metadata, including:. Metadata is not strictly bounded to one of these categories, as it can describe a piece of data in many other ways. Metadata has various purposes.
Data Governance
Governance in Tableau is a critical step to driving usage and adoption of analytics while maintaining security and integrity of the data. You must define standards, processes, and policies to securely manage data and content through the Modern Analytics Workflow. The purpose of data governance in the Modern Analytics Workflow is to ensure that the right data is available to the right people in the organization, at the time they need it. It creates accountability and enables, rather than restricts, access to secure and trusted content and for users of all skill levels. Data source management includes processes related to selection and distribution of data within your organization. Tableau connects to your enterprise data platforms and leverages the governance you already have applied to those systems.
Discover, manage, and share community curated metadata
Deriving value from data depends extensively on understanding the data and sharing knowledge among everyone who works with data. Sharing data knowledge is the core purpose of metadata. This online training course is designed to provide the foundational metadata knowledge needed by anyone who has data management roles and responsibilities. It covers metadata basics such as the types and purposes of metadata, and explores core metadata disciplines of data modeling, data profiling, and data cataloging. Metadata roles in data governance, stewardship, security, quality, and analysis are explained. Click - here - to download a more detailed outline of this course. This exam tests knowledge and understanding of basic concepts, principles, and terminology of data modeling and metadata management.
The Expanding Role of Metadata Management, Data Quality, and Data Governance
Application modernization can help your business improve application performance to support digital transformation goals. Often, enterprises operate their business-critical applications and processes in silos, causing digital transformation initiatives to stall or fail entirely. Instead, you need to integrate all applications to get a unified view across business operations, processes, and systems. This helps your business keep pace with ever-changing market demands and stay competitive.
Metadata Management Solutions
Data management middleware companies tend to be relatively small. Information management vendors such as IBM, Oracle, and SAP pick off smaller data management vendors and add their offerings as solutions to their overall platform portfolio to sell as enablers of their big data and cloud systems. Thus, data management and governance have lagged behind the big data and cloud trends. Even today, many of these vendors still offer one on-premises tool and another cloud tool. Newer ones may only run in the cloud. Venture capitalists and private equity firms jumped in to fund big data startups early.
DMBOK: Metadata Management
Jul 24, Meta Data. For the second time, Gartner is devoting a study to metadata management tools. Metadata has long been the poor relation of IT to data. Until then, there was little interest in exploiting these descriptions of information. Time has done its work.
Enterprise Metadata Management. Metadata defines the layout and lineage of transactional data and master data in the enterprise. According to Gartner's Guido de Simone, enterprise metadata management EMM is designed to link, reconcile, and govern the information in our enterprise.
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