Displays incomplete Published data assets. This report can be used to help improve the Data Asset Completeness score from the Enterprise Data Management (EDM) Scorecard by identifying which missing fields are required for completeness.

Displays Anticipated Systems not mapped to a Published data asset. An Anticipated System can be described as a system expected to have at least one associated data asset. It excludes systems exclusively designated as Network, WAN, Web Server and has a Lifecycle Configuration Status as Canceled, Decommission, Deleted, Development, Discontinued, Disposal, or Renamed. This report can be used to help improve the Percentage of Data Assets to Anticipated Systems Mapped Score from Enterprise Data Management (EDM) Scorecard by identifying which anticipated system need to be mapped to a data asset.

Report of Data Asset Management Market is currently supplying a comprehensive analysis of many things which are liable for economy growth and factors which could play an important part in the increase of the marketplace in the prediction period. The record of Data Asset Management Industry is providing the thorough study on the grounds of market revenue discuss production and price happened. The report also provides the overview of the segmentation on the basis of area, contemplating the particulars of earnings and sales pertaining to marketplace.

The global Data Asset Management (DAM) in Finance market is experiencing robust growth, driven by the increasing need for efficient management of vast amounts of financial data, including regulatory compliance demands and the rise of digital transformation initiatives within financial institutions. The market, estimated at $10 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $30 billion by 2033. This expansion is fueled by several key factors. Firstly, the growing volume of unstructured data within financial organizations necessitates sophisticated DAM solutions to improve accessibility, searchability, and overall data governance. Secondly, stricter regulatory compliance requirements, such as GDPR and CCPA, necessitate robust data management strategies to ensure data privacy and security. Finally, the increasing adoption of cloud-based DAM solutions offers scalability, cost-effectiveness, and improved collaboration across geographically dispersed teams within financial institutions. The market is segmented by application (Government, Small and Medium-sized Enterprises (SMEs), and Large Enterprises) and by type (cloud-based and local-based). Large enterprises currently dominate the market due to their greater investment capacity and data volume. However, SMEs are expected to show significant growth in the coming years as they adopt cloud-based solutions to overcome resource limitations. Cloud-based solutions are leading the market due to their flexibility and accessibility. Geographically, North America and Europe currently hold the largest market share due to early adoption of DAM technologies and stringent regulatory frameworks. However, Asia Pacific is projected to exhibit significant growth, driven by increasing digitalization across the finance sector in developing economies. Competition in the DAM in Finance market is intense, with a mix of established players like Adobe and newer entrants. Key players are constantly innovating to offer advanced features such as AI-powered metadata tagging, enhanced security, and integration with other financial technology solutions. The market is also seeing the emergence of specialized DAM solutions tailored specifically to the financial sector, addressing unique challenges and compliance requirements. While high initial investment costs and the need for skilled professionals can be restraining factors, the long-term benefits of improved data management, reduced compliance risks, and enhanced operational efficiency are driving market growth. Future growth will depend on continued technological advancements, regulatory changes, and the increasing awareness of the importance of effective data asset management within financial organizations.

An National Geospatial Data Asset (NGDA) is defined as a geospatial dataset that has been designated by the FGDC Steering Committee and meets at least one of the following criteria: used by multiple agencies or with agency partners such as State, Tribal and local governments; applied to achieve Presidential priorities as expressed by OMB; required to meet shared mission goals of multiple Federal agencies; or expressly required by statutory mandate. Together, these datasets comprise the NGDA Portfolio. This metadata points to a spreadsheet that contains the official list of NGDA with a link to specific NGDA metadata maintained by the dataset owners on Data.gov, GeoPlatform.gov, a link to their associated NGDA Theme, and the agency responsible for the NGDA.

An interactive dashboard that provides an inventory of open data assets and visually presents an overview of the various types of data assets classified as public that have been published to the City of Austin Open Data Portal (data.austintexas.gov) by departmental data owners. *City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj‐cccq

Ethical Data ManagementExecutive SummaryIn the age of data and information, it is imperative that the City of Virginia Beach strategically utilize its data assets. Through expanding data access, improving quality, maintaining pace with advanced technologies, and strengthening capabilities, IT will ensure that the city remains at the forefront of digital transformation and innovation. The Data and Information Management team works under the purpose:“To promote a data-driven culture at all levels of the decision making process by supporting and enabling business capabilities with relevant and accurate information that can be accessed securely anytime, anywhere, and from any platform.”To fulfill this mission, IT will implement and utilize new and advanced technologies, enhanced data management and infrastructure, and will expand internal capabilities and regional collaboration.Introduction and JustificationThe Information technology (IT) department’s resources are integral features of the social, political and economic welfare of the City of Virginia Beach residents. In regard to local administration, the IT department makes it possible for the Data and Information Management Team to provide the general public with high-quality services, generate and disseminate knowledge, and facilitate growth through improved productivity.For the Data and Information Management Team, it is important to maximize the quality and security of the City’s data; to develop and apply the coherent management of information resources and management policies that aim to keep the general public constantly informed, protect their rights as subjects, improve the productivity, efficiency, effectiveness and public return of its projects and to promote responsible innovation. Furthermore, as technology evolves, it is important for public institutions to manage their information systems in such a way as to identify and minimize the security and privacy risks associated with the new capacities of those systems.The responsible and ethical use of data strategy is part of the City’s Master Technology Plan 2.0 (MTP), which establishes the roadmap designed by improve data and information accessibility, quality, and capabilities throughout the entire City. The strategy is being put into practice in the shape of a plan that involves various programs. Although these programs was specifically conceived as a conceptual framework for achieving a cultural change in terms of the public perception of data, it basically covers all the aspects of the MTP that concern data, and in particular the open-data and data-commons strategies, data-driven projects, with the aim of providing better urban services and interoperability based on metadata schemes and open-data formats, permanent access and data use and reuse, with the minimum possible legal, economic and technological barriers within current legislation.Fundamental valuesThe City of Virginia Beach’s data is a strategic asset and a valuable resource that enables our local government carry out its mission and its programs effectively. Appropriate access to municipal data significantly improves the value of the information and the return on the investment involved in generating it. In accordance with the Master Technology Plan 2.0 and its emphasis on public innovation, the digital economy and empowering city residents, this data-management strategy is based on the following considerations.Within this context, this new management and use of data has to respect and comply with the essential values applicable to data. For the Data and Information Team, these values are:Shared municipal knowledge. Municipal data, in its broadest sense, has a significant social dimension and provides the general public with past, present and future knowledge concerning the government, the city, society, the economy and the environment.The strategic value of data. The team must manage data as a strategic value, with an innovative vision, in order to turn it into an intellectual asset for the organization.Geared towards results. Municipal data is also a means of ensuring the administration’s accountability and transparency, for managing services and investments and for maintaining and improving the performance of the economy, wealth and the general public’s well-being.Data as a common asset. City residents and the common good have to be the central focus of the City of Virginia Beach’s plans and technological platforms. Data is a source of wealth that empowers people who have access to it. Making it possible for city residents to control the data, minimizing the digital gap and preventing discriminatory or unethical practices is the essence of municipal technological sovereignty.Transparency and interoperability. Public institutions must be open, transparent and responsible towards the general public. Promoting openness and interoperability, subject to technical and legal requirements, increases the efficiency of operations, reduces costs, improves services, supports needs and increases public access to valuable municipal information. In this way, it also promotes public participation in government.Reuse and open-source licenses. Making municipal information accessible, usable by everyone by default, without having to ask for prior permission, and analyzable by anyone who wishes to do so can foster entrepreneurship, social and digital innovation, jobs and excellence in scientific research, as well as improving the lives of Virginia Beach residents and making a significant contribution to the city’s stability and prosperity.Quality and security. The city government must take firm steps to ensure and maximize the quality, objectivity, usefulness, integrity and security of municipal information before disclosing it, and maintain processes to effectuate requests for amendments to the publicly-available information.Responsible organization. Adding value to the data and turning it into an asset, with the aim of promoting accountability and citizens’ rights, requires new actions, new integrated procedures, so that the new platforms can grow in an organic, transparent and cross-departmental way. A comprehensive governance strategy makes it possible to promote this revision and avoid redundancies, increased costs, inefficiency and bad practices.Care throughout the data’s life cycle. Paying attention to the management of municipal registers, from when they are created to when they are destroyed or preserved, is an essential part of data management and of promoting public responsibility. Being careful with the data throughout its life cycle combined with activities that ensure continued access to digital materials for as long as necessary, help with the analytic exploitation of the data, but also with the responsible protection of historic municipal government registers and safeguarding the economic and legal rights of the municipal government and the city’s residents.Privacy “by design”. Protecting privacy is of maximum importance. The Data and Information Management Team has to consider and protect individual and collective privacy during the data life cycle, systematically and verifiably, as specified in the general regulation for data protection.Security. Municipal information is a strategic asset subject to risks, and it has to be managed in such a way as to minimize those risks. This includes privacy, data protection, algorithmic discrimination and cybersecurity risks that must be specifically established, promoting ethical and responsible data architecture, techniques for improving privacy and evaluating the social effects. Although security and privacy are two separate, independent fields, they are closely related, and it is essential for the units to take a coordinated approach in order to identify and manage cybersecurity and risks to privacy with applicable requirements and standards.Open Source. It is obligatory for the Data and Information Management Team to maintain its Open Data- Open Source platform. The platform allows citizens to access open data from multiple cities in a central location, regional universities and colleges to foster continuous education, and aids in the development of data analytics skills for citizens. Continuing to uphold the Open Source platform with allow the City to continually offer citizens the ability to provide valuable input on the structure and availability of its data. Strategic areasIn order to deploy the strategy for the responsible and ethical use of data, the following areas of action have been established, which we will detail below, together with the actions and emblematic projects associated with them.In general, the strategy pivots on the following general principals, which form the basis for the strategic areas described in this section.Data sovereigntyOpen data and transparencyThe exchange and reuse of dataPolitical decision-making informed by dataThe life cycle of data and continual or permanent accessData GovernanceData quality and accessibility are crucial for meaningful data analysis, and must be ensured through the implementation of data governance. IT will establish a Data Governance Board, a collaborative organizational capability made up of the city’s data and analytics champions, who will work together to develop policies and practices to treat and use data as a strategic asset.Data governance is the overall management of the availability, usability, integrity and security of data used in the city. Increased data quality will positively impact overall trust in data, resulting in increased use and adoption. The ownership, accessibility, security, and quality, of the data is defined and maintained by the Data Governance Board.To improve operational efficiency, an enterprise-wide data catalog will be created to inventory data and track metadata from various data sources to allow for rapid data asset discovery. Through the data catalog, the city will

This table is the primary table for information about work orders, and contains general information - including a description of the work, assigned title, request date and completion date - about each work order. Each row represents a single work order. The primary key field is EVT_CODE. The EVT_OBJECT field can be joined to the Assets table on OBJ_CODE to know which asset the work order was for.

For the User Guide, please follow this link For the Data Dictionary, please follow this link

Information about accesses (visits) of city data assets. Combines analytics from both employee (citydata.mesaaz.gov) and public data (data.mesaaz.gov) portals.

The following usage types are included in the Access Type column: grid view – tabular view of the dataset / filtered view primer page view – dataset / filtered view’s homepage, includes metadata and table preview of the data download – download of the dataset / filtered view to CSV, JSON, etc. api read access – programmatic access of dataset/filtered vew, etc. story page view – accessing a story page asset visualization page view – accessing a chart or map asset measure page view – accessing a performance measure asset

Usage data are segmented into the following user types: site member: users who have logged in and have been granted a role on the domain community user: users who have logged in but do not have a role on the domain anonymous: users who have not logged in to the domain Data are updated by a system process at least once a day.

Please see Site Analytics: Asset Access for more detail.

This data asset contains the datasets for the Gender and Inclusive Development Analysis: Final Report, prepared by International Business and Technical Consultants. The Analysis team administered an online survey for USAID implementing partners. The online questionnaire was sent via Survey Monkey to 30 partners organizations, from a list provided by USAID. Twenty-nine IPs replied; one organization was discarded because it is not an implementing partner.

This asset is a derived view based on the system dataset 'Site Analytics: Asset Inventory' which is automatically generated by the data management platform and provides a comprehensive inventory of all assets on this site. This asset has been filtered to present an overview of the various types of data that are classified as public and have been published on the City of Austin Open Data Portal (data.austintexas.gov) by departmental data owners.

The columns of the Asset Inventory dataset contain information about every asset. These include metadata fields (e.g., Name, Description, and Category), as well as statistics, such as the number of visits, row count, column count, and downloads. This asset is updated at least once per day to sync any changes, additional assets, or removed assets.

Data provided by: Tyler Technologies Creation date of data source: November 1, 2022

*City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

The information that is collected, used, disseminated, or maintained in the International Technology Services Admin Services system is used for user identification, authorization, and authentication purposes and can include the user’s name, organizational unit information, office telephone number, electronic mail address, and physical office address to adequately identify the individual for Help Desk support purposes.

Data Center Asset Management Market is expected to reach USD 9,924.99 Million by 2034, expanding at a CAGR of 15.2% from 2025 to 2034

VBA Compensation and Pension related data assets

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

Click here to download data from 2011 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2011/fpz8-jqf4

Click here to download data from 2012 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2012/as38-8eb5

Click here to download data from 2013 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2013/hybb-af8n

Click here to download data from 2014 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2014/vtzg-7562

Click here to download data from 2015 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2015/57g5-etyj

Polygon dataset of Sevenoaks District Assets of Community Value - API and download in British National Grid.

Graph and download economic data for Mutual Funds; Total Financial Assets, Market Value Levels (BOGZ1LM654090000Q) from Q4 1945 to Q1 2025 about mutual funds, assets, and USA.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This database is an initial Asset database for the Central West subregion on 29 April 2015. This dataset contains the spatial and non-spatial (attribute) components of the Central West subregion Asset List as one .mdb files, which is readable as an MS Access database and a personal geodatabase. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. All reports received associated with the WAIT process for Central West are included in the zip file as part of this dataset. Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Central West subregion are found in the "AssetList" table of the database. In this version of the database only M1 has been assessed. Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "CEN_asset_database_doc_20150429.doc ", located in the zip file as part of this dataset. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "CEN_asset_database_doc_20150429.doc" located in the zip file. Some of the source data used in the compilation of this dataset is restricted.

Dataset History

This is initial asset database.

The Bioregional Assessments methodology (Barrett et al., 2013) defines a water-dependent asset as a spatially distinct, geo-referenced entity contained within a bioregion with characteristics having a defined cultural indigenous, economic or environmental value, and that can be linked directly or indirectly to a dependency on water quantity and/or quality.

Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. Elements are initially included in database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet materiality test 2 (M2) - assets considered to be water dependent.

Elements may be represented by a single, discrete spatial unit (polygon, line or point), or a number of spatial units occurring at more than one location (multipart polygons/lines or multipoints). Spatial features representing elements are not clipped to the preliminary assessment extent - features that extend beyond the boundary of the assessment extent have been included in full. To assist with an assessment of the relative importance of elements, area statements have been included as an attribute of the spatial data. Detailed attribute tables contain descriptions of the geographic features at the element level. Tables are organised by data source and can be joined to the spatial data on the "ElementID" field

Elements are grouped into Assets, which are the objects used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy.

The "Element_to_asset" table contains the relationships and identifies the elements that were grouped to create each asset.

Following delivery of the first pass asset list, project teams make a determination as to whether an asset (comprised of one or more elements) is water dependent, as assessed against the materiality tests detailed in the BA Methodology. These decisions are provided to ERIN by the project team leader and incorporated into the Assetlist table in the Asset database. The Asset database is then re-registered into the BA repository.

The Asset database dataset (which is registered to the BA repository) contains separate spatial and non-spatial databases.

Non-spatial (tabular data) is provided in an ESRI personal geodatabase (.mdb - doubling as a MS Access database) to store, query, and manage non-spatial data. This database can be accessed using either MS Access or ESRI GIS products. Non-spatial data has been provided in the Access database to simplify the querying process for BA project teams. Source datasets are highly variable and have different attributes, so separate tables are maintained in the Access database to enable the querying of thematic source layers.

Spatial data is provided as an ESRI file geodatabase (.gdb), and can only be used in an ESRI GIS environment. Spatial data is represented as a series of spatial feature classes (point, line and polygon layers). Non-spatial attribution can be joined from the Access database using the AID and ElementID fields, which are common to both the spatial and non-spatial datasets. Spatial layers containing all the point, line and polygon - derived elements and assets have been created to simplify management of the Elementlist and Assetlist tables, which list all the elements and assets, regardless of the spatial data geometry type. i.e. the total number of features in the combined spatial layers (points, lines, polygons) for assets (and elements) is equal to the total number of non-spatial records of all the individual data sources.

Dataset Citation

Department of the Environment (2013) Asset database for the Central West subregion on 29 April 2015. Bioregional Assessment Derived Dataset. Viewed 08 February 2017, http://data.bioregionalassessments.gov.au/dataset/5c3f9a56-7a48-4c26-a617-a186c2de5bf7.

Dataset Ancestors

• Derived From Macquarie Marshes Vegetation 1991-2008 VIS_ID 3920

• Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014)

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From Travelling Stock Route Conservation Values

• Derived From NSW Wetlands

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Birds Australia - Important Bird Areas (IBA) 2009

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From NSW Office of Water Surface Water Offtakes - NIC v1 20131024

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Ramsar Wetlands of Australia

• Derived From Native Vegetation Management (NVM) - Manage Benefits

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Climate Change Corridors (Dry Habitat) for North East NSW

• Derived From Great Artesian Basin and Laura Basin groundwater recharge areas

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From [New South Wales NSW Regional CMA Water Asset

United States - U.S. Assets was 35885452.00000 Mil. of $ in January of 2024, according to the United States Federal Reserve. Historically, United States - U.S. Assets reached a record high of 35885452.00000 in January of 2024 and a record low of 371424.00000 in January of 1976. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - U.S. Assets - last updated from the United States Federal Reserve on August of 2025.

Turkey Household Financial Assets: TRY Deposits data was reported at 591.000 TRY bn in Mar 2018. This records an increase from the previous number of 535.100 TRY bn for Sep 2017. Turkey Household Financial Assets: TRY Deposits data is updated semiannually, averaging 431.800 TRY bn from Sep 2013 (Median) to Mar 2018, with 10 observations. The data reached an all-time high of 591.000 TRY bn in Mar 2018 and a record low of 341.100 TRY bn in Sep 2013. Turkey Household Financial Assets: TRY Deposits data remains active status in CEIC and is reported by Central Bank of the Republic of Turkey. The data is categorized under Global Database’s Turkey – Table TR.KB040: Households Financial Asset Composition.