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.

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 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

These data are derived from returns submitted to the Australian Prudential Regulation Authority (APRA) by banks authorised under the Banking Act 1959. APRA assumed responsibility for the supervision and regulation of banks on 1 July 1998. Data prior to that date were submitted to the RBA.

Up to and including June 2000, data are averages of weekly (Wednesday) figures. From July 2000, data are for the last business day of every month. Up to and including March 2002, banks submitted Form D (Statement of Liabilities and Assets on the Australian Books). In March 2002, APRA implemented new reporting forms for banks. The data, dating from April 2002, are derived from ARF 320.0 Statement of Financial Position (Domestic Books).

ARF 320.0 covers the domestic books of the licensed bank and is an unconsolidated report of the Australian bank’s operations/transactions that are booked or recorded inside Australia (with Australian residents and non-residents). ARF 320.0 does not consolidate Australian and offshore-controlled entities (thus offshore branches of the Australian bank are excluded). ARF 320.0 includes transactions of Australian-based offshore banking units of the licensed ADI but excludes transactions of overseas-based offshore banking units.

An Australian ‘resident’ is any individual, business or other organisation domiciled in Australia. Australian branches and subsidiaries of foreign businesses are regarded as Australian residents. A ‘non-resident’ is any individual, business or other organisation domiciled overseas. Foreign branches and subsidiaries of Australian businesses are regarded as non-residents.

‘Resident assets – notes and coins, and deposits due from RBA’ includes: Australian and foreign currency notes and coins; settlement account balances with the RBA and any other central bank; and any other funds held at the RBA.

‘Resident assets – bills receivables’ refers to assets arising from undertakings by customers to pay bills of exchange drawn by the banks. From April 2002, this item includes Australian dollar- and foreign currency-denominated (AUD equivalent) bill receivables. Prior to that date, foreign currency-denominated (AUD equivalent) bill receivables are included in ‘resident assets – other assets’.

‘Resident assets – loans and advances – residential’ include: owner-occupied and investment housing loans. ‘Resident assets – loans and advances – personal’ include: revolving credit; credit cards; personal lease financing; and other personal term loans. ‘Resident assets – loans and advances – commercial’ include: loans to community service organisations and non-profit institutions; loans to non-financial corporations; loans to general government; and loans to financial corporations. The loans and advances data are net of specific provisions for bad and doubtful debts, but gross of general provisions for bad and doubtful debts. Loans and advances exclude: bills of exchange, commercial paper, promissory notes, certificates of deposit, and some other debt securities. From April 2002, loans and advances refer to Australian dollar- and foreign currency-denominated (AUD equivalent) loans and advances. Prior to that date, foreign currency-denominated (AUD equivalent) loans and advances are included in ‘resident assets – other assets’.

‘Resident assets – other assets’ refers to all other resident assets not included in the above items. Prior to April 2002, this item includes: shares; bullion; past-due bills; accounts receivable; prepayments made; public sector securities; and all other resident assets other than accrued interest not yet receivable and intangible assets. From April 2002, this item includes: cash and liquid assets other than notes and coins and deposits due from RBA; trading and investment securities; fixed assets; intangible assets; other investments and all other assets not reported above. Note that, from April 2002, this item also includes unrealised gains on trading derivatives – prior to that date, these were excluded.

‘Resident assets – total’ refers to total assets on the Australian books of banks that are due from residents, and is the sum of the above items. ‘Resident assets – of which: denominated in foreign currency’ refers to the Australian dollar equivalent of ‘resident assets – total’ on the Australian books of banks that are denominated in foreign currency.

‘Non-resident assets – total’ refers to total assets on the Australian books of banks that are due from non-residents, though from April 2002, this series excludes the total amount due from banks’ overseas operations, which have been separately identified on the new reporting form. ‘Non-resident assets – of which: denominated in foreign currency’ refers to the Australian dollar equivalent of ‘non-resident assets – total’ on the Australian books of banks that are denominated in foreign currency.

‘Total assets’ is the sum of ‘resident assets – total’ and ‘non-resident assets – total’. From April 2002, this item also includes the ‘amount due from overseas operations’, which is identified separately from ‘resident assets – total’ and ‘non-resident assets – total’. The ‘amount due from overseas operations’ refers to domestic book on-balance sheet assets due from overseas operations of banks which have not been included in the above items.

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 dataset contains lists of various kinds of assets (open prior to or as of March 1, 2020), derived from a variety of local, state, and federal data sources. Some of this data still needs to be verified, as this is a work in progress. Some geocoding is either missing or incorrect. Improvements are pending.

This data was assembled to support 2020 Census outreach efforts, and can also be used to support COVID-19 response efforts.

A companion map displaying this data has also been created to support Census outreach. It too is a work in progress, and we'll be adding new features in the coming weeks.

Types of assets reflected in the data include (Note - we'll add additional source info. in coming days):

Allegheny County Housing Authority community sites, Allegheny County Health Department clinics, affordable housing properties, apartment buildings, banks, barbershops, bars, bus stops, child care centers, coffee shops, community nonprofit organizations, dentists, doctor's offices, faith-based facilities, family support centers, farmers' markets, food banks, gas stations, hair salons, health centers, homeless shelters, laundromats, libraries, museums, nail salons, nursing homes, park and rides, parks and facilities, pharmacies, polling places, post offices, public buildings, recreation centers, restaurants, schools, senior centers, supermarkets, universities, VA facilities, veterans social orgs, WIC offices, and WIC vendors.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

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

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

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.

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.

The approximate physical location of each individual Public Housing Building. If the building has more than one entrance or street address, the address of the entrance with the highest number of units and best possible geocode was chosen to represent the building location.

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

Indonesia Rural Banks: Assets: Total data was reported at 137,443.847 IDR bn in May 2019. This records a decrease from the previous number of 137,497.080 IDR bn for Apr 2019. Indonesia Rural Banks: Assets: Total data is updated monthly, averaging 68,464.712 IDR bn from Dec 2006 (Median) to May 2019, with 150 observations. The data reached an all-time high of 137,497.080 IDR bn in Apr 2019 and a record low of 23,045.000 IDR bn in Dec 2006. Indonesia Rural Banks: Assets: Total data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Global Database’s Indonesia – Table ID.KAC026: Rural Banks: Asset: by Province.

VBA Compensation and Pension related data assets

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.

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

• What is the average net worth of households within my trade area? • What proportions of my customers’ assets are held in chequing and savings accounts? Stocks? Bonds? Mutual Funds? • Which areas within my trade area have a high debt-to-asset ratio? • What types of debt do my customers have? Are their debts dominated by credit cards? HELOC? Mortgages? • Are the average real estate values around my business higher or lower than the average real estate values in my base market? • What proportion of my trade area holds an RRIF vs. an RRSP? • and more

Built using authoritative data from a variety of sources—such as the Bank of Canada, Canada Revenue Agency, and Statistics Canada—and sophisticated modelling techniques, WealthScapes is designed for financial planning, marketing, and targeting applications.

The latest version features a database of over 100 key financial and investment statistics to help financial institutions, charitable organizations, and large retailers better understand their customers' financial and investment behaviour. The key variables used in WealthScapes are created to match the best available control totals in Canada so that chief economists will endorse them.

This dataset provides information about access to public assets on the CT Open Data Portal by day. Types of access include:

-Grid view -Primer page view -Download -API read -Story page view -Visualization page view

It includes assets that meet the following criteria:

-Published on the data.ct.gov domain -Public -Official (ie published by a registered user) -Not a derived view

JO: Foreign Assets: Net data was reported at 8,516.050 JOD mn in 2016. This records an increase from the previous number of 7,913.331 JOD mn for 2015. JO: Foreign Assets: Net data is updated yearly, averaging 487.318 JOD mn from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 9,787.732 JOD mn in 2010 and a record low of 30.840 JOD mn in 1960. JO: Foreign Assets: Net data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank.WDI: Net Foreign Assets. Net foreign assets are the sum of foreign assets held by monetary authorities and deposit money banks, less their foreign liabilities. Data are in current local currency.; ; International Monetary Fund, International Financial Statistics and data files.; ;