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.

Explore the burgeoning Data Asset Map System market, projected for substantial growth driven by cloud adoption and intelligent analytics. Discover key trends, drivers, and leading companies shaping data governance and management.

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.

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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

VBA Insurance related data assets

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.

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

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.

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

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.

VBA Compensation and Pension related data assets

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.

Data Catalogs for Transportation Assets Market Outlook

According to our latest research, the global Data Catalogs for Transportation Assets market size was valued at USD 2.47 billion in 2024, reflecting a robust growth trajectory driven by increasing digitalization in the transportation sector. The market is set to expand at a CAGR of 17.2% from 2025 to 2033, reaching an estimated value of USD 7.98 billion by 2033. This impressive growth is primarily fueled by the rising adoption of advanced data management tools to optimize asset utilization, ensure regulatory compliance, and enhance operational efficiency across various transportation modes worldwide.

The primary growth driver for the Data Catalogs for Transportation Assets market is the growing complexity and scale of transportation networks. As transportation companies and public agencies handle increasingly diverse fleets, infrastructure, and equipment, the need for unified data cataloging solutions has become paramount. These platforms enable organizations to consolidate disparate data sources, streamline asset tracking, and facilitate predictive maintenance strategies. The integration of IoT devices and telematics further amplifies the data volume, necessitating robust cataloging tools to ensure data integrity and accessibility. The convergence of cloud computing and big data analytics is empowering transportation stakeholders to derive actionable insights from vast asset datasets, driving market expansion.

Another significant factor propelling market growth is the regulatory environment that mandates stringent asset compliance and reporting standards. Governments and industry bodies across regions are enforcing rigorous asset documentation, lifecycle management, and safety protocols, making data catalogs indispensable for meeting these requirements. The rise of sustainability initiatives and smart city projects is also contributing to market momentum, as transportation operators are compelled to manage assets more efficiently while minimizing environmental impact. The increasing focus on transparency and accountability in public transportation and logistics is further encouraging the adoption of data catalog solutions.

Technological advancements are transforming the way transportation assets are managed, with artificial intelligence, machine learning, and blockchain technologies being integrated into data catalog platforms. These innovations are enhancing the accuracy of asset tracking, enabling real-time monitoring, and automating compliance processes. The proliferation of connected vehicles, smart infrastructure, and digital twins is creating new opportunities for data catalog vendors to offer specialized solutions tailored to the unique needs of the transportation industry. As organizations continue to invest in digital transformation, the demand for scalable, interoperable, and secure data cataloging systems is expected to surge.

From a regional perspective, North America currently dominates the Data Catalogs for Transportation Assets market, accounting for over 38% of the global revenue in 2024. This leadership is attributed to the presence of technologically advanced transportation networks, high adoption of digital solutions, and significant investments in smart infrastructure. However, the Asia Pacific region is projected to witness the fastest growth during the forecast period, with a CAGR exceeding 19%, fueled by rapid urbanization, expanding logistics sectors, and government initiatives aimed at modernizing transportation systems. Europe and Latin America are also experiencing steady growth, driven by the increasing emphasis on sustainability and compliance in asset management.

Component Analysis

The Component segment of the Data Catalogs for Transportation Assets market is bifurcated into Solutions and Services. Solutions comprise the core software platforms that enable organizatio

This dataset represents the total number of Open Data Portal assets and the frequency of how often the asset is accessed. This data is collected by using Socrata Analytics. This dataset supports measure GTW.G.4 of SD23. Data Source: Socrata. Calculations: (GTW.G.4) Percentage of datasets published in the Open Data portal that are being accessed frequently (such as through a website views, API interactions, embeds or mobile views). Measure Time Period: Fiscal Year Annually Automated: No Date of Last description update: 4/1/2020 For questions please contact CTMCollaborationServices@austintexas.gov

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

Land and Property Assets in York. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

This dataset provides information on Social Housing Asset Data at Salford City Council. Details are provided to meet the required standards of the Local Government Transparency Code 2014.

With the rapid expansion of non-customized data assets, developing reliable and objective methods for their valuation has become essential. However, current evaluation techniques often face challenges such as incomplete indicator systems and an over-reliance on subjective judgment. To address these issues, this study presents a structured framework comprising 17 key indicators for assessing data asset value. A neural network is employed to calculate indicator weights, which reduces subjectivity and enhances the accuracy of the assessment. Additionally, knowledge graph techniques are used to organize and visualize relationships among the indicators, providing a comprehensive evaluation view. The proposed model combines information entropy and the TOPSIS method to refine asset valuation by integrating indicator weights and performance metrics. To validate the model, it is applied to two datasets: Bitcoin market data from the past seven years and BYD stock data. The Bitcoin dataset demonstrates the model’s capability to capture market trends and assess purchasing potential, while the BYD stock dataset highlights its adaptability across diverse financial assets. The successful application of these cases confirms the model’s effectiveness in supporting data-driven asset management and pricing. This framework provides a systematic methodology for data asset valuation, offering significant theoretical and practical implications for asset pricing and management.

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

With the rapid expansion of non-customized data assets, developing reliable and objective methods for their valuation has become essential. However, current evaluation techniques often face challenges such as incomplete indicator systems and an over-reliance on subjective judgment. To address these issues, this study presents a structured framework comprising 17 key indicators for assessing data asset value. A neural network is employed to calculate indicator weights, which reduces subjectivity and enhances the accuracy of the assessment. Additionally, knowledge graph techniques are used to organize and visualize relationships among the indicators, providing a comprehensive evaluation view. The proposed model combines information entropy and the TOPSIS method to refine asset valuation by integrating indicator weights and performance metrics. To validate the model, it is applied to two datasets: Bitcoin market data from the past seven years and BYD stock data. The Bitcoin dataset demonstrates the model’s capability to capture market trends and assess purchasing potential, while the BYD stock dataset highlights its adaptability across diverse financial assets. The successful application of these cases confirms the model’s effectiveness in supporting data-driven asset management and pricing. This framework provides a systematic methodology for data asset valuation, offering significant theoretical and practical implications for asset pricing and management.