In an August 2019 survey of global marketing professionals, ** percent of respondents said they trusted third party research from their vendors or partners. In-house data provided by their research teams was the second most trusted source among surveyed marketers.

OAN's Third Party Audience Data provides targeted audiences for online advertising campaigns. With the ever-increasing competition in the digital advertising space, businesses must reach the right audience to maximize their marketing efforts. OAN offers a comprehensive solution by leveraging third-party data sources to identify and segment audiences based on various demographic, behavioral, and interest-based attributes.

Key features: - 1 billion unique Xandr IDs/ month, globally - 1 billion unique Mobile IDs/ month, globally - 887 IAB-compliant segments - 500 segments of players globally - We gather and provide non-cookie IDs - for example, Universal IDs, CTV IDs and Mobile Ad IDs

How you can use our data? - Marketers - targeting online campaigns With our high-quality audience data, you can easily reach specific audiences across the world in programmatic campaigns. Show them personalized ads adjusted to their specific profiles. - Ad tech companies Enriching 1st party data or using our raw data by your own data science team.

Our third party audience dataset is collected from various reliable sources, ensuring its accuracy and reliability. We provide GDPR and CCPA-compliant data that is constantly updated and enriched, providing businesses with the most up-to-date segmented information on consumer behavior and preferences.

Snapshot img

Third-Party Risk Management Market Size 2025-2029

The third-party risk management market size is forecast to increase by USD 9.78 billion, at a CAGR of 18.5% between 2024 and 2029.

The market is experiencing significant growth and transformation, driven by the increasing adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in third-party risk management software solutions. These technologies enable organizations to automate risk assessments, monitor risks in real-time, and make data-driven decisions, thereby improving operational efficiency and reducing risks. However, the market also faces challenges, including the emergence of open-source risk management software. While open-source solutions offer cost advantages, they may lack the advanced features and capabilities of proprietary software, potentially compromising the effectiveness of risk management efforts. Organizations must carefully evaluate the trade-offs between cost savings and risk mitigation capabilities when considering open-source solutions. Effective third-party risk management is crucial for businesses seeking to protect their reputation, mitigate financial losses, and ensure regulatory compliance. Companies can capitalize on market opportunities by investing in AI- and ML-powered third-party risk management software, while addressing challenges by conducting thorough evaluations of open-source solutions.

What will be the Size of the Third-Party Risk Management Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, with dynamic market dynamics shaping its applications across various sectors. Access control and risk avoidance remain key priorities, as entities seek to mitigate potential threats posed by external partners. Performance indicators and company management are essential tools for measuring and optimizing third-party relationships, while supplier diversity and performance measurement help ensure ethical sourcing and maintain compliance with regulatory frameworks. Key risk indicators, data loss prevention, and compliance monitoring are critical components of effective third-party risk management. Strategic risk, regulatory frameworks, and security audits are integral to managing risks associated with third-party relationships. Reputational risk and stakeholder engagement are also crucial, as entities strive to maintain a positive public image and build strong partnerships. Risk monitoring, policy development, metrics reporting, identity management, financial risk, vulnerability management, business continuity, technology solutions, data analytics, scenario planning, contract lifecycle management, information governance, quantitative analysis, and governance framework are all integral to the ongoing management of third-party risks. Disaster recovery, ethical sourcing, data security, training programs, contract negotiation, communication strategy, risk appetite, board reporting, incident response, due diligence, fraud detection, compliance audits, insurance policies, risk transfer, penetration testing, risk mitigation, predictive modeling, threat intelligence, risk assessment, risk tolerance, legal counsel, internal controls, and qualitative analysis are all essential elements of a comprehensive third-party risk management strategy. As market dynamics continue to unfold, entities must remain vigilant and adapt to evolving risks and regulatory requirements. By implementing robust third-party risk management practices, organizations can mitigate risks, optimize performance, and build strong, sustainable partnerships.

How is this Third-Party Risk Management Industry segmented?

The third-party risk management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentSolutionServiceDeploymentCloudOn-premisesConsumerLarge enterprisesSMEsEnd-userBFSIIT and telecomHealthcareRetailOthersServiceProfessional servicesManagement servicesGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW)

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period.Third-party risk management solutions have gained significant importance in business organizations, particularly in managing risks associated with external entities such as companies, suppliers, and contractors. These solutions offer software-as-a-service (SaaS) that provides a real-time, integrated view of the extended enterprise to mitigate third-party risks. The offerings automate end-to-end processes, including information gathering,

Data includes the following information for each service provider: * region (the ministry's geographical boundary) * program * service provider ID * service provider name (legal name) * service provider address * service delivery site ID * service delivery site address Service providers have contractual agreements with the Ministry of Training, Colleges and Universities to deliver a program. Service delivery sites are the physical locations where these programs are offered. There may be multiple service delivery sites for each service provider. Visit Employment Ontario for more information.

At Factori, we gather various data sets from leading publishers, data platforms, online services, and data aggregators globally, linked to consumers, places, and businesses. We combine this data with public and private sources to derive interests, intent, and behavioral attributes. Our proprietary algorithms clean, enrich, unify, and aggregate these data sets for use in our products. We categorize our audience data into consumable categories such as interest, demographics, behavior, geography, etc.

Third-Party Banking Software Market Outlook

The global third-party banking software market size was valued at approximately USD 26.4 billion in 2023 and is projected to reach around USD 53.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.3% during the forecast period. The surge in digital banking trends, coupled with the increasing need for robust security measures and efficient risk management solutions, is driving the market's growth.

One of the prominent growth factors for this market is the rapid digital transformation occurring within the banking sector. Banks are increasingly adopting third-party software solutions to enhance operational efficiency, meet regulatory requirements, and offer better customer experiences. The advent of technologies such as artificial intelligence (AI), machine learning (ML), and blockchain has further accelerated this transformation, providing banks with sophisticated tools to combat fraud, optimize operations, and personalize customer interactions. Additionally, the growing trend of open banking, which mandates banks to provide third-party providers access to their financial data through APIs, has catalyzed the demand for third-party banking software to facilitate seamless and secure data exchange.

Another critical driver is the increasing prevalence of cyber threats and financial crimes. The banking sector is a prime target for cyberattacks, necessitating robust information security solutions. Third-party banking software providers are continuously innovating to offer advanced security features that protect sensitive financial data, detect suspicious activities, and comply with stringent regulatory standards. The implementation of security solutions is not just a regulatory requirement but also a strategic imperative to build trust and credibility with customers. Enhanced security features, such as real-time monitoring, biometric authentication, and end-to-end encryption, are becoming indispensable components of modern banking infrastructure.

The growing inclination towards customer-centric banking is also propelling the market. Banks are focusing on providing personalized services and seamless digital experiences to retain and attract customers. Third-party banking software helps banks analyze customer data and derive valuable insights, enabling them to tailor products and services according to individual preferences. Business intelligence and analytical tools are gaining traction as they assist banks in understanding consumer behavior, predicting market trends, and making data-driven decisions. The integration of customer relationship management (CRM) systems with banking software is further enhancing customer engagement and loyalty.

Regionally, the Asia Pacific market is anticipated to witness substantial growth owing to the rapid adoption of digital banking solutions and increasing investments in fintech. Countries like China, India, and Japan are at the forefront of this transformation, driven by favorable government initiatives, a large unbanked population, and the proliferation of smartphones. North America and Europe are also significant markets, characterized by a high degree of technological adoption, mature banking sectors, and stringent regulatory landscapes. Latin America and the Middle East & Africa are emerging markets with considerable growth potential, buoyed by improving economic conditions and increasing penetration of digital banking services.

In the realm of financial technology, Banking Accounting Software plays a pivotal role in streamlining financial operations for banks and financial institutions. This software is designed to manage and automate the accounting processes, ensuring accuracy and compliance with financial regulations. By integrating with existing banking systems, it provides real-time financial insights and reporting capabilities, which are crucial for strategic decision-making. The adoption of such software not only enhances operational efficiency but also reduces the risk of human error in financial transactions. As banks continue to evolve in the digital age, the demand for robust Banking Accounting Software is expected to rise, providing a competitive edge in the market.

Deployment Type Analysis

The deployment type segment of the third-party banking software market is bifurcated into on-premises and cloud-based solutions. On-premises deployment involves hosting software within the bank's own infrastructure, providing complete

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Indonesia Commercial Banks: BUKU3: Source of Funds (SoF): Third Party Funds data was reported at 1,648,202.579 IDR bn in May 2019. This records a decrease from the previous number of 1,670,371.930 IDR bn for Apr 2019. Indonesia Commercial Banks: BUKU3: Source of Funds (SoF): Third Party Funds data is updated monthly, averaging 1,585,771.277 IDR bn from Jan 2015 (Median) to May 2019, with 53 observations. The data reached an all-time high of 1,785,294.643 IDR bn in Feb 2019 and a record low of 1,282,366.469 IDR bn in Jan 2015. Indonesia Commercial Banks: BUKU3: Source of Funds (SoF): Third Party Funds data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAE002: Sources and Uses of Fund: by Bank.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Indonesia Commercial Banks: KBMI 3: Source of Funds: Third Party Funds data was reported at 2,313,847.477 IDR bn in Feb 2025. This records an increase from the previous number of 2,311,322.717 IDR bn for Jan 2025. Indonesia Commercial Banks: KBMI 3: Source of Funds: Third Party Funds data is updated monthly, averaging 1,992,488.233 IDR bn from Oct 2021 (Median) to Feb 2025, with 41 observations. The data reached an all-time high of 2,313,847.477 IDR bn in Feb 2025 and a record low of 1,800,855.813 IDR bn in Oct 2021. Indonesia Commercial Banks: KBMI 3: Source of Funds: Third Party Funds 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.KAF002: Sources and Uses of Fund: by Bank.

Snapshot img

Third-Party Banking Software Market Size 2025-2029

The third-party banking software market size is forecast to increase by USD 10.56 billion at a CAGR of 6.6% between 2024 and 2029.

The market is witnessing significant growth, driven by the increasing adoption of digital payment solutions and the incorporation of advanced analytics capabilities. Digital transformation in the banking sector is leading to a surge in demand for third-party banking software that enables seamless integration with various digital payment platforms and provides real-time transaction processing and analysis. Furthermore, the integration of analytics into third-party banking software is enabling financial institutions to gain valuable insights into customer behavior and preferences, thereby enhancing customer experience and loyalty. However, the market also faces challenges related to data privacy and security.
With the increasing use of digital channels for banking transactions, ensuring the security and privacy of customer data is paramount. Breaches and cyber-attacks pose a significant threat to financial institutions and can result in reputational damage and financial losses. Therefore, third-party banking software providers must prioritize data security and privacy to gain the trust of financial institutions and their customers. Additionally, regulatory compliance is another challenge, with financial institutions requiring third-party software providers to adhere to stringent regulatory frameworks to ensure data security and privacy. Companies seeking to capitalize on market opportunities and navigate challenges effectively must focus on providing robust data security and privacy features and ensuring regulatory compliance.

What will be the Size of the Third-Party Banking Software Market during the forecast period?

Request Free Sample

The market continues to evolve, driven by the ever-changing needs of financial institutions and their customers. User interfaces are becoming more intuitive, enabling seamless customer acquisition and retention. Open banking and financial wellness initiatives are integrating personalized services, data analytics, and payment processing to enhance the digital banking experience. Businesses are leveraging real-time data, API integration, and machine learning to optimize financial planning and investment management. Workflow automation and artificial intelligence are streamlining customer relationship management and wealth management processes. Digital transformation is also revolutionizing enterprise resource planning and financial education. Moreover, the integration of loan origination, data visualization, and agile development is enabling financial institutions to provide more efficient and effective services.
Fraud detection and financial inclusion are also becoming essential components of the market, ensuring security and accessibility for all customers. The ongoing digital banking revolution is transforming the financial landscape, with mobile banking and cloud computing playing a significant role. The market's continuous dynamism is reflected in its ability to adapt to emerging trends, such as financial literacy and account aggregation, and incorporate them into its offerings. The future of the market is bright, with endless possibilities for innovation and growth.

How is this Third-Party Banking Software Industry segmented?

The third-party banking software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Core banking software
  Omnichannel banking software
  Business intelligence software
  Wealth management software


Deployment

  On-premises
  Cloud


Application

  Risk Management
  Information Security
  Business Intelligence


Service Model

  Managed Services
  Professional Services
  Implementation Services


Geography

  North America

    US
    Canada


  South America

    Brazil
    Argentina


  Middle East and Africa

    UAE


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The core banking software segment is estimated to witness significant growth during the forecast period.

The market encompasses various solutions that empower financial institutions to enhance their operations and deliver superior customer experiences. Core banking software, a significant segment, focuses on essential banking processes such as loan, credit, deposit, and funds transfer. Multi-channel access via ATMs, Internet banking, and phone banking are also facilitated through this software. The retail banking sector's expansion, driven by government initiatives encouraging account opening, is fueling the demand f

According to Cognitive Market Research, The Global market for Information Services was USD 140.9 billion in 2022 and will grow at a 7.80% CAGR from 2023 to 2030. Market Dynamics of

Information Services Market

Key Drivers for

Information Services Market

Data generation is expanding exponentially: The digital transformation across industries has produced massive quantities of structured and unstructured data, which has increased the need for data processing and analytics services. Information services are essential for organizations to extract practical knowledge from huge datasets. Cloud computing supports real-time analysis and scalable data storage. Risk management and regulatory compliance needs: Businesses are now compelled to use specialized information services due to increased data privacy legislation (GDPR, CCPA) and financial reporting standards. Demand for compliance is driven by industries such as healthcare, finance, and the law. Third-party providers are knowledgeable about how regulations are changing. Integration of AI and automation: The speed and correctness of information services are increased by the integration of sophisticated analytics, machine learning, and natural language processing. Automated data curation and predictive modeling lessen manual labor while enhancing decision-making.

Key Restraints for

Information Services Market

Worries about data security and privacy: High-profile breaches and misuse of personal data undermine consumer trust in information service companies. High operational costs result from stringent cybersecurity safeguards and encryption protocols. Cross-border data transfer limitations make it harder to provide services globally. Market fragmentation and strong competition: Low entry barriers for simple data services result in oversaturation in some areas. As suppliers compete on price rather than value-added features, differentiation becomes more difficult. Reliance on third-party data sources: The dependability of services is impacted by the inconsistent data quality from outside vendors. Proprietary datasets' licensing fees lower the profit margins of information service companies

Key Trends for

Information Services Market

Specific industry-specific solutions: Targeted niche information services for sectors like healthcare (clinical trial data) or supply chain (IoT sensor analytics) are gaining popularity. A higher-value knowledge is produced by combining domain expertise with data science. Real-time data delivery: switch from static reports to dynamic dashboards and streaming analytics. Edge computing allows for quicker processing for time-sensitive applications like financial trading or fraud detection. Ethical AI and open data sourcing: Increasingly, socially conscious firms are asking for auditable algorithms and unbiased datasets. Providers are implementing fair data acquisition strategies and explainable AI frameworks Introduction of Information Services

Information systems are a collection of interconnected components that are used to capture, process, save, and disseminate various sorts of data for people to view and utilize. Businesses and consumers can choose from a variety of services offered by the information services market. These services might range from analytics tools and cloud-based storage to data management services and cybersecurity solutions. The market is being driven by an increase in the demand for these services as businesses search for fresh ways to use technology to spur development and innovation.

For instance, Amazon Web Services (AWS) offers a variety of cloud-based services, such as data storage and analysis tools. AWS provides a number of storage solutions, such as object storage, block storage, and file storage, as well as data analysis and machine learning capabilities. These services enable businesses to store and analyze massive volumes of data in the cloud, making it more accessible and usable for a wide range of applications.

(Source: docs.aws.amazon.com/whitepapers/latest/aws-overview/storage-services.html)

According to Cognitive Market Research, the global Data Exchange Platform Services Market size was USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of XX% from 2024 to 2033.

North America held largest share of XX% in the year 2024 
Europe held share of XX% in the year 2024 
Asia-Pacific held significant share of XX% in the year 2024 
South America held significant share of XX% in the year 2024
Middle East and Africa held significant share of XX% in the year 2024 

Market Dynamics of the Data Exchange Platform Service Market:

Key Drivers for the Data Exchange Platform Service Market

Businesses Are Increasingly Requiring Third-Party Data to Analyse Consumer Purchase Behavior and the Market which las led to the growth of the market 

The market is experiencing an increase in demand for third-party data, which is being met by data exchange platform services. This data ranges from traffic and financial data to climatic, geographic, and streaming sensor data. In order to enhance their statistical and machine learning models, data scientists and researchers are always searching for new sources of data. Third-party data, including as demographic, psychographic, and social media information, is needed by market researchers in a variety of domains to enhance analysis, predictions, and plans and to build 360-degree perspectives of their clientele. Furthermore, big companies are already requesting clickstream data in order to, among other things, personalize user experiences and develop engaging suggestion engines. For instance, in January 2020, IBM Corporation and Yara International worked together to create an open data sharing platform that can help with field and farm data collaboration, allowing more food to be produced globally while leaving a reduced environmental impact. It is anticipated that demand for data exchange platform services will continue to grow during the forecast period due to intensifying competition and platform service providers' rush to create premium features. In order to enable data consumers to quickly survey, purchase, upload, and query such data sets, businesses are increasingly working to simplify the process for data providers to package, distribute, sell, protect, and manage data assets. Unquestionably, an uncontested data exchange platform fosters development for all parties involved—data operators, suppliers, and customers—and is easier to market and use. Throughout the forecast period, all of these factors will be propelling the worldwide data exchange platform services market.

Restraints for the Data Exchange Platform Service Market

High initial costs for Data Exchange Platform Services may hamper the growth of the market 

Initial installation costs for demand planning solution programs might be high. They also incur additional expenditures associated with upkeep. Furthermore, organizations may be compelled to boost their expenditures for staff training on how to use the systems, in addition to spending on information technology (IT) infrastructure within the company. These challenges may impede Data Exchange Platform Services market growth throughout the projection period, particularly for small and medium-sized businesses. Without internal knowledge or technical resources, the costs for gear purchases, implementation fees, and software licensing can be prohibitive. Furthermore, continuing maintenance, such as repairs, training expenses, and IT assistance, may put further strain on already limited funds Market Overview of the Data Exchange Platform Services Market

Data Exchange Platform Services are often valuable for marketers, developers, website owners, and UI/UX professionals. It collects mouse motions such as scrolling, highlighting, typing, keypresses, heatmaps, and funnels, which assist to improve the efficiency of an application or website and obtain greater conversion rates. A replay solution delivers intangible facts for users who encounter difficult challenges when visiting a website. It helps to identify issues, eradicate them, and provide a smoother online experience. Furthermore, it aids in inspecting possible consumer behavior, better investigating customer wants, and adjusting web design layouts. A session replay tool lets the customer support staff fix difficulties in real-time using heatmap analysis, which reveals...

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCity boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal Buffers (this dataset)Counties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal BuffersWithout Coastal BuffersCity and County AbbreviationsUnincorporated Areas (Coming Soon)Census Designated PlacesCartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing excludes the coastal buffers for cities and counties that have them in the source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except OFFSHORE and AREA_SQMI to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCDTFA_CITY: CDTFA incorporated city nameCDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.CENSUS_GEOID: numeric geographic identifiers from the US Census BureauCENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.CDT_CITY_ABBR: Abbreviations of incorporated area names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 4 characters. Not present in the county-specific layers.CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or countyCENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.Boundary AccuracyCounty boundaries were originally derived from a 1:24,000 accuracy dataset, with improvements made in some places to boundary alignments based on research into historical records and boundary changes as CDTFA learns of them. City boundary data are derived from pre-GIS tax maps, digitized at BOE and CDTFA, with adjustments made directly in GIS for new annexations, detachments, and corrections. Boundary accuracy within the dataset varies. While CDTFA strives to correctly include or exclude parcels from jurisdictions for accurate tax assessment, this dataset does not guarantee that a parcel is placed in the correct jurisdiction. When a parcel is in the correct jurisdiction, this dataset cannot guarantee accurate placement of boundary lines within or between parcels or rights of way. This dataset also provides no information on parcel boundaries. For exact jurisdictional or parcel boundary locations, please consult the county assessor's office and a licensed surveyor.CDTFA's data is used as the best available source because BOE and CDTFA receive information about changes in jurisdictions which otherwise need to be collected independently by an agency or company to compile into usable map boundaries. CDTFA maintains the best available statewide boundary information.CDTFA's source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.

Purpose

County boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.

This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This layer removes the coastal buffer polygons. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers (this dataset)
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers
   • Without Coastal Buffers
4. City and County Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• CDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.
• CENSUS_GEOID: numeric geographic identifiers from the US Census Bureau
• CENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.
• GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.
• CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.
• AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or county
• CENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.

Data Management Platform Market Report is Segmented by Functionality (First-Party, Second-Party, Third-Party), Data Source (Web Analytics Tools, Mobile Web and Apps, CRM Data, POS Data, Social Networks), Deployment (Cloud, On-Premise), Enterprise Size (Large Enterprises, Smes), Industry Vertical (Retail and E-Commerce, Media and Entertainment, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.