The Data Quality Software and Solutions market is experiencing robust growth, driven by the increasing volume and complexity of data across various industries. The market's expansion is fueled by the rising need for accurate, reliable, and consistent data to support critical business decisions, improve operational efficiency, and comply with stringent data regulations. Businesses are increasingly recognizing the significant financial and reputational risks associated with poor data quality, leading to substantial investments in data quality tools and solutions. The market is segmented by deployment (cloud, on-premise), organization size (SMEs, large enterprises), and industry vertical (BFSI, healthcare, retail, manufacturing, etc.). Key trends include the growing adoption of cloud-based solutions, the integration of AI and machine learning for automated data quality checks, and the increasing focus on data governance and compliance. While the market faces some restraints like high implementation costs and the need for skilled professionals, the overall growth trajectory remains positive, indicating significant potential for expansion. We estimate the market size in 2025 to be around $15 billion, with a CAGR of approximately 12% projected through 2033. This growth is supported by the continued digital transformation across industries and the escalating demand for data-driven insights. The competitive landscape is characterized by a mix of established players like Informatica, IBM, and SAP, and smaller, specialized vendors. These companies offer a range of solutions, from data cleansing and profiling to data matching and deduplication. The market is witnessing increased consolidation through mergers and acquisitions, as companies strive to expand their product portfolios and enhance their market share. The focus on developing user-friendly interfaces and integrating data quality solutions with other enterprise applications is another key driver of market growth. Furthermore, the emergence of open-source data quality tools presents an alternative for organizations looking for more cost-effective solutions. However, the successful implementation and maintenance of data quality solutions require a strategic approach involving comprehensive data governance policies, robust data management infrastructure, and skilled personnel. This underscores the importance of ongoing investment and expertise in navigating this dynamic landscape.

The Augmented Data Quality Solution market is experiencing robust growth, driven by the increasing volume and complexity of data generated across various industries. The market's expansion is fueled by the urgent need for accurate, reliable, and consistent data to support critical business decisions, particularly in areas like AI/ML model development and data-driven business strategies. The rising adoption of cloud-based solutions and the integration of advanced technologies such as machine learning and AI into data quality management tools are further accelerating market growth. While precise figures for market size and CAGR require further specification, a reasonable estimate based on similar technology markets suggests a current market size (2025) of approximately $5 billion, with a compound annual growth rate (CAGR) hovering around 15% during the forecast period (2025-2033). This implies a significant expansion of the market to roughly $15 billion by 2033. Key market segments include applications in finance, healthcare, and retail, with various solution types, such as data profiling, cleansing, and matching tools driving the growth. Competitive pressures are also shaping the landscape with both established players and innovative startups vying for market share. However, challenges like integration complexities, high implementation costs, and the need for skilled professionals to manage these solutions can potentially restrain wider adoption. The geographical distribution of the market reveals significant growth opportunities across North America and Europe, driven by early adoption of advanced technologies and robust digital infrastructures. The Asia-Pacific region is expected to witness rapid growth in the coming years, fueled by rising digitalization and increasing investments in data-driven initiatives. Specific regional variations in growth rates will likely reflect factors such as regulatory frameworks, technological maturity, and economic development. Successful players in this space must focus on developing user-friendly and scalable solutions, fostering strategic partnerships to expand their reach, and continuously innovating to stay ahead of evolving market needs. Furthermore, addressing concerns about data privacy and security will be paramount for sustained growth.

Explore the Entity Resolution Software market, projected to reach $10.9 billion by 2033 with a 15% CAGR. Discover key drivers, trends, restraints, leading companies, and regional insights for data unification and customer identity management.

Discover the booming Entity Resolution Software market! Our in-depth analysis reveals key trends, growth drivers, and leading companies shaping this dynamic sector. Learn about market size, CAGR, and future projections through 2033. Explore regional breakdowns and competitive landscapes to gain actionable insights.

Data Clean Room Market Outlook

According to our latest research, the global data clean room market size in 2024 stood at USD 1.27 billion, reflecting the growing adoption of privacy-centric data collaboration solutions worldwide. The market is witnessing robust expansion, registering a compound annual growth rate (CAGR) of 19.6% from 2025 to 2033. By the end of 2033, the data clean room market is projected to reach a substantial valuation of USD 6.14 billion. This impressive growth is being driven by increasing regulatory pressure for data privacy, the phasing out of third-party cookies, and the urgent need for secure data collaboration in the digital advertising and analytics ecosystems.

The primary growth factor for the data clean room market is the escalating demand for privacy-compliant data sharing and analytics. As organizations face heightened scrutiny over data privacy, especially with the enforcement of regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), there is a clear shift towards solutions that enable secure, privacy-preserving data collaboration. Data clean rooms allow multiple parties to analyze shared data sets without exposing personally identifiable information (PII), thereby maintaining compliance and trust. This feature is especially vital for industries such as advertising, where brands, publishers, and platforms require granular insights without breaching privacy laws.

Another significant driver is the rapid transformation of the digital advertising landscape. With major browsers phasing out third-party cookies, advertisers and marketers are seeking alternative methods to measure campaign effectiveness and audience insights. Data clean rooms provide a secure environment for brands and publishers to match and analyze first-party data, unlocking new opportunities for targeted advertising and advanced measurement. In addition, the rise of walled gardens—large digital platforms that control vast amounts of user data—has further accelerated the adoption of data clean rooms, as these platforms offer clean room solutions to enable privacy-safe data collaboration with advertisers.

Technological advancements and the integration of artificial intelligence (AI) and machine learning (ML) into data clean rooms are also fueling market growth. Modern data clean room platforms are leveraging AI/ML to enhance data matching, automate compliance checks, and provide deeper analytics while ensuring privacy. This not only streamlines operations for enterprises but also unlocks new value from data sets that were previously inaccessible due to privacy concerns. As a result, organizations across sectors such as BFSI, healthcare, retail, and media are increasingly investing in data clean rooms to gain competitive advantage and drive innovation.

From a regional perspective, North America continues to dominate the data clean room market, accounting for the largest share in 2024 due to the presence of leading technology providers, early regulatory adoption, and a mature digital advertising ecosystem. However, Europe and the Asia Pacific regions are rapidly catching up, driven by stringent data privacy regulations and the digital transformation of key industries. Emerging markets in Latin America and the Middle East & Africa are also witnessing increased adoption, albeit at a slower pace, as enterprises in these regions begin to recognize the importance of secure data collaboration in the evolving digital economy.

Component Analysis

The data clean room market is segmented by component into software and services, each playing a distinct yet complementary role in the ecosystem. The software segment encompasses the core platforms and solutions that facilitate secure data collaboration, analytics, and privacy management. These platforms are designed to integrate seamlessly with existing enterp

Retail Media Data Clean Rooms Market Outlook

According to our latest research, the global Retail Media Data Clean Rooms market size reached USD 1.64 billion in 2024 and is expected to grow at a robust CAGR of 18.7% over the forecast period, attaining a value of USD 8.12 billion by 2033. This exceptional growth trajectory is primarily attributed to the increasing demand for privacy-compliant data collaboration and analytics solutions within the retail media ecosystem, as organizations strive to maximize the value of first-party data while adhering to stringent data privacy regulations worldwide.

One of the primary growth factors driving the Retail Media Data Clean Rooms market is the rapid proliferation of digital advertising and the corresponding surge in first-party data collection by retailers and brands. As third-party cookies face obsolescence and data privacy regulations such as GDPR and CCPA reshape the digital landscape, retailers and advertisers are increasingly turning to data clean rooms as a secure environment to collaborate, analyze, and activate data without compromising consumer privacy. The ability of clean rooms to facilitate privacy-preserving data matching, audience segmentation, and campaign measurement has become a cornerstone for unlocking the full potential of retail media networks, empowering brands to deliver highly targeted and measurable advertising while maintaining compliance with evolving data governance frameworks. This paradigm shift is fueling investments in advanced clean room technologies, fostering innovation, and driving market expansion across the globe.

Another significant driver for the Retail Media Data Clean Rooms market is the growing emphasis on personalized customer experiences and measurable marketing outcomes. Retailers and brands are under increasing pressure to demonstrate return on ad spend (ROAS) and improve customer engagement through data-driven insights. Clean rooms enable secure data collaboration between multiple parties, including retailers, brands, and agencies, allowing for granular audience insights, advanced attribution modeling, and precise personalization strategies. This collaborative approach not only enhances campaign performance but also strengthens strategic partnerships within the retail media value chain. The integration of artificial intelligence (AI) and machine learning (ML) within clean room platforms further amplifies their value proposition, enabling predictive analytics, real-time optimization, and automated decision-making, thereby accelerating market adoption among both large enterprises and small & medium businesses.

Furthermore, the Retail Media Data Clean Rooms market is benefiting from the expanding ecosystem of technology providers and the increasing availability of cloud-based deployment models. Cloud-native clean room solutions offer unparalleled scalability, flexibility, and cost-efficiency, making them particularly attractive for retailers and advertisers seeking to rapidly deploy and scale their data collaboration initiatives. The rise of retail media networks operated by major retailers, coupled with the entry of specialized technology vendors offering turnkey clean room solutions, is intensifying market competition and driving innovation. As interoperability standards and APIs mature, the integration of clean rooms with existing marketing technology stacks is becoming more seamless, further lowering barriers to adoption and broadening the addressable market.

From a regional perspective, North America currently dominates the Retail Media Data Clean Rooms market, accounting for the largest share in 2024, driven by the presence of leading retailers, advanced digital advertising infrastructure, and a favorable regulatory environment for data innovation. However, significant growth opportunities are emerging across Europe and Asia Pacific, where increasing regulatory scrutiny and the rapid digitization of retail are catalyzing investments in privacy-enhancing technologies. Europe, in particular, is witnessing accelerated adoption due to GDPR compliance requirements, while Asia Pacific is expected to register the highest CAGR during the forecast period, fueled by the expansion of e-commerce and the proliferation of retail media networks in markets such as China, India, and Southeast Asia.

Component Analysis

The Component segment of the Retail Media Data Clean Rooms market is bifurcated into softw

Description: The dataset is intentionally provided for data cleansing and applying EDA techniques. This brings fun exploring and wrangling for data geeks. The data is very original so dive-in and Happy Exploring.

Features: In total the dataset contains 121 Features. Details given below.

SK_ID_CURR ID of loan in our sample TARGET Target variable (1 - client with payment difficulties: he/she had late payment more than X days on at least one of the first Y installments of the loan in our sample, 0 - all other cases) NAME_CONTRACT_TYPE Identification if loan is cash or revolving CODE_GENDER Gender of the client FLAG_OWN_CAR Flag if the client owns a car FLAG_OWN_REALTY Flag if client owns a house or flat CNT_CHILDREN Number of children the client has AMT_INCOME_TOTAL Income of the client AMT_CREDIT Credit amount of the loan AMT_ANNUITY Loan annuity AMT_GOODS_PRICE For consumer loans it is the price of the goods for which the loan is given NAME_TYPE_SUITE Who was accompanying client when he was applying for the loan NAME_INCOME_TYPE Clients income type (businessman, working, maternity leave,…) NAME_EDUCATION_TYPE Level of highest education the client achieved NAME_FAMILY_STATUS Family status of the client NAME_HOUSING_TYPE What is the housing situation of the client (renting, living with parents, ...) REGION_POPULATION_RELATIVE Normalized population of region where client lives (higher number means the client lives in more populated region) DAYS_BIRTH Client's age in days at the time of application DAYS_EMPLOYED How many days before the application the person started current employment DAYS_REGISTRATION How many days before the application did client change his registration DAYS_ID_PUBLISH How many days before the application did client change the identity document with which he applied for the loan OWN_CAR_AGE Age of client's car FLAG_MOBIL Did client provide mobile phone (1=YES, 0=NO) FLAG_EMP_PHONE Did client provide work phone (1=YES, 0=NO) **FLAG_WORK_PHONE ** Did client provide home phone (1=YES, 0=NO) FLAG_CONT_MOBILE Was mobile phone reachable (1=YES, 0=NO) FLAG_PHONE Did client provide home phone (1=YES, 0=NO) FLAG_EMAIL Did client provide email (1=YES, 0=NO) OCCUPATION_TYPE What kind of occupation does the client have CNT_FAM_MEMBERS How many family members does client have REGION_RATING_CLIENT Our rating of the region where client lives (1,2,3) REGION_RATING_CLIENT_W_CITY Our rating of the region where client lives with taking city into account (1,2,3) WEEKDAY_APPR_PROCESS_START On which day of the week did the client apply for the loan HOUR_APPR_PROCESS_START Approximately at what hour did the client apply for the loan REG_REGION_NOT_LIVE_REGION Flag if client's permanent address does not match contact address (1=different, 0=same, at region level) REG_REGION_NOT_WORK_REGION Flag if client's permanent address does not match work address (1=different, 0=same, at region level) LIVE_REGION_NOT_WORK_REGION Flag if client's contact address does not match work address (1=different, 0=same, at region level) REG_CITY_NOT_LIVE_CITY Flag if client's permanent address does not match contact address (1=different, 0=same, at city level) REG_CITY_NOT_WORK_CITY Flag if client's permanent address does not match work address (1=different, 0=same, at city level) LIVE_CITY_NOT_WORK_CITY Flag if client's contact address does not match work address (1=different, 0=same, at city level) ORGANIZATION_TYPE Type of organization where client works EXT_SOURCE_1 Normalized score from external data source EXT_SOURCE_2 Normalized score from external data source EXT_SOURCE_3 Normalized score from external data source APARTMENTS_AVG Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor BASEMENTAREA_AVG Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor YEARS_BEGINEXPLUATATION_AVG Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor YEARS_BUILD_AVG Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MED...

Data Clean Rooms for Joint Analytics Market Outlook

According to our latest research, the global Data Clean Rooms for Joint Analytics market size reached USD 1.67 billion in 2024, reflecting the rapid adoption of privacy-centric data collaboration solutions across industries. The market is projected to grow at a robust CAGR of 22.4% from 2025 to 2033, reaching a forecasted value of USD 12.17 billion by 2033. This impressive growth is driven by the increasing demand for secure data sharing, regulatory compliance, and the need for advanced analytics in a data-driven business environment.

One of the primary growth factors for the Data Clean Rooms for Joint Analytics market is the escalating concern over data privacy and security. As regulatory frameworks such as GDPR, CCPA, and other privacy legislations become more stringent, organizations are compelled to adopt solutions that enable data collaboration without compromising individual privacy. Data clean rooms offer a controlled environment where multiple entities can analyze joint datasets while ensuring that sensitive information remains confidential and compliant with legal requirements. This capability is particularly crucial in industries like healthcare, finance, and advertising, where the use of personal data is both valuable and highly regulated.

Another significant driver is the proliferation of digital advertising and marketing initiatives that rely on third-party data. The phasing out of third-party cookies and heightened scrutiny over cross-platform data sharing have forced advertisers and publishers to seek innovative alternatives for audience insights and campaign measurement. Data clean rooms for joint analytics facilitate secure data matching and attribution across partners, enabling brands to measure performance and optimize strategies without exposing raw user data. This not only enhances marketing effectiveness but also builds consumer trust, which is increasingly vital in today’s digital ecosystem.

The market is also witnessing robust growth due to advancements in cloud computing and analytics technologies. The integration of data clean rooms with AI-powered analytics and scalable cloud infrastructure has made it easier for organizations of all sizes to leverage these solutions. Enterprises are now able to perform complex joint analytics on large datasets in real time, unlocking deeper insights while maintaining data governance. The emergence of managed service providers and specialized software platforms is further lowering the barriers to adoption, making data clean rooms accessible to a broader range of sectors including retail, media, and life sciences.

From a regional perspective, North America currently dominates the Data Clean Rooms for Joint Analytics market, accounting for the largest share in 2024. This leadership is attributed to the region’s early adoption of privacy technologies, a mature digital advertising ecosystem, and proactive regulatory compliance. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digital transformation, expanding e-commerce markets, and increasing awareness of data privacy. Europe follows closely, with strong regulatory mandates driving adoption across financial services and healthcare. Latin America and the Middle East & Africa are also showing steady growth, supported by investments in digital infrastructure and rising demand for secure data collaboration tools.

Component Analysis

The Data Clean Rooms for Joint Analytics market is segmented by component into software and services, each playing a pivotal role in shaping the industry landscape. Software solutions form the backbone of data clean rooms, providing the technological framework necessary for secure multi-party computation, data encryption, and privacy-preserving analytics. These platforms are increasingly incorporating advanced features such as automated data onboarding, customizable access controls, and built-in compliance checks to address the evolving needs of enter

Data Quality AI Market Outlook

According to our latest research, the global Data Quality AI market size reached USD 1.92 billion in 2024, driven by a robust surge in data-driven business operations across industries. The sector has demonstrated a remarkable compound annual growth rate (CAGR) of 18.6% from 2024, with projections indicating that the market will expand to USD 9.38 billion by 2033. This impressive growth trajectory is underpinned by the increasing necessity for automated data quality management solutions, as organizations recognize the strategic value of high-quality data for analytics, compliance, and digital transformation initiatives.

One of the primary growth factors for the Data Quality AI market is the exponential increase in data volume and complexity generated by modern enterprises. With the proliferation of IoT devices, cloud platforms, and digital business models, organizations are inundated with vast and diverse datasets. This data deluge, while offering immense potential, also introduces significant challenges related to data consistency, accuracy, and reliability. As a result, businesses are increasingly turning to AI-powered data quality solutions that can automate data cleansing, profiling, matching, and enrichment processes. These solutions not only enhance data integrity but also reduce manual intervention, enabling organizations to extract actionable insights more efficiently and cost-effectively.

Another significant driver fueling the growth of the Data Quality AI market is the mounting regulatory pressure and compliance requirements across various sectors, particularly in BFSI, healthcare, and government. Stringent regulations such as GDPR, HIPAA, and CCPA mandate organizations to maintain high standards of data accuracy, security, and privacy. AI-driven data quality tools are instrumental in ensuring compliance by continuously monitoring data flows, identifying anomalies, and providing real-time remediation. This proactive approach to data governance mitigates risks associated with data breaches, financial penalties, and reputational damage, thereby making AI-based data quality management a strategic investment for organizations operating in highly regulated environments.

The rapid adoption of advanced analytics, machine learning, and artificial intelligence across industries has also amplified the demand for high-quality data. As organizations increasingly leverage AI and advanced analytics for decision-making, the importance of data quality becomes paramount. Poor data quality can lead to inaccurate predictions, flawed business strategies, and suboptimal outcomes. Consequently, enterprises are prioritizing investments in AI-powered data quality solutions to ensure that their analytics initiatives are built on a foundation of reliable and consistent data. This trend is particularly pronounced among large enterprises and digitally mature organizations that view data as a critical asset for competitive differentiation and innovation.

Data Quality Tools have become indispensable in the modern business landscape, particularly as organizations grapple with the complexities of managing vast amounts of data. These tools are designed to ensure that data is accurate, consistent, and reliable, which is crucial for making informed business decisions. By leveraging advanced algorithms and machine learning, Data Quality Tools can automate the processes of data cleansing, profiling, and enrichment, thereby reducing the time and effort required for manual data management. This automation not only enhances data integrity but also empowers businesses to derive actionable insights more efficiently. As a result, companies are increasingly investing in these tools to maintain a competitive edge in their respective industries.

From a regional perspective, North America continues to dominate the Data Quality AI market, accounting for the largest share in 2024. The region's leadership is attributed to the presence of major technology vendors, early adoption of AI-driven solutions, and a robust ecosystem of data-centric enterprises. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digital transformation, increasing investments in cloud infrastructure, and a burgeoning startup ecosystem. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, driven by regulatory mandat

Data Clean Room Interoperability Platforms Market Outlook

As per our latest research, the global Data Clean Room Interoperability Platforms market size reached USD 1.42 billion in 2024, driven by the escalating demand for secure data collaboration and privacy-centric data analytics across industries. The market is poised for robust expansion, exhibiting a CAGR of 22.8% from 2025 to 2033, with the total market value projected to reach USD 10.03 billion by 2033. This remarkable growth trajectory is primarily fueled by the rising adoption of privacy regulations, the need for advanced data sharing frameworks, and the proliferation of data-driven marketing and analytics initiatives globally.

One of the main growth factors for the Data Clean Room Interoperability Platforms market is the intensification of data privacy regulations such as GDPR in Europe, CCPA in California, and other similar frameworks worldwide. Organizations are increasingly compelled to ensure compliance with these stringent data protection laws. Data clean rooms provide a secure environment where multiple parties can collaborate on sensitive data without exposing raw information, thus facilitating compliance and minimizing risk. As businesses become more data-driven, the ability to analyze, share, and leverage data while maintaining privacy and regulatory adherence is becoming a critical differentiator, propelling the adoption of interoperable data clean room solutions.

Another significant driver is the exponential growth in digital advertising, marketing analytics, and cross-industry data partnerships. Marketers, publishers, and brands are seeking innovative ways to maximize the value of first-party and second-party data in a privacy-first world. Data clean room interoperability platforms enable seamless data matching, audience segmentation, and campaign measurement across disparate data sources, all while maintaining user anonymity and data security. This capability is increasingly vital as third-party cookies are phased out, and organizations look for new ways to achieve actionable insights and personalized experiences without compromising user privacy.

Technological advancements in cloud computing, artificial intelligence, and secure multi-party computation are also catalyzing market growth. Modern data clean room platforms leverage these technologies to offer scalable, flexible, and highly secure environments for data collaboration. Interoperability is becoming a key requirement, as enterprises seek to integrate clean room solutions with existing data lakes, analytics tools, and business intelligence platforms. Vendors are responding by developing solutions that support a wide range of data formats, APIs, and privacy-enhancing technologies, making data clean room interoperability platforms more accessible and effective for organizations of all sizes.

From a regional perspective, North America currently dominates the Data Clean Room Interoperability Platforms market, accounting for over 48% of global revenue in 2024, thanks to its mature digital ecosystem and early adoption of privacy-centric data solutions. Europe follows closely, driven by strict regulatory environments and the widespread adoption of GDPR. The Asia Pacific region is expected to witness the fastest growth during the forecast period, with a CAGR of 25.6%, fueled by rapid digitalization, increasing data volumes, and growing awareness of data privacy issues. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a relatively nascent stage, as organizations in these regions ramp up investments in secure data collaboration tools.

Component Analysis

The Data Clean Room Interoperability Platforms market by component is segmented into software and services. The software segment holds a dominant share, accounting for approximately 68% of the market in 2024. This is attributed to the increasing demand for advanced software solutions that facilitate secure data collaboration, privacy-preserving analytics, and seamless integration with existing enterprise systems. Leading software offerings include robust data matching, identity resolution, and secure computation capabilities, all designed to enable organizations to extract actionable insights from shared datasets without exposing sensitive information. The rise of cloud-native architectures and API-driven platforms has further accelerated the adoption o

CAT Data Quality Tools Market Outlook

According to our latest research, the global CAT Data Quality Tools market size is valued at USD 2.85 billion in 2024, reflecting a robust industry that is increasingly critical to data-driven enterprises worldwide. The market is expected to grow at a compelling CAGR of 16.2% from 2025 to 2033, reaching an estimated USD 10.48 billion by 2033. This impressive growth trajectory is primarily fueled by the escalating volume of enterprise data, the urgent need for regulatory compliance, and the critical importance of data-driven decision-making in modern organizations. As per our latest research, the CAT Data Quality Tools market is poised for transformative expansion, underpinned by technological advancements and the growing recognition of data as a strategic asset.

A significant growth factor for the CAT Data Quality Tools market is the rapid digitization across industries, which has led to an exponential increase in data generation. Enterprises are increasingly reliant on accurate, consistent, and reliable data to drive their business intelligence, analytics, and operational processes. The rising adoption of cloud computing, artificial intelligence, and machine learning is further amplifying the need for sophisticated data quality tools. Companies are investing heavily in such solutions to ensure that their data assets are not only secure but also actionable. Moreover, the proliferation of IoT devices and the integration of disparate data sources are making data quality management more complex, thereby driving demand for advanced CAT Data Quality Tools that can automate and streamline data cleansing, profiling, matching, and monitoring processes.

Another key driver is the tightening regulatory landscape across regions such as North America and Europe. Stringent regulations like GDPR, CCPA, and HIPAA mandate organizations to maintain high standards of data integrity and privacy. Non-compliance can result in hefty fines and reputational damage, prompting enterprises to adopt comprehensive data quality management frameworks. Furthermore, the growing focus on customer experience and personalization in sectors like BFSI, healthcare, and retail necessitates the use of high-quality, accurate data. This has led to a surge in demand for CAT Data Quality Tools that not only ensure compliance but also enhance operational efficiency and customer satisfaction by eliminating data redundancies and inaccuracies.

The emergence of big data analytics and real-time decision-making has made data quality management a boardroom priority. Organizations are recognizing that poor data quality can lead to flawed analytics, misguided strategies, and financial losses. As a result, there is a marked shift towards proactive data quality management, with enterprises seeking tools that offer real-time monitoring, automated cleansing, and robust profiling capabilities. The integration of AI and machine learning into CAT Data Quality Tools is enabling predictive analytics and anomaly detection, further elevating the value proposition of these solutions. As businesses continue to digitalize their operations and embrace data-centric models, the demand for scalable, flexible, and intelligent data quality tools is expected to surge.

Regionally, North America dominates the CAT Data Quality Tools market, owing to its advanced technological infrastructure, high digital adoption rates, and stringent regulatory environment. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid industrialization, digital transformation initiatives, and increasing investments in IT infrastructure. Europe also holds a significant market share, supported by strong regulatory frameworks and a mature enterprise sector. Latin America and the Middle East & Africa are witnessing steady growth, fueled by expanding digital economies and the growing recognition of data as a key business asset. The regional outlook for the CAT Data Quality Tools market remains highly optimistic, with all major regions contributing to the market’s sustained expansion.

Operator Data Clean Room Market Outlook

According to our latest research, the global operator data clean room market size in 2024 stands at USD 1.47 billion, reflecting the rapid adoption of privacy-centric data collaboration solutions across industries. The market is set to experience a robust compound annual growth rate (CAGR) of 22.8% from 2025 to 2033, projecting a substantial growth to USD 11.7 billion by 2033. This significant expansion is primarily driven by the increasing regulatory focus on data privacy, the proliferation of digital advertising, and the critical need for secure data sharing and analytics without compromising user confidentiality.

The primary growth driver for the operator data clean room market is the escalating demand for privacy-compliant data collaboration tools, especially in sectors like advertising, banking, and healthcare. As organizations face mounting pressure from stringent data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), they are compelled to adopt advanced solutions that enable secure multi-party data analysis. Operator data clean rooms allow companies to aggregate, analyze, and activate data from different sources without exposing sensitive user information, making them indispensable in today’s privacy-first digital ecosystem. Furthermore, the surge in third-party cookie deprecation and the need for alternative targeting and measurement solutions in digital marketing are accelerating the adoption of clean room technologies.

Another significant factor fueling market growth is the exponential increase in data volumes generated by enterprises, coupled with the strategic importance of leveraging first-party and second-party data for competitive advantage. As data-driven decision-making becomes a cornerstone of business strategy, organizations are seeking advanced platforms that can facilitate secure data integration and insight generation across organizational boundaries. Operator data clean rooms offer a scalable, secure, and privacy-preserving environment for such collaboration, enabling businesses to unlock valuable insights while maintaining compliance with evolving data protection standards. The integration of artificial intelligence and machine learning within clean rooms further enhances their analytical capabilities, driving broader adoption across industries.

In addition to regulatory and technological drivers, the operator data clean room market is benefiting from the growing emphasis on customer-centric marketing and personalized experiences. Brands are increasingly looking to partner with media owners, publishers, and other data providers to enrich their understanding of customer journeys and optimize campaign performance. Clean room environments facilitate these partnerships by enabling granular, privacy-safe data analysis and activation. As businesses recognize the strategic value of data collaboration, investments in operator data clean room solutions are expected to surge, further propelling market growth over the forecast period.

From a regional perspective, North America currently dominates the operator data clean room market, owing to its advanced digital infrastructure, early adoption of privacy technologies, and the presence of leading market players. However, Asia Pacific is anticipated to exhibit the fastest growth rate over the forecast period, driven by rapid digital transformation, increasing regulatory scrutiny, and the proliferation of e-commerce and digital media platforms. Europe remains a critical market as well, given its rigorous data protection framework and the growing focus on cross-border data collaboration. As organizations worldwide prioritize data privacy and secure analytics, the operator data clean room market is poised for robust global expansion.

Component Analysis

The operator data clean room market by component is segmented into software and services, each playing a pivotal role in the ecosystem. The software segment accounts for the largest share, driven by the increasing demand for robust, scalable, and interoperable platforms that facilitate secure data collaboration. Operator data clean room software solutions are designed to provide privacy-preserving data matching, advanced analytics, and seamless integration with existing data infrastructure. Vendors are continuously enhancing their offerings with features such as differential privacy, secur

Dataset Description

This dataset is a collection of customer, product, sales, and location data extracted from a CRM and ERP system for a retail company. It has been cleaned and transformed through various ETL (Extract, Transform, Load) processes to ensure data consistency, accuracy, and completeness. Below is a breakdown of the dataset components: 1. Customer Information (s_crm_cust_info)

This table contains information about customers, including their unique identifiers and demographic details.

Columns:

  cst_id: Customer ID (Primary Key)

  cst_gndr: Gender

  cst_marital_status: Marital status

  cst_create_date: Customer account creation date

Cleaning Steps:

  Removed duplicates and handled missing or null cst_id values.

  Trimmed leading and trailing spaces in cst_gndr and cst_marital_status.

  Standardized gender values and identified inconsistencies in marital status.

1. Product Information (s_crm_prd_info / b_crm_prd_info)

This table contains information about products, including product identifiers, names, costs, and lifecycle dates.

Columns:

  prd_id: Product ID

  prd_key: Product key

  prd_nm: Product name

  prd_cost: Product cost

  prd_start_dt: Product start date

  prd_end_dt: Product end date

Cleaning Steps:

  Checked for duplicates and null values in the prd_key column.

  Validated product dates to ensure prd_start_dt is earlier than prd_end_dt.

  Corrected product costs to remove invalid entries (e.g., negative values).

1. Sales Details (s_crm_sales_details / b_crm_sales_details)

This table contains information about sales transactions, including order dates, quantities, prices, and sales amounts.

Columns:

  sls_order_dt: Sales order date

  sls_due_dt: Sales due date

  sls_sales: Total sales amount

  sls_quantity: Number of products sold

  sls_price: Product unit price

Cleaning Steps:

  Validated sales order dates and corrected invalid entries.

  Checked for discrepancies where sls_sales did not match sls_price * sls_quantity and corrected them.

  Removed null and negative values from sls_sales, sls_quantity, and sls_price.

1. ERP Customer Data (b_erp_cust_az12, s_erp_cust_az12)

This table contains additional customer demographic data, including gender and birthdate.

Columns:

  cid: Customer ID

  gen: Gender

  bdate: Birthdate

Cleaning Steps:

  Checked for missing or null gender values and standardized inconsistent entries.

  Removed leading/trailing spaces from gen and bdate.

  Validated birthdates to ensure they were within a realistic range.

1. Location Information (b_erp_loc_a101)

This table contains country information related to the customers' locations.

Columns:

  cntry: Country

Cleaning Steps:

  Standardized country names (e.g., "US" and "USA" were mapped to "United States").

  Removed special characters (e.g., carriage returns) and trimmed whitespace.

1. Product Category (b_erp_px_cat_g1v2)

This table contains product category information.

Columns:

  Product category data (no significant cleaning required).

Key Features:

Customer demographics, including gender and marital status

Product details such as cost, start date, and end date

Sales data with order dates, quantities, and sales amounts

ERP-specific customer and location data

Data Cleaning Process:

This dataset underwent extensive cleaning and validation, including:

Null and Duplicate Removal: Ensuring no duplicate or missing critical data (e.g., customer IDs, product keys).

Date Validations: Ensuring correct date ranges and chronological consistency.

Data Standardization: Standardizing categorical fields (e.g., gender, country names) and fixing inconsistent values.

Sales Integrity Checks: Ensuring sales amounts match the expected product of price and quantity.

This dataset is now ready for analysis and modeling, with clean, consistent, and validated data for retail analytics, customer segmentation, product analysis, and sales forecasting.

Data Clean Room Platform Market Outlook

According to our latest research, the global Data Clean Room Platform market size reached USD 1.38 billion in 2024, reflecting a robust expansion driven by the increasing demand for privacy-centric data collaboration. The market is projected to grow at a CAGR of 22.3% from 2025 to 2033, reaching an estimated USD 9.94 billion by 2033. This growth is primarily fueled by the rising adoption of privacy regulations, the proliferation of digital advertising, and the heightened need for secure data sharing across industries. As businesses seek to unlock the value of data while ensuring compliance and privacy, Data Clean Room Platforms are becoming essential tools in the evolving digital ecosystem.

One of the key growth factors propelling the Data Clean Room Platform market is the tightening regulatory landscape around data privacy and protection. With landmark regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, organizations are under increasing pressure to manage and process data responsibly. Data Clean Room Platforms provide a secure and compliant environment where multiple parties can collaborate on sensitive datasets without exposing raw data. This capability is particularly crucial for industries such as advertising, healthcare, and financial services, where the handling of personally identifiable information (PII) is heavily regulated. The ability to derive insights from shared data while maintaining compliance is a significant driver for the adoption of these platforms.

Another significant driver is the rapid digitalization of industries and the growing importance of data-driven decision-making. As organizations across sectors collect and generate vast volumes of data, the need to collaborate with partners, advertisers, and third-party vendors becomes more pronounced. However, traditional data-sharing methods often expose organizations to security risks and compliance breaches. Data Clean Room Platforms address these concerns by enabling secure, privacy-preserving analytics and audience segmentation. This is particularly valuable in the advertising and marketing domain, where brands and publishers need to work together without compromising consumer privacy. The shift toward first-party data strategies, especially in the wake of third-party cookie deprecation, further amplifies the demand for robust Data Clean Room solutions.

Technological advancements and the integration of artificial intelligence (AI) and machine learning (ML) are also accelerating market growth. Modern Data Clean Room Platforms are increasingly leveraging AI/ML to enhance data matching, anonymization, and analytics capabilities. These innovations enable organizations to extract deeper insights from combined datasets while minimizing the risk of re-identification. Additionally, the proliferation of cloud computing has made these platforms more accessible and scalable, allowing organizations of all sizes to benefit from secure data collaboration. The convergence of privacy technology, cloud infrastructure, and advanced analytics is creating new opportunities for value creation and competitive differentiation in the Data Clean Room Platform market.

From a regional perspective, North America currently dominates the Data Clean Room Platform market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The early adoption of privacy regulations, a mature digital advertising ecosystem, and a high concentration of technology providers have positioned North America at the forefront of this market. Europe’s stringent data protection laws and growing emphasis on ethical data usage are driving rapid adoption across the region. Meanwhile, Asia Pacific is emerging as a high-growth market due to increasing digital transformation initiatives and rising awareness of data privacy. Latin America and the Middle East & Africa, while still nascent, are expected to witness accelerated growth as regulatory frameworks evolve and digital economies expand.

We describe a bibliometric network characterizing co-authorship collaborations in the entire Italian academic community. The network, consisting of 38,220 nodes and 507,050 edges, is built upon two distinct data sources: faculty information provided by the Italian Ministry of University and Research and publications available in Semantic Scholar. Both nodes and edges are associated with a large variety of semantic data, including gender, bibliometric indexes, authors' and publications' research fields, and temporal information. While linking data between the two original sources posed many challenges, the network has been carefully validated to assess its reliability and to understand its graph-theoretic characteristics. By resembling several features of social networks, our dataset can be profitably leveraged in experimental studies in the wide social network analytics domain as well as in more specific bibliometric contexts. , The proposed network is built starting from two distinct data sources:

the entire dataset dump from Semantic Scholar (with particular emphasis on the authors and papers datasets) the entire list of Italian faculty members as maintained by Cineca (under appointment by the Italian Ministry of University and Research).

By means of a custom name-identity recognition algorithm (details are available in the accompanying paper published in Scientific Data), the names of the authors in the Semantic Scholar dataset have been mapped against the names contained in the Cineca dataset and authors with no match (e.g., because of not being part of an Italian university) have been discarded. The remaining authors will compose the nodes of the network, which have been enriched with node-related (i.e., author-related) attributes. In order to build the network edges, we leveraged the papers dataset from Semantic Scholar: specifically, any two authors are said to be connected if there is at least one pap..., , # Data cleaning and enrichment through data integration: networking the Italian academia

https://doi.org/10.5061/dryad.wpzgmsbwj

Manuscript published in Scientific Data with DOI .

Description of the data and file structure

This repository contains two main data files:

• edge_data_AGG.csv, the full network in comma-separated edge list format (this file contains mainly temporal co-authorship information);
• Coauthorship_Network_AGG.graphml, the full network in GraphML format.Â

along with several supplementary data, listed below, useful only to build the network (i.e., for reproducibility only):

• University-City-match.xlsx, an Excel file that maps the name of a university against the city where its respective headquarter is located;
• Areas-SS-CINECA-match.xlsx, an Excel file that maps the research areas in Cineca against the research areas in Semantic Scholar.

Description of the main data files

The `Coauthorship_Networ...

Data Quality as a Service Market Outlook

According to our latest research, the Data Quality as a Service (DQaaS) market size reached USD 2.4 billion globally in 2024. The market is experiencing robust expansion, with a recorded compound annual growth rate (CAGR) of 17.8% from 2025 to 2033. By the end of 2033, the DQaaS market is forecasted to attain a value of USD 8.2 billion. This remarkable growth trajectory is primarily driven by the escalating need for real-time data accuracy, regulatory compliance, and the proliferation of cloud-based data management solutions across industries.

The growth of the Data Quality as a Service market is fundamentally propelled by the increasing adoption of cloud computing and digital transformation initiatives across enterprises of all sizes. Organizations are generating and consuming vast volumes of data, making it imperative to ensure data integrity, consistency, and reliability. The surge in big data analytics, artificial intelligence, and machine learning applications further amplifies the necessity for high-quality data. As businesses strive to make data-driven decisions, the demand for DQaaS solutions that can seamlessly integrate with existing IT infrastructure and provide scalable, on-demand data quality management is surging. The convenience of subscription-based models and the ability to access advanced data quality tools without significant upfront investment are also catalyzing market growth.

Another significant driver for the DQaaS market is the stringent regulatory landscape governing data privacy and security, particularly in sectors such as banking, financial services, insurance (BFSI), healthcare, and government. Regulations like the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and other regional data protection laws necessitate that organizations maintain accurate and compliant data records. DQaaS providers offer specialized services that help enterprises automate compliance processes, minimize data errors, and mitigate the risks associated with poor data quality. As regulatory scrutiny intensifies globally, organizations are increasingly leveraging DQaaS to ensure continuous compliance and avoid hefty penalties.

Technological advancements and the integration of artificial intelligence and machine learning into DQaaS platforms are revolutionizing how data quality is managed. Modern DQaaS solutions now offer sophisticated features such as real-time data profiling, automated anomaly detection, predictive data cleansing, and intelligent data matching. These innovations enable organizations to proactively monitor and enhance data quality, leading to improved operational efficiency and competitive advantage. Moreover, the rise of multi-cloud and hybrid IT environments is fostering the adoption of DQaaS, as these solutions provide unified data quality management across diverse data sources and platforms. The continuous evolution of DQaaS technologies is expected to further accelerate market growth over the forecast period.

From a regional perspective, North America continues to dominate the Data Quality as a Service market, accounting for the largest revenue share in 2024. This leadership is attributed to the early adoption of cloud technologies, a robust digital infrastructure, and the presence of key market players in the United States and Canada. Europe follows closely, driven by stringent data protection regulations and a strong focus on data governance. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, increasing cloud adoption among enterprises, and expanding e-commerce and financial sectors. As organizations across the globe recognize the strategic importance of high-quality data, the demand for DQaaS is expected to surge in both developed and emerging markets.

Component Analysis

The Component segment of the Data Quality as a Service market is bifurcated into software and services, each playing a pivotal role in the overall ecosystem. The software component comprises platforms and tools that offer functionalities such as data cleansing, profiling, matching, and monitoring. These solutions are designed to automate and streamline data quality processes, ensuring that data remains accurate, consistent, and reliable across the enterprise. The services component, on the other hand, includes consulting, imp

Data Clean Room for Advertising Market Outlook

According to our latest research, the global Data Clean Room for Advertising market size reached USD 1.62 billion in 2024, and is projected to grow at a robust CAGR of 22.7% from 2025 to 2033, reaching a forecasted market size of USD 12.16 billion by 2033. This impressive growth is primarily fueled by the increasing demand for privacy-centric data collaboration solutions in digital advertising, driven by regulatory changes and the phasing out of third-party cookies. The market’s expansion is further supported by technological advancements and the growing need for advertisers and publishers to securely share and analyze audience data while maintaining compliance with global data privacy standards.

One of the primary growth factors for the Data Clean Room for Advertising market is the escalating emphasis on data privacy and regulatory compliance. With the enforcement of stringent data protection laws such as GDPR in Europe, CCPA in California, and similar frameworks globally, advertisers and publishers are under mounting pressure to ensure that consumer data is handled responsibly. Data clean rooms provide a secure, neutral environment for multiple parties to analyze and collaborate on user data without exposing personally identifiable information (PII). This capability is crucial in a digital advertising landscape where privacy breaches can lead to severe reputational and financial repercussions. As a result, organizations are increasingly adopting data clean room solutions to balance effective audience targeting with robust privacy protection, driving the market’s sustained growth.

Another significant driver is the ongoing deprecation of third-party cookies by major web browsers and platforms. As third-party cookies become obsolete, advertisers and marketers are seeking alternative methods to understand user behavior and measure campaign effectiveness without direct access to granular user data. Data clean rooms are emerging as a vital tool in this context, enabling secure data matching and audience overlap analysis between brands, agencies, and publishers. By leveraging clean room technology, stakeholders can derive actionable insights from aggregated and anonymized data sets, supporting advanced use cases such as audience measurement, attribution analysis, and campaign planning. This shift is prompting substantial investments in clean room platforms and services, further accelerating market growth.

The proliferation of data-driven advertising strategies and the rise of omnichannel marketing are also contributing to the expansion of the Data Clean Room for Advertising market. As brands strive to deliver personalized experiences across multiple touchpoints, the ability to integrate and analyze disparate data sources becomes increasingly important. Data clean rooms facilitate secure data collaboration between advertisers, publishers, retailers, and other ecosystem participants, enabling holistic audience insights and improved campaign performance. Moreover, advancements in cloud computing, artificial intelligence, and machine learning are enhancing the capabilities of data clean rooms, enabling more sophisticated analytics and automation. These technological trends are expected to continue shaping the market landscape, driving innovation and adoption across industries.

From a regional perspective, North America currently leads the Data Clean Room for Advertising market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s dominance is attributed to the presence of major technology providers, a mature digital advertising ecosystem, and early adoption of privacy-enhancing technologies. Europe is also witnessing significant growth, driven by strict regulatory requirements and increasing investments in data privacy infrastructure. Meanwhile, Asia Pacific is emerging as a high-growth market, fueled by rapid digitalization, expanding internet penetration, and rising awareness of data privacy among enterprises. Latin America and the Middle East & Africa are expected to experience steady growth, supported by evolving regulatory landscapes and increasing adoption of digital advertising solutions.

This comprehensive dataset delivers 387M+ U.S. phone numbers enriched with deep telecom intelligence and granular geographic metadata, providing one of the most complete national phone data assets available today. Designed for data enrichment, verification, identity resolution, analytics, risk modeling, telecom research, and large-scale customer intelligence, this file combines broad coverage with highly structured attributes and reliable carrier-grade metadata. It is a powerful resource for any organization that needs accurate, up-to-date U.S. phone number data supported by robust telecom identifiers.

Our dataset includes mobile, landline, and VOIP numbers, paired with detailed fields such as carrier, line type, city, state, ZIP code, county, latitude/longitude, time zone, rate center, LATA, and OCN. These attributes make the file suitable for a wide range of applications, from consumer analytics and segmentation to identity graph construction and marketing audience modeling. Updated regularly and validated for completeness, this dataset offers high-confidence coverage across all 50 states, major metros, rural areas, and underserved regions.

Field Coverage & Schema Overview

The dataset contains a rich set of fields commonly required for telecom analysis, identity resolution, and large-scale data cleansing:

Phone Number – Standardized 10-digit U.S. number

Line Type – Wireless, Landline, VOIP, fixed-wireless, etc.

Carrier / Provider – Underlying or current carrier assignment

City & State – Parsed from rate center and location metadata

ZIP Code – Primary ZIP associated with the phone block

County – County name mapped to geographic area

Latitude / Longitude – Approximate geo centroid for the assigned location

Time Zone – Automatically mapped; useful for outbound compliance

Rate Center – Telco rate center tied to number blocks

LATA – Local Access and Transport Area for telecom routing

OCN (Operating Company Number) – Carrier identifier for precision analytics

Additional metadata such as region codes, telecom identifiers, and national routing attributes depending on the number block

These data points provide a complete snapshot of the phone number’s telecom context and geographic footprint.

Key Features

387M+ fully structured U.S. phone numbers

Mobile, landline, and VOIP line types

Accurate carrier and OCN information

Geo-enriched records with city, state, ZIP, county, lat/long

Telecom routing metadata including rate center and LATA

Ideal for large-scale analytics, enrichment, and modeling

Nationwide coverage with consistent formatting and schema

Primary Use Cases 1. Data Enrichment & Appending

Enhance customer databases by adding carrier information, line type, geographic attributes, and telecom routing fields to improve downstream analytics and segmentation.

1. Identity Resolution & Profile Matching

Use carrier, OCN, and geographic fields to strengthen your identity graph, resolve duplicate entities, confirm telephone types, or enrich cross-channel identifiers.

1. Lead Scoring & Consumer Modeling

Build predictive models based on:

Line type (mobile vs landline)

Geography (state, county, ZIP)

Telecom infrastructure and regional carrier assignments Useful for ML/AI scoring, propensity models, risk analysis, and customer lifetime value studies.

1. Compliance-Aware Outreach Planning

Fields like time zone, rate center, and line type support compliant outbound operations, call scheduling, and segmentation of mobile vs landline users for regulated environments.

1. Data Quality, Cleansing & Validation

Normalize customer files, detect outdated or mismatched phone metadata, resolve carrier inconsistencies, and remove non-U.S. or structurally invalid numbers.

1. Telecom Market Analysis

Researchers and telecom analysts can use the dataset to understand national carrier distribution, regional line-type patterns, infrastructure growth, and switching behavior.

1. Fraud Detection & Risk Intelligence

Carrier metadata, OCN patterns, and geographic context support:

Synthetic identity detection

Fraud scoring models

Device/number reputation systems

VOIP risk modeling

1. Location-Based Analytics & Mapping

Lat/long and geographic context fields allow integration into GIS systems, heat-mapping, regional modeling, and ZIP- or county-level segmentation.

1. Customer Acquisition & Audience Building

Build highly targeted audiences for:

Marketing analytics

Look-alike modeling

Cross-channel segmentation

Regional consumer insights

1. Enterprise-Scale ETL & Data Infrastructure

The structured, normalized schema makes this file easy to integrate into:

Data lakes

Snowflake / BigQuery warehouses

ID graphs

Customer 360 platforms

Telecom research systems

Ideal Users

Marketing analytics teams

Data science groups

Identity resolution providers

Fraud & risk intelligence platforms

Telecom analysts

Consumer data platforms

Credit, insurance, and fintech modeling teams

Data brokers & a...

The “Company ID Linktables” dataset comprises ID linkage tables for the datasets BVD, MiDi, URS, USTAN,JANIS, BAKIS-M, LEI, RIAD (AnaCredit), SIFCT, SITS, and Schufa. The individual ID linkage tables are two-column tables containing a different company identifier in each column, resulting in a table of ID value pairs, showing which ID values of the two IDs refer to the same real-world company entity. The need for these ID linkage tables originates from the fact that company data is often held in separate databases, which use different company identifiers. The tables are produced by the RDSC through the use of current record linkage techniques, which,among other methods, include comprehensive data cleaning, matching based on common externalIDs, as well as name and place based matching, which includes probabilisic matching using supervisedmachine learning. The technical properties of this record linkage process are described by Gábor-Tóth, E., Schild, C., and Walter, S. (2023). Linking Deutsche Bundesbank Company Data. Technical Report 2023-05. Deutsche Bundesbank, Research Data and Service Centre. The size of the generated data overlaps / intersections is analyzed by Gábor-Tóth, E., Schild, C., and Walter, S. (2023). Understanding Overlaps betweenDifferent Company Data. Technical Report 2023-06. Deutsche Bundesbank, Research Data andService Centre.