Financial Data Services Market size was valued at USD 23.3 Billion in 2023 and is projected to reach USD 42.6 Billion by 2031, growing at a CAGR of 8.1% during the forecast period 2024-2031.Global Financial Data Services Market DriversThe market drivers for the Financial Data Services Market can be influenced by various factors. These may include:The need for real-time analytics is growing: Real-time analytics are becoming more and more necessary in the financial sector due to the acceleration of data consumption. To reduce risks, make wise decisions, and enhance customer service, organizations need quick insights. Stakeholders are giving priority to solutions that enable quick data processing and analysis due to the increase in market volatility and complexity. The need for sophisticated analytical skills is driving providers of financial data services to modernize their products. As companies come to realize that using real-time data is crucial for keeping a competitive edge in a fast-paced financial climate, the competition among them to provide timely insights also boosts market growth.Growing Machine Learning and AI Adoption: Data analysis has been profoundly changed by the incorporation of AI and machine learning technology into financial data services. By enabling predictive analytics, these technologies help financial organizations make better decisions and reduce risk. Businesses can find trends that were previously invisible by automating data processing operations. This leads to more precise forecasts and improved investment plans. Furthermore, sophisticated algorithms are flexible enough to adjust to shifting circumstances, keeping organizations flexible. The increasing intricacy of financial markets necessitates the use of AI and machine learning, which in turn drives demand for sophisticated financial data services and promotes innovation in the sector.Global Financial Data Services Market RestraintsSeveral factors can act as restraints or challenges for the Financial Data Services Market. These may include:Difficulties in Regulatory Compliance: Regulations controlling data management, privacy, and financial transactions place heavy restrictions on the financial data services market. Regulations like the GDPR, CCPA, and banking industry standards like Basel III and SOX must all be complied with by organizations. Complying with these requirements frequently necessitates a significant investment in staff and compliance systems, which can be taxing, especially for smaller businesses. Regulations are dynamic, and different locations have different needs, which adds to the complexity and expense. Noncompliance not only results in monetary fines but also has the potential to harm an entity's image, so impeding market expansion.Dangers to Data Security: Threats to data security are a major impediment to the financial data services market. Because they manage sensitive data, financial institutions are often the targets of cyberattacks. Breach can lead to significant monetary losses, legal repercussions, and long-term harm to one's image. Although they can greatly increase operating expenses, investments in strong security measures like encryption, safe access protocols, and continual monitoring are crucial. Moreover, the dynamic strategies employed by cybercriminals need continuous adjustment, placing a burden on resources and detracting from the main operations of businesses. The evolution of security threats poses a challenge to preserving consumer trust, hence impeding industry expansion.

Market Size statistics on the Financial Data Service Providers industry in the US

Data Subscription Service Market Outlook

The global data subscription service market size was valued at approximately USD 45 billion in 2023 and is expected to reach about USD 120 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% during the forecast period. This impressive growth is driven by the increasing reliance on data-driven decision-making across various industries. Businesses and individuals are increasingly subscribing to data services to gain insights, optimize operations, and drive innovation, which in turn fuels market expansion.

Several factors contribute to the robust growth of the data subscription service market. First, the exponential increase in data generation and the need for real-time analytics are primary drivers. In today’s digital age, vast amounts of data are generated every second through various channels such as social media, IoT devices, and e-commerce platforms. Organizations require sophisticated data services to analyze and interpret this data, drawing actionable insights that can enhance their business strategies, optimize operations, and improve customer experiences. Therefore, the demand for data subscription services is soaring, leading to significant market expansion.

Second, the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies is a pivotal growth factor. Data subscription services are integral to the functioning of AI and ML systems as they provide the necessary data inputs for training and refining algorithms. As these technologies become more prevalent across industries such as healthcare, finance, and retail, the reliance on high-quality data services increases. Companies are investing more in data subscription services to harness the full potential of AI and ML, thereby driving market growth.

Third, the rise of remote work and digital transformation initiatives has further augmented the demand for data subscription services. With the shift towards remote and hybrid work models, organizations are increasingly leveraging cloud-based data services to ensure seamless access to vital information regardless of location. Additionally, digital transformation efforts are pushing companies to modernize their data infrastructure, thereby increasing the uptake of subscription-based data services. These trends are expected to continue, contributing significantly to the growth of the market.

Regionally, North America holds the lion’s share of the market, driven by the early adoption of advanced technologies and a strong presence of key industry players. The region's technological infrastructure and focus on innovation make it a fertile ground for the proliferation of data subscription services. However, the Asia Pacific region is projected to witness the highest growth rate, fueled by rapid digitalization, increasing internet penetration, and growing investments in AI and ML technologies. European markets are also notable, with a strong emphasis on regulatory compliance and data privacy driving the adoption of sophisticated data management solutions.

Type Analysis

The data subscription service market can be segmented by type into individual and corporate subscriptions. Individual subscriptions are generally tailored for personal use, providing users with access to specific datasets, market reports, or analytics tools that assist in personal projects, research, or small business operations. As digital literacy increases and more consumers become data-savvy, the demand for individual data subscription services is on the rise. These services are often more affordable and offer flexible payment options, making them accessible to a broader audience.

On the other hand, corporate subscriptions command a significant share of the market due to their comprehensive service offerings and value propositions tailored for businesses. Corporate subscriptions often include access to a vast array of datasets, advanced analytics tools, and dedicated support services. These subscriptions are critical for enterprises looking to enhance their data-driven decision-making processes, optimize operations, and gain a competitive edge. The complexity and volume of data required by corporations necessitate robust data subscription services, driving significant market demand in this segment.

A notable trend in the corporate segment is the increasing preference for customized data solutions. Businesses are seeking subscription services that can be tailored to their unique needs and industry-specific requirements. This customization trend is prompting servi

This graph presents the results of a survey, conducted by BARC in 2014/15, into the current and planned use of technology for the analysis of big data. At the beginning of 2015, ** percent of respondents indicated that their company was already using a big data analytical appliance for big data.

Records from operating a customer call center or service center providing services to the public. Services may address a wide variety of topics such as understanding agency mission-specific functions or how to resolve technical difficulties with external-facing systems or programs. Includes:rn- incoming requests and responsesrn- trouble tickets and tracking logs rn- recordings of call center phone conversations with customers used for quality control and customer service trainingrn- system data, including customer ticket numbers and visit tracking rn- evaluations and feedback about customer servicesrn- information about customer services, such as “Frequently Asked Questions” (FAQs) and user guidesrn- reports generated from customer management datarn- complaints and commendation records; customer feedback and satisfaction surveys, including survey instruments, data, background materials, and reports.

The Annual Survey of Hours and Earnings (ASHE) is one of the largest surveys of the earnings of individuals in the UK. Data on the wages, paid hours of work, and pensions arrangements of nearly one per cent of the working population are collected. Other variables relating to age, occupation and industrial classification are also available. The ASHE sample is drawn from National Insurance records for working individuals, and the survey forms are sent to their respective employers to complete.

While limited in terms of personal characteristics compared to surveys such as the Labour Force Survey, the ASHE is useful not only because of its larger sample size, but also the responses regarding wages and hours are considered to be more accurate, since the responses are provided by employers rather than from employees themselves. A further advantage of the ASHE is that data for the same individuals are collected year after year. It is therefore possible to construct a panel dataset of responses for each individual running back as far as 1997, and to track how occupations, earnings and working hours change for individuals over time. Furthermore, using the unique business identifiers, it is possible to combine ASHE data with data from other business surveys, such as the Annual Business Survey (UK Data Archive SN 7451).

The ASHE replaced the New Earnings Survey (NES, SN 6704) in 2004. NES was developed in the 1970s in response to the policy needs of the time. The survey had changed very little in its thirty-year history. ASHE datasets for the years 1997-2003 were derived using ASHE methodologies applied to NES data.

The ASHE improves on the NES in the following ways:

• the NES questionnaire allowed too much variation in employer responses, leading to wide variations in the data
• weightings have been introduced to take account of the population size (significant biases were a known problem in NES data)
• the significant numbers of employees who change jobs between the sample selection and survey reference dates are retained in the ASHE sample, whereas these were dropped from the NES

Linking to other business studies
These data contain Inter-Departmental Business Register (IDBR) reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Observations from Northern Ireland
The ASHE data held by the UK Data Archive include very few observations from Northern Ireland. Users requiring access to Northern Ireland data are advised to contact the Northern Ireland Statistics and Research Agency, who administer this aspect of the survey.

Local unit reference variable, luref
The local unit reference variable 'luref', is generated to indicate multiple occurrences of the same local unit for disclosure checking purposes. It is inconsistent across years and is not an IDBR reference number. It should not be used to link ASHE with other business datasets.

For Secure Lab projects applying for access to this study as well as to SN 6697 Business Structure Database and/or SN 7683 Business Structure Database Longitudinal, only postcode-free versions of the data will be made available.

Latest Edition Information
For the twenty-sixth edition (February 2025), the data file 'ashegb_2023r_2024p_pc' has been added, along with the accompanying data dictionary.

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Big Data As A Service Market Size 2025-2029

The big data as a service market size is forecast to increase by USD 75.71 billion, at a CAGR of 20.5% between 2024 and 2029.

The Big Data as a Service (BDaaS) market is experiencing significant growth, driven by the increasing volume of data being generated daily. This trend is further fueled by the rising popularity of big data in emerging technologies, such as blockchain, which requires massive amounts of data for optimal functionality. However, this market is not without challenges. Data privacy and security risks pose a significant obstacle, as the handling of large volumes of data increases the potential for breaches and cyberattacks. Edge computing solutions and on-premise data centers facilitate real-time data processing and analysis, while alerting systems and data validation rules maintain data quality.
Companies must navigate these challenges to effectively capitalize on the opportunities presented by the BDaaS market. By implementing robust data security measures and adhering to data privacy regulations, organizations can mitigate risks and build trust with their customers, ensuring long-term success in this dynamic market.

What will be the Size of the Big Data As A Service Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market continues to evolve, offering a range of solutions that address various data management needs across industries. Hadoop ecosystem services play a crucial role in handling large volumes of data, while ETL process optimization ensures data quality metrics are met. Data transformation services and data pipeline automation streamline data workflows, enabling businesses to derive valuable insights from their data. Nosql database solutions and custom data solutions cater to unique data requirements, with Spark cluster management optimizing performance. Data security protocols, metadata management tools, and data encryption methods protect sensitive information. Cloud data storage, predictive modeling APIs, and real-time data ingestion facilitate agile data processing.
Data anonymization techniques and data governance frameworks ensure compliance with regulations. Machine learning algorithms, access control mechanisms, and data processing pipelines drive automation and efficiency. API integration services, scalable data infrastructure, and distributed computing platforms enable seamless data integration and processing. Data lineage tracking, high-velocity data streams, data visualization dashboards, and data lake formation provide actionable insights for informed decision-making.
For instance, a leading retailer leveraged data warehousing services and predictive modeling APIs to analyze customer buying patterns, resulting in a 15% increase in sales. This success story highlights the potential of big data solutions to drive business growth and innovation.

How is this Big Data As A Service Industry segmented?

The big data as a service 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.

Type

  Data Analytics-as-a-service (DAaaS)
  Hadoop-as-a-service (HaaS)
  Data-as-a-service (DaaS)


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


End-user

  Large enterprises
  SMEs


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Type Insights

The Data analytics-as-a-service (DAaas) segment is estimated to witness significant growth during the forecast period. The data analytics-as-a-service (DAaaS) segment experiences significant growth within the market. Currently, over 30% of businesses adopt cloud-based data analytics solutions, reflecting the increasing demand for flexible, cost-effective alternatives to traditional on-premises infrastructure. Furthermore, industry experts anticipate that the DAaaS market will expand by approximately 25% in the upcoming years. This market segment offers organizations of all sizes the opportunity to access advanced analytical tools without the need for substantial capital investment and operational overhead. DAaaS solutions encompass the entire data analytics process, from data ingestion and preparation to advanced modeling and visualization, on a subscription or pay-per-use basis. Data integration tools, data cataloging systems, self-service data discovery, and data version control enhance data accessibility and usability.

The continuous evolution of this market is driven by the increasing volume, variety, and velocity of data, as well as the growing recognition of the business value that can be derived from data insights. Organizations across var

The statistic presents the leading financial data service companies in the United States in 2015, by revenue. In that year, Visa was ranked second with the revenue of approximately 13.88 billion U.S. dollars.

By 2025, the AI basic data service market size in China was estimated to surpass ** billion yuan. Currently, voice, vision and natural language processing segments constituted to much of the AI basic data services.

The Big Data Services market is experiencing explosive growth, with a market size of $57.40 billion in 2025 and a projected Compound Annual Growth Rate (CAGR) of 55.18% from 2025 to 2033. This rapid expansion is driven by several key factors. Firstly, the increasing volume and complexity of data generated across various industries necessitates sophisticated solutions for data storage, processing, and analysis. The BFSI (Banking, Financial Services, and Insurance), Telecom, and Retail sectors are leading adopters, leveraging big data analytics for improved customer experience, risk management, and operational efficiency. Furthermore, advancements in cloud computing, artificial intelligence (AI), and machine learning (ML) are fueling the adoption of big data services, enabling more efficient and insightful data analysis. Finally, the growing demand for real-time data processing and advanced analytics is creating new opportunities for service providers. The market is segmented by component (solutions and services) and end-user (BFSI, Telecom, Retail, and Others), with North America currently holding a significant market share, followed by Europe and APAC. The competitive landscape is characterized by a mix of established technology giants (e.g., Microsoft, IBM, Oracle) and specialized big data solution providers. These companies are employing various strategies, including mergers and acquisitions, strategic partnerships, and product innovation, to gain market share and maintain a competitive edge. While the market exhibits significant growth potential, challenges remain, including the high cost of implementation, the need for skilled professionals, and concerns related to data security and privacy. Despite these restraints, the long-term outlook for the big data services market remains extremely positive, with continued expansion driven by technological advancements and increasing data volumes across all sectors. The forecast period of 2025-2033 promises even greater market expansion as organizations increasingly recognize the value of extracting actionable insights from their data.

The global sports data service market, valued at $1736 million in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 9.4% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing popularity of sports globally, coupled with the rising demand for advanced analytics in sports management and broadcasting, is a major catalyst. Furthermore, technological advancements, particularly in data collection and processing, are enabling the development of sophisticated data services catering to diverse needs within the industry. The rise of fantasy sports and esports further contributes to market growth, as these sectors heavily rely on accurate and timely sports data. The market is segmented by application (Professional Clubs, State Management Agencies, Others) and type of service (Sports Data Collection Service, Sports Data Analysis Service, Others). Professional clubs and sports data analysis services currently dominate, but other segments are poised for significant growth driven by increased adoption in emerging markets and the expanding use of data for fan engagement and player development. Market restraints primarily involve data privacy concerns and the competitive landscape. Stringent regulations regarding data usage and player information necessitate robust security protocols, increasing operational costs. Simultaneously, the market is characterized by intense competition amongst established players and new entrants vying for market share. This competition leads to pricing pressure, making innovation and differentiation crucial for sustainable growth. North America currently holds a significant market share due to a well-established sports infrastructure and advanced technological capabilities. However, rapidly growing interest in sports and increasing digital penetration in Asia-Pacific and other regions indicate a promising expansion potential for these markets in the forecast period. Successful companies in this space are adapting to these factors by focusing on data security, providing personalized and niche services, and forging strategic partnerships to expand their reach and maintain a competitive edge.

This session will focus on the baseline of skills that Data Liberation Initiative (DLI) Contacts should have and the corresponding training to achieve these skills. Introducing newcomers to the language of statistics and data is one of the important tasks of the orientation. Acquiring a technical language often poses a barrier to newcomers. To overcome this hurdle, newcomers must grasp both the meaning of new concepts and its abbreviated language of acronyms. Should we expect the orientation to offer all of the baseline skills or is other instruction needed? Do different local environments result in varying uses of DLI resources? Are the same skills needed among differing environments? How much attention should be paid during the orientation to different models of data service? For example, should the implications of buying services from elsewhere (e.g., Sherlock, IDLS, CHASS, Queen’s, etc.) be covered? What kind of distinctions need to be made for the levels of support for instructional and research uses of data? What about the reference uses of data, that is, using data to answer reference questions? Are there additional skills required of those supporting DLI data for research and reference uses? If there are, what are they and how should they be introduced?

The Center for Research in Security Prices (CRSP) stock databases provide time-series and event data on individual stocks, augmented with market time-series. Daily and monthly time-series variables include returns, closing, low bid and high ask prices, and trading volume. Event data includes distributions, shares outstanding, names, etc.

Dataset is an external database available here for Cornell affiliates: https://johnson.library.cornell.edu/database/wharton-research-data-services-wrds/

The ICSU-WDS & RDA Publishing Data Services group proposes an approach to sharing information about the links between the literature and research data. A set of hubs will collect literaturedata (as well as datadata) links from their natural communities using minor extensions to existing local procedures and, in some cases, inference. The hubs agree on an interoperability framework with a common information model and open exchange methods, optimised for exchanging information among the hubs. The hubs will serve as an enabling global information infrastructure for the development of (third party) services.

Archived as of 6/26/2025: The datasets will no longer receive updates but the historical data will continue to be available for download. This dataset provides information related to the major services for patients. It contains information about the total number of patients, total number of claims, and dollar amount paid, grouped by recipient zip code. Restricted to claims with service date between 01/2012 to 12/2017. Service categories considered are: 01 - Inpatient Service 03 - Outpatient Service 06 - Physician Service 11 - Lab Service 12 - X-Ray Service 17 - Clinic Service 26 - Mental Health Service 27 - Dental Service/Child 28 - Dental Service/Adult 31 - Eye Care and Exams 38 - EPSDT Service Provider is billing provider. This data is for research purposes and is not intended to be used for reporting. Due to differences in geographic aggregation, time period considerations, and units of analysis, these numbers may differ from those reported by FSSA. Distance between recipient and provider is a straight-line distance calculated and not the physical distance.

Sexual health services in York. For further information please visit City of York Council's website. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

This data set includes all channels of engagement in which a service request is created.

Refer to this link to learn more about BOS:311: https://www.cityofboston.gov/311/

9/22/25 UPDATE: In October 2025, 311 service requests will begin transitioning to a new backend system; cases from the new system will populate a new and differently structured dataset. During the transition, some cases will populate in the current dataset ("311 Service Requests - 2025") and some cases will populate in the new dataset (not yet posted) depending on the type of service the case requires. The data dictionary labeled NEW SYSTEM corresponds to the dataset for the new system. The Data Transition Guide provides current information on which case types to find in which dataset as well as how to use old and new datasets together. We anticipate all cases will be transitioned to the new system by mid-2026. If you have any questions on the upcoming changes, you can contact doitbcstech@boston.gov.

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Subscriber Data Management Market Size 2024-2028

The subscriber data management market size is forecast to increase by USD 4.08 billion at a CAGR of 16.9% between 2023 and 2028.

The market is experiencing significant growth due to the increasing adoption of target advertisement-based streaming apps. This trend is driven by the rising demand for personalized content and services, which necessitates effective management of subscriber data. Furthermore, the proliferation of 5G technology is fueling the need for faster and more secure data processing and transmission. However, this market is not without challenges. Data privacy and security risks continue to pose a significant threat, with subscriber data being a valuable asset for cybercriminals. Companies must invest in security measures to protect sensitive information and maintain customer trust. Additionally, regulatory compliance and data interoperability across multiple platforms are other challenges that market participants must navigate to capitalize on the opportunities presented by this dynamic market. Overall, the market offers significant potential for growth, particularly for those players who can effectively address the evolving needs of subscribers and mitigate the risks associated with managing large volumes of sensitive data.

What will be the Size of the Subscriber Data Management Market during the forecast period?

Request Free SampleThe market in the US is experiencing significant growth due to the increasing number of mobile subscriptions and the shift towards cloud-based solutions. Telecom operators are prioritizing network functions virtualization (NFV) and long-term evolution (LTE) technologies to enhance their mobile networks, leading to an escalating demand for user data repositories and policy management systems. Telecommunication network providers are also focusing on data security to mitigate cyberattacks and ensure data privacy policies are adhered to. Moreover, the proliferation of Voice over LTE (VoLTE) and Volte services, as well as the integration of subscriber data management systems in telecom service providers' customer relationship management (CRM) and identity management solutions, is driving market expansion. The market is expected to continue growing as 5G subscriptions increase, with hybrid solutions gaining popularity among network carriers to optimize their on-premise and cloud-based offerings. The market's size and direction reflect the industry's commitment to delivering secure, efficient, and innovative subscriber data management solutions to meet the evolving needs of mobile subscribers.

How is this Subscriber Data Management Industry segmented?

The subscriber data management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. TypeMobile networksFixed networksGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanMiddle East and AfricaSouth America

By Type Insights

The mobile networks segment is estimated to witness significant growth during the forecast period.The Subscriber Data Management (SDM) market is driven by the mobile networks segment, which accounts for a substantial revenue share. This growth is attributed to the widespread use of mobile devices and the escalating demand for high-speed data services, particularly with the emergence of 5G technology. Mobile networks necessitate advanced SDM solutions to manage the voluminous subscriber data, ensuring uninterrupted service delivery and superior user experiences. The integration of 5G has intensified the need for sophisticated SDM systems due to the complexities introduced in data management, including real-time processing, authentication, and security. Cloud-based SDM solutions with cloud-native design are increasingly popular due to their flexibility, scalability, and ability to handle large volumes of data. Identity management, data integration, and policy management are crucial components of these solutions. The Internet of Things (IoT) and Voice Over IP (VoIP) are additional areas driving the market, as they generate substantial subscriber data that needs to be managed effectively. Data security and privacy are paramount concerns, necessitating the adoption of advanced security solutions and adherence to stringent data privacy policies. Network Carriers, Telecom Operators, and Communication Service Providers (CSPs) are key players in the market, leveraging SDM systems to manage their subscriber data and enhance network performance. Network Functions Virtualization (NFV) and Long-Term Evolution (LTE) are key technologies enabling the deployment of SDM solutions in a hybrid environment, ensuring seamless integration with fixed networks and mobile networks. The market is further fueled by the increas

National statistical systems are facing significant challenges. These challenges arise from increasing demands for high quality and trustworthy data to guide decision making, coupled with the rapidly changing landscape of the data revolution. To help create a mechanism for learning amongst national statistical systems, the World Bank has developed improved Statistical Performance Indicators (SPI) to monitor the statistical performance of countries. The SPI focuses on five key dimensions of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. This will replace the Statistical Capacity Index (SCI) that the World Bank has regularly published since 2004. The SPI focus on five key pillars of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. The SPI are composed of more than 50 indicators and contain data for 174 countries. This set of countries covers 99.2 percent of the world population. The data extend from 2016-2019, with some indicators going back to 2004.

Lebanon LB: SPI: Pillar 2 Data Services Score: Scale 0-100 data was reported at 61.567 NA in 2023. This stayed constant from the previous number of 61.567 NA for 2022. Lebanon LB: SPI: Pillar 2 Data Services Score: Scale 0-100 data is updated yearly, averaging 62.083 NA from Dec 2016 (Median) to 2023, with 8 observations. The data reached an all-time high of 66.067 NA in 2021 and a record low of 8.500 NA in 2016. Lebanon LB: SPI: Pillar 2 Data Services Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Lebanon – Table LB.World Bank.WDI: Governance: Policy and Institutions. The data services pillar overall score is a composite indicator based on four dimensions of data services: (i) the quality of data releases, (ii) the richness and openness of online access, (iii) the effectiveness of advisory and analytical services related to statistics, and (iv) the availability and use of data access services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including input to national data strategies, advice on data ethics and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;