The majority of respondents stated that their company used more than one database for their operations. This indicates the complexity of maintaining security of IT infrastructure at organizations. Microsoft Azure database (** percent) and Microsoft SQL Server (** percent) were the most commonly used databases among respondents.

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

A good DATA is crucial for any business or organization to grow the network. This is because all relevant details about the company and user are stored in the database. Your companies have benefited from using our email database to extract their prospect's details.

It is a well-known fact that LinkedIn gives you the opportunity to expand your business network. You can easily connect with your prospects, directly or through mutual connections, by using search keywords related to their name, company, profile, address, etc. However, we're a leading data provider, with us you do not need to do such a thing. Our Professional's email database contains all the necessary business information from your prospects. There are several ways to access them (especially email addresses and phone numbers).

With our service, you can reach over 69 million records in 200+ countries. Our database is well organized and keeps information easily accessible, so you can use it. Easily increase your sales with reliable LinkedIn data that connects you directly to your goal, here we have worked hard to supply quality, reliable, sustainable email databases.

CompanyData.com, powered by BoldData, offers high-quality, verified company data from official trade registers around the world. Our Hong Kong database includes 1,978,451 verified company records, giving you a clear, up-to-date view of one of Asia’s most dynamic business hubs.

Each Hong Kong company profile is packed with firmographic and structural data, including company name, registration number, business status, legal entity type, incorporation date, and industry classification. Many records are enhanced with decision-maker contact details, such as email addresses, mobile numbers, and direct phone lines, where available.

Our Hong Kong company data is trusted for a wide range of business applications, including compliance and KYC checks, B2B lead generation, sales outreach, market research, CRM enrichment, and AI model training. Whether you're targeting global enterprises, SMEs, or startups registered in Hong Kong, our database gives you the clarity and precision you need.

We offer flexible delivery formats to match your workflow — from tailored company lists and full datasets in Excel or CSV, to seamless integration via our real-time API or self-service platform. You can also enhance your own databases with our data enrichment and cleansing services, using fresh, verified data from Hong Kong.

With access to a global database of 1,978,451 verified companies, CompanyData.com empowers you to scale your business locally and internationally. Whether you're navigating regulatory requirements or building new B2B pipelines, our accurate, ready-to-use data helps you succeed in Hong Kong and beyond.

Approximately ** percent of the surveyed software companies in Russia mentioned PostgreSQL, making it the most popular database management system (DBMS) in the period between February and May 2022. MS SQL and MySQL followed, having been mentioned by ** percent and ** percent of respondents, respectively.

The size of the In Memory Database Industry market was valued at USD XX Million in 2024 and is projected to reach USD XXX Million by 2033, with an expected CAGR of 19.00% during the forecast period. Recent developments include: May 2022: IBM and SAP announced the extension of their collaboration as IBM embarks on a corporate transformation initiative to optimize its business operations using RISE and SAP S/4HANA Cloud. To execute work for over 1,000 legal entities in more than 120 countries and multiple IBM companies supporting hardware, software, consulting, and finance, IBM said it is transferring to SAP S/4HANA, SAP's most recent ERP system, as part of the extended relationship. The replacement for SAP R/3 and SAP ERP, SAP S/4HANA, is SAP's ERP system for large businesses. It is intended to work optimally with SAP's in-memory database, SAP HANA., November 2022: Redis, a provider of real-time in-memory databases, and Amazon Web Services have announced a multi-year strategic alliance. Redis is a networked, open-source NoSQL system that stores data on disk for durability before moving it to DRAM as necessary. It can function as a streaming engine, message broker, database, or cache. The business claims that when Redis is used as a database, apps may instantly search across tens of millions of rows of customer data to locate information specific to one particular customer. A managed database-as-a-service product on AWS is called the real-time Redis Enterprise Cloud., December 2022: The National Stock Exchange, the largest stock exchange in India, chose the Raima Database Manager (RDM) Workgroup 12.0 in-memory system as a foundational component for the next iterations of its trading platform front-end, the National Exchange for Automated Trading (NEAT).. Key drivers for this market are: Decreasing Hardware Cost, Increasing Penetration Of Trends Like Big Data And IOT; Increase In The Volume Of Data Generated And Shift Of Enterprise Operations. Potential restraints include: Resilience In Integration With VLDB'S. Notable trends are: Telecommunication End-User Industry to Hold Significant Market Share.

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

Detailed Financial Profiles: Gain insights into company financials, including revenue, profit margins, funding rounds, and operational costs. Profiles are enriched with key contact details, including work emails, phone numbers, and physical addresses, ensuring direct access to decision-makers.

Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

Real-Time Accuracy: With continuous updates powered by AI-driven validation, our financial data maintains a 99% accuracy rate, ensuring you have access to the most reliable and up-to-date information available.

Compliance and Security: All data is collected and processed in strict adherence to global compliance standards, including GDPR, ensuring ethical and lawful usage.

Why Choose Success.ai for Company Financial Data?

Best Price Guarantee: We pride ourselves on offering the most competitive pricing in the industry, ensuring you receive unparalleled value for comprehensive financial data.

AI-Validated Accuracy: Our advanced AI algorithms meticulously verify every data point to ensure precision and reliability, helping you avoid costly errors in your financial decision-making.

Customized Data Solutions: Whether you need data for a specific region, industry, or type of business, we tailor our datasets to align perfectly with your requirements.

Scalable Data Access: From small startups to global enterprises, our platform caters to businesses of all sizes, delivering scalable solutions to suit your operational needs.

Comprehensive Use Cases for Financial Data:

1. Strategic Financial Planning:

Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

1. Mergers and Acquisitions (M&A):

Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

1. Investment Analysis:

Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

1. Lead Generation and Sales:

Enhance your sales outreach by targeting CFOs, financial analysts, and other decision-makers with verified contact details. Utilize accurate email and phone data to increase conversion rates.

1. Market Research:

Understand market trends and financial benchmarks with our industry-specific datasets. Use the data for competitive analysis, benchmarking, and identifying market gaps.

APIs to Power Your Financial Strategies:

Enrichment API: Integrate real-time updates into your systems with our Enrichment API. Keep your financial data accurate and current to drive dynamic decision-making and maintain a competitive edge.

Lead Generation API: Supercharge your lead generation efforts with access to verified contact details for key financial decision-makers. Perfect for personalized outreach and targeted campaigns.

Tailored Solutions for Industry Professionals:

Financial Services Firms: Gain detailed insights into revenue streams, funding rounds, and operational costs for competitor analysis and client acquisition.

Corporate Finance Teams: Enhance decision-making with precise data on industry trends and benchmarks.

Consulting Firms: Deliver informed recommendations to clients with access to detailed financial datasets and key stakeholder profiles.

Investment Firms: Identify potential investment opportunities with verified data on financial performance and market positioning.

What Sets Success.ai Apart?

Extensive Database: Access detailed financial data for 70M+ companies worldwide, including small businesses, startups, and large corporations.

Ethical Practices: Our data collection and processing methods are fully comp...

This is a beginner-friendly SQLite database designed to help users practice SQL and relational database concepts. The dataset represents a basic business model inspired by NVIDIA and includes interconnected tables covering essential aspects like products, customers, sales, suppliers, employees, and projects. It's perfect for anyone new to SQL or data analytics who wants to learn and experiment with structured data.

Tables and Their Contents:

Products:

Includes details of 15 products (e.g., GPUs, AI accelerators). Attributes: product_id, product_name, category, release_date, price.

Customers:

Lists 20 fictional customers with their industry and contact information. Attributes: customer_id, customer_name, industry, contact_email, contact_phone.

Sales:

Contains 100 sales records tied to products and customers. Attributes: sale_id, product_id, customer_id, sale_date, region, quantity_sold, revenue.

Suppliers:

Features 50 suppliers and the materials they provide. Attributes: supplier_id, supplier_name, material_supplied, contact_email.

Supply Chain:

Tracks materials supplied to produce products, proportional to sales. Attributes: supply_chain_id, supplier_id, product_id, supply_date, quantity_supplied.

Departments:

Lists 5 departments within the business. Attributes: department_id, department_name, location.

Employees:

Contains data on 30 employees and their roles in different departments. Attributes: employee_id, first_name, last_name, department_id, hire_date, salary.

Projects:

Describes 10 projects handled by different departments. Attributes: project_id, project_name, department_id, start_date, end_date, budget.

Why Use This Dataset?

• Perfect for Beginners: The dataset is simple and easy to understand.
• Interconnected Tables: Provides a basic introduction to relational database concepts like joins and foreign keys.
• SQL Practice: Run basic queries, filter data, and perform simple aggregations or calculations.
• Learning Tool: Great for small projects and understanding business datasets.

Potential Use Cases:

• Practice SQL queries (SELECT, INSERT, UPDATE, DELETE, JOIN).
• Understand how to design and query relational databases.
• Analyze basic sales and supply chain data for patterns and trends.
• Learn how to use databases in analytics tools like Excel, Power BI, or Tableau.

Data Size:

Number of Tables: 8 Total Rows: Around 230 across all tables, ensuring quick queries and easy exploration.

The Public Use Database (PUDB) is released annually to meet FHFA’s requirement under 12 U.S.C. 4543 and 4546(d) to publicly disclose data about the Enterprises’ single-family and multifamily mortgage acquisitions. The datasets supply mortgage lenders, planners, researchers, policymakers, and housing advocates with information concerning the flow of mortgage credit in America’s neighborhoods. Beginning with data for mortgages acquired in 2018, FHFA has ordered that the PUDB be expanded to include additional data that is the same as the data definitions used by the regulations implementing the Home Mortgage Disclosure Act, as required by 12 U.S.C. 4543(a)(2) and 4546(d)(1).The PUDB single-family datasets include loan-level records that include data elements on the income, race, and sex of each borrower as well as the census tract location of the property, loan-to-value (LTV) ratio, age of mortgage note, and affordability of the mortgage. New for 2018 are the inclusion of the borrower’s debt-to-income (DTI) ratio and detailed LTV ratio data at the census tract level. The PUDB multifamily property-level datasets include information on the unpaid principal balance and type of seller/servicer from which the Enterprise acquired the mortgage. New for 2018 is the inclusion of property size data at the census tract level. The multifamily unit-class files also include information on the number and affordability of the units in the property. Both the single-family and multifamily datasets include indicators of whether the purchases are from “underserved” census tracts, as defined in terms of median income and minority percentage of population.Prior to 2010 the single-family PUDB consisted of three files: Census Tract, National A, and National B files. With the 2010 PUDB a fourth file, National C, was added to provide information on high-cost mortgages acquired by the Enterprises. The single-family Census Tract file includes information on the location of the property based on the 2010 Census for acquisition years 2012 through 2021, and the 2020 Census beginning with the 2022 acquisition year. The National files contain other information but lack detailed geographic information in order to protect Enterprise proprietary data. The multifamily datasets also consist of a Census Tract file, and a National file without detailed geographic information.Several dashboards are available to analyze the data:Enterprise Multifamily Public Use Database DashboardThe Enterprise Multifamily Public Use Database (PUDB) Dashboard provides users an interactive way to generate and visualize Enterprise PUDB data of multifamily mortgage acquisitions by Fannie Mae and Freddie Mac. It shows characteristics about multifamily loans, properties and units at the national level, and characteristics about multifamily loans and properties at the state level. It includes key statistics, time series charts, and state maps of multifamily housing characteristics such as median loan amount, number of properties, average number of units per property, and unit affordability. The underlying aggregate statistics presented in the dashboard come from three multifamily data files in the Enterprise PUDB, updated annually since 2008, including two property-level datasets and a data file on the size and affordability of individual units.Enterprise Multifamily Public Use DashboardPress Release - FHFA Releases Data Visualization Dashboard for Enterprises’ Multifamily Mortgage AcquisitionsMortgage Loan and Natural Disaster DashboardFHFA published an interactive Mortgage Loan and Natural Disaster Dashboard that combines FHFA’s PUDB reports on single-family and multifamily acquisitions for the regulated entities, FEMA’s National Risk Index (NRI), and FHFA’s Duty to Serve 2023 High-Needs rural areas. Desired geographies can be exported to .pdf and Excel from the Public Use Database and National Risk Index Dashboard.Mortgage Loan and Natural Disaster DashboardMortgage Loan and Natural Disaster Dashboard FAQs

The initial sample of this study covers the A-share companies listed on the Shanghai and Shenzhen stock exchanges during the period 2008-2021. We then screened and processed the initial sample data, including (a) Screening for companies with both RepRisk's ESG rating and Bloomberg's ESG rating. Specifically, the selection is based on samples with the same ISIN code and companies' English names in the Bloomberg and RepRisk lndex (RRI) databases. The ISIN code is a securities coding standard developed by the International Organization for Standardization (ISO) and is a unique code used to identify securities in each country or region around the world. We exclude samples that do not provide ISIN codes or have inconsistent English names. (b) We exclude observations with missing values for the main variables. (c) We exclude the ST, *ST and PT trading status samples during the observation period. Our final sample contains 1456 firm-year observations.The ESG disclosure score data and ESG performance score data required for the ESG-washing construction are respectively obtained from the Bloomberg database and the RepRisk Index (RRI) database of the Wharton Research Centre for Data Studies (WRDS). Positive media coverage data is sourced from the China Research Data Services Platform (CNRDS), while the instrumental variable (IV_population) is obtained from the EPS database and Juhe Data (https://www.gotohui.com/). Unless otherwise stated, all other data in this study are from the China Stock Market and Accounting Research (CSMAR) database.Data on executive company changes were collected manually by the authors back-to-back and independently. Then we compared and reconciled the data collected by each, and where there were discrepancies, we again collected and calibrated the data to maximize their reliability. We first obtained executive biographies from the CSMAR database, and the missing values were retrieved from Sina Finance ( https://finance.sina.com.cn/). Due to the unstructured nature of the resume data, we manually processed more than 30,000 resumes of executives to get the data of executives' company changes, based on which we calculated the per capita number of job hops of all executives in each company. The number of part-time jobs held by executives also reflects their pursuit of career changes and development, so in the robustness test the per capita mean of the number of part-time jobs held by executives is used as a proxy variable for careerist orientation. These data can be obtained directly from the CSMAR database.

▲ INSEE modernises its API portal, building on a new architecture ▲:

The general conditions of use of the portal, as well as those of the APIs presented, remain unchanged. Under the URL of the new portal https://portail-api.insee.fr/, you will find INSEE’s dissemination APIs.

Access to the SIRENE API:

To access the SIRENE API, you must first create an account on the new portal and then subscribe to the API. Instructions for use here. The same account can subscribe to several APIs, following the same procedure.

!! Attention, we advise you to quickly integrate this new environment, by the end of the year, the Sirene API will be accessible only from this new portal. !!

To subscribe to our newsletter Sirene open data news, click here To consult our newsletters Sirene open data news, click here

Stock files

• On May 1st, a new monthly file stockDoublons is proposed, it consists of the list of siren and their duplicates with the date of last treatment always in CSV format.
• The final stock files in 3.11 format are published on 26 March 2024 instead of the previous 3.9 files.

Six compacted monthly stock files (ZIP format) are available: - the stock file of legal units (active and discontinued legal units in their current state in the directory) - the stock file of the historical values of the legal units - the inventory file of establishments (active and closed establishments in their current state in the directory) - the stock file of the historical values of the establishments - the stock file of the succession links of the establishments - the stock file of duplicate siren

Each compacted file (ZIP format) contains a data file in CSV format. Files uploaded from the 1st of the month are an image of the Sirene directory as of the last day of the previous month. A stock file of a given month replaces that of the previous month. Discontinued legal units and closed establishments are included, thus providing access to Sirene data since 1973.

Updates

Infra-monthly updates of these files, including daily updates, are possible: - using the SIRENE APIs available on the catalogue of the INSEE APIs. With the API, you have access to variables indicating, for both establishments and legal units, the date of the last processing carried out. These are the variables dateLastUniteLegalTreatment and dateLastEstablishmentTreatment. Since this date is different from the date of the same record in your stock file, you know that an update has been made. Documentation on Sirene API variables and services is available on the [Documentation] tab (https://porttail-api.insee.fr/catalog/api/2ba0e549-5587-3ef1-9082-99cd865de66f/doc?page=52d26f24-963b-4fc0-926f-24963b4fc021) of each API; - using "Build a list" on sirene.fr (select Update Date tab) to be able to download files consisting of daily updates. You can consult the Sirene letter open data news n°2.

Siren database containing personal data, INSEE draws your attention to the legal obligations arising therefrom: - The processing of these data falls in particular under the obligations of the General Data Protection Regulation (GDPR), of Law 78-17 of 6 January 1978 as amended, known as CNIL Law - Depending on your use of the dataset, it is therefore your responsibility to take into account the most recent distribution status of each natural person, which takes into account the objections made by some of them, to the consultation or use of their SIRENE data by third parties other than authorized administrations or bodies. - Legal units or establishments which have a distribution status coded ‘P’ (resp. statusDiffusionUniteLegale or statusDiffusionEtablissement) are subject to partial dissemination of data following a request for opposition. For an objection by a natural person, the identity of the entrepreneur (surname, first names, etc.), the address in the municipality and the geolocation will be masked (i.e. not disseminated by the SIRENE API). In case of opposition by legal representatives of a legal person, the address of the establishment in the municipality and its geolocation will be hidden. It is understood that data relating to legal representatives are not disseminated by INSEE as Open Data, even in the absence of opposition, in accordance with**Article R 123-232** of the French Commercial C

Snapshot img

Graph Database Market Size 2025-2029

The graph database market size is valued to increase by USD 11.24 billion, at a CAGR of 29% from 2024 to 2029. Open knowledge network gaining popularity will drive the graph database market.

Market Insights

North America dominated the market and accounted for a 46% growth during the 2025-2029.
By End-user - Large enterprises segment was valued at USD 1.51 billion in 2023
By Type - RDF segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 670.01 million 
Market Future Opportunities 2024: USD 11235.10 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth due to the increasing demand for low-latency query capabilities and the ability to handle complex, interconnected data. Graph databases are deployed in both on-premises data centers and cloud regions, providing flexibility for businesses with varying IT infrastructures. One real-world business scenario where graph databases excel is in supply chain optimization. In this context, graph databases can help identify the shortest path between suppliers and consumers, taking into account various factors such as inventory levels, transportation routes, and demand patterns. This can lead to increased operational efficiency and reduced costs.
However, the market faces challenges such as the lack of standardization and programming flexibility. Graph databases, while powerful, require specialized skills to implement and manage effectively. Additionally, the market is still evolving, with new players and technologies emerging regularly. Despite these challenges, the potential benefits of graph databases make them an attractive option for businesses seeking to gain a competitive edge through improved data management and analysis.

What will be the size of the Graph Database Market during the forecast period?

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

The market is an evolving landscape, with businesses increasingly recognizing the value of graph technology for managing complex and interconnected data. According to recent research, the adoption of graph databases is projected to grow by over 20% annually, surpassing traditional relational databases in certain use cases. This trend is particularly significant for industries requiring advanced data analysis, such as finance, healthcare, and telecommunications. Compliance is a key decision area where graph databases offer a competitive edge. By modeling data as nodes and relationships, organizations can easily trace and analyze interconnected data, ensuring regulatory requirements are met. Moreover, graph databases enable real-time insights, which is crucial for budgeting and product strategy in today's fast-paced business environment.
Graph databases also provide superior performance compared to traditional databases, especially in handling complex queries involving relationships and connections. This translates to significant time and cost savings, making it an attractive option for businesses seeking to optimize their data management infrastructure. In conclusion, the market is experiencing robust growth, driven by its ability to handle complex data relationships and offer real-time insights. This trend is particularly relevant for industries dealing with regulatory compliance and seeking to optimize their data management infrastructure.

Unpacking the Graph Database Market Landscape

In today's data-driven business landscape, the adoption of graph databases has surged due to their unique capabilities in handling complex network data modeling. Compared to traditional relational databases, graph databases offer a significant improvement in query performance for intricate relationship queries, with some reports suggesting up to a 500% increase in query response time. Furthermore, graph databases enable efficient data lineage tracking, ensuring regulatory compliance and enhancing data version control. Graph databases, such as property graph models and RDF databases, facilitate node relationship management and real-time graph processing, making them indispensable for industries like finance, healthcare, and social media. With the rise of distributed and knowledge graph databases, organizations can achieve scalability and performance improvements, handling massive datasets with ease. Security, indexing, and deployment are essential aspects of graph databases, ensuring data integrity and availability. Query performance tuning and graph analytics libraries further enhance the value of graph databases in data integration and business intelligence applications. Ultimately, graph databases offer a powerful alternative to NoSQL databases, providing a more flexible and efficient approach to managing complex data relationships.

Key Market Drivers Fueling Growth

The growing popularity o

1The totals in this column equal the number of articles using a particular type of data, minus instances of duplicate classification by type of company within category of type of data. These instances were: Other types of data were used by articles classified as both tobacco and transportation, both mining and manufacturing, and both tobacco and alcohol, and quantitative data from internal company studies were used by the article classified as both mining and manufacturing. The overall column total is not shown, as it is greater than the total number of included articles (n = 361) because several articles used multiple types of internal documents.2The totals in this row equal the total number of articles for each type of company, minus instances where articles used multiple types of data, of which there are too many to list. The totals for the columns are therefore not equal to the sum of the classifications within the columns. The overall row total is not shown, as it is greater than the total number of included articles (N = 361) because three articles were classified with two types of companies.

Maximize your business potential with Success.ai's LinkedIn Company and Contact Data, a comprehensive solution designed to empower your business with strategic insights drawn from one of the largest professional networks in the world. This extensive dataset includes in-depth profiles from over 700 million professionals and 70 million companies globally, making it a goldmine for businesses aiming to enhance their marketing strategies, refine competitive intelligence, and drive robust B2B lead generation.

Transform Your Email Marketing Efforts With Success.ai, tap into highly detailed and direct contact data to personalize your communications effectively. By accessing a vast array of email addresses, personalize your outreach efforts to dramatically improve engagement rates and conversion possibilities.

Data Enrichment for Comprehensive Insights Integrate enriched LinkedIn data seamlessly into your CRM or any analytical system to gain a comprehensive understanding of your market landscape. This enriched view helps you navigate through complex business environments, enhancing decision-making and strategic planning.

Elevate Your Online Marketing Deploy targeted and precision-based online marketing campaigns leveraging detailed professional data from LinkedIn. Tailor your messages and offers based on specific professional demographics, industry segments, and more, to optimize engagement and maximize online marketing ROI.

Digital Advertising Optimized Utilize LinkedIn’s precise company and professional data to create highly targeted digital advertising campaigns. By understanding the profiles of key decision-makers, tailor your advertising strategies to resonate well with your target audience, ensuring high impact and better expenditure returns.

Accelerate B2B Lead Generation Identify and connect directly with key stakeholders and decision-makers to shorten your sales cycles and close deals quicker. With access to high-level contacts in your industry, streamline your lead generation process and enhance the efficiency of your sales funnel.

Why Partner with Success.ai for LinkedIn Data? - Competitive Pricing Assurance: Success.ai guarantees the most aggressive pricing, ensuring you receive unbeatable value for your investment in high-quality professional data. - Global Data Access: With coverage extending across 195 countries, tap into a rich reservoir of professional information, covering diverse industries and market segments. - High Data Accuracy: Backed by advanced AI technology and manual validation processes, our data accuracy rate stands at 99%, providing you with reliable and actionable insights. - Custom Data Integration: Receive tailored data solutions that fit seamlessly into your existing business processes, delivered in formats such as CSV and Parquet for easy integration. - Ethical Data Compliance: Our data sourcing and processing practices are fully compliant with global standards, ensuring ethical and responsible use of data. - Industry-wide Applications: Whether you’re in technology, finance, healthcare, or any other sector, our data solutions are designed to meet your specific industry needs.

Strategic Use Cases for Enhanced Business Performance - Email Marketing: Leverage accurate contact details for personalized and effective email marketing campaigns. - Online Marketing and Digital Advertising: Use detailed demographic and professional data to refine your online presence and digital ad targeting. - Data Enrichment and B2B Lead Generation: Enhance your databases and accelerate your lead generation with enriched, up-to-date data. - Competitive Intelligence and Market Research: Stay ahead of the curve by using our data for deep market analysis and competitive research.

With Success.ai, you’re not just accessing data; you’re unlocking a gateway to strategic business growth and enhanced market positioning. Start with Success.ai today to leverage our LinkedIn Company Data and transform your business operations with precision and efficiency.

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Elevate B2B outreach with DataCaptive's precise 98M+ geo-targeted B2B contact data. Unlock growth opportunities effortlessly.

Bank data is a comprehensive list of phone numbers for businesses and individuals. With bank accounts, ensuring compliance with data privacy regulations. Firstly, it allows businesses to filter the data by account type, transaction history, location, and other criteria, enabling them to identify their target audience for marketing campaigns. Bank data is continuously updated, ensuring the accuracy and relevance of contact information for effective outreach. Moreover, this data enables businesses to connect with potential customers quickly, accelerating business growth and facilitating efficient customer acquisition. In addition, telemarketing campaigns using Bank data are highly effective, as they directly reach individuals with existing relationships with financial institutions, indicating potential interest in their offerings. It is available on List To Data. Bank number databases are essential tools for businesses to gather accurate and reliable data from reputable sources. These databases ensure that data is collected methodically, resulting in high accuracy and validity. Businesses may increase openness and trust by visiting the source URL and verifying the provenance of the content. Regular updates bring new, up-to-date information, preventing obsolete contact information from interfering with business operations. Businesses that use an up-to-date Bank number database may connect with potential customers swiftly and efficiently, saving time and resources. This database is a useful tool for organizations looking to broaden their reach and create long-term client connections. Constant assistance guarantees a smooth and efficient experience, allowing businesses to focus on making real connections and driving company success. It is available on List To Data.

As of November 2024, the application and data-centric security technology most used by companies worldwide was database firewall. At the same time, over ** percent of respondents stated that their company already used web application firewall (WAF). Moreover, the data security technology that most companies planned to acquire in the next 12 months was bot management.