Success.ai’s LinkedIn Data Solutions provide unparalleled access to a comprehensive dataset of 700 million public LinkedIn profiles and 70 million company records. This vast collection of corporate data is essential for businesses aiming to enhance recruitment, lead generation, and personalized B2B marketing campaigns.

Our LinkedIn data offerings are designed to streamline your operations, whether you’re enhancing CRM systems with up-to-date LinkedIn profile data, refining email address data for targeted outreach, or utilizing UK B2B data for expansive market reach. Every dataset includes detailed insights across more than 40 critical data points per profile, encompassing education, professional history, and specialized skills.

• Global Reach: Access data across industries globally, including detailed insights on 259M profiles in the USA and 22M in the UK.
• Real-Time Updates: Profiles updated daily to provide the most current data, helping you make informed business decisions quickly.
• AI-Verified Accuracy: Rigorous data validation ensures 99% accuracy, offering you reliability in data-driven strategies.
• Ethical Data Sourcing: Compliance with GDPR and other global privacy standards ensures responsible data utilization.
• Tailored Data Solutions: Our white-glove service customizes data delivery, catering to specific business requirements without the need for you to navigate complex platforms.

Why Success.ai stands out:

• Best Price Guarantee: We confidently beat any competitor’s pricing, ensuring you receive the best deal for comprehensive LinkedIn data.
• Comprehensive Corporate Coverage: From small business contact data to extensive corporate profiles, we cover all bases.
• Flexible Data Integration: Receive data in formats that seamlessly integrate into your systems, enhancing both efficiency and productivity.
• Dedicated Support: Our Success Managers ensure a hassle-free experience by managing the data curation and delivery process for you.

Key Use Cases:

• Sales Prospecting: Utilize detailed corporate data to build precise lead lists and engage with decision-makers effectively.
• Recruitment: Access up-to-date employee data to identify and attract top talent worldwide.
• Account-Based Marketing (ABM): Leverage detailed firmographic data to create highly personalized marketing campaigns targeted at key accounts.
• Market Research: Use comprehensive industry data to gain insights into market trends and competitor strategies.
• CRM Enrichment: Enhance your CRM with real-time data updates, maintaining the accuracy and relevance of your sales and marketing tools.

Start transforming your business strategies with Success.ai’s LinkedIn Data Solutions today. Contact us to customize your dataset and leverage our best price guarantee along with our specialized personal service to propel your business forward with confidence.

No one beats us on price. Period.

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Search for a business by name. You can obtain business information and then proceed to purchase a certificate of good standing or other documents. The purpose of this search is simply to determine whether a company/entity exists and to provide basic information on the company/entity.

With 1.9 Million Businesses in Hong Kong , Techsalerator has access to the highest B2B count of Data/Business Data in the country. .

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all regions and cities in Hong Kong : ( example) Kowloon Northern Hong Kong Island Tsuen Wan New Town Sha Tin New Town Tuen Mun New Town Tseung Kwan O New Town Aberdeen Tai Po New Town Tin Shui Wai New Town Fanling-Sheung Shui New Town Yuen Long New Town North Lantau New Town

With 1.8 Million Businesses in Sweden , Techsalerator has access to the highest B2B count of Data/Business Data in the country. .

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

Techsalerator covers all regions and cities in the country :

Blekinge Karlskrona Dalarna Borlänge Falun Gävleborg/Gavleborg Gävle/Gavle Gotland Visby Halland Halmstad Jämtland Östersund/Osterund Jönköping/Jonkoping Jönköping Kalmar Kalmar Kronoberg Växjö/Vaxjo Norrbotten Kiruna Luleå Örebro/Orebro Örebro Östergötland/Ostergotland Linköping/Linkoping Norrköping/Norrkoping Skåne/Skane Helsingborg Kristianstad Landskrona Lund Malmö/Malmo Trelleborg Södermanland/ Sodermanland Eskilstuna Nyköping/ Nykoping Stockholm Södertälje/ Sodertalje Solna Stockholm Uppsala Uppsala Värmland/ Varmland Karlstad Västerbotten/ Vasterbotten Umeå/ Umea Västernorrland/ Vasternorrland Sundsvall Västmanland/ Vastmanland Västerås/ Vasteras Västra Götaland/ Vastra Gotaland Borås/ Boras Gothenburg Lidköping/ Lidkoping Skara

Big Data and Business Analytics Market Outlook

In 2023, the global Big Data and Business Analytics market size is estimated to be valued at approximately $274 billion, and with a projected compound annual growth rate (CAGR) of 12.4%, it is anticipated to reach around $693 billion by 2032. This significant growth is driven by the escalating demand for data-driven decision-making processes across various industries, which leverage insights derived from vast data sets to enhance business efficiency, optimize operations, and drive innovation. The increasing adoption of Internet of Things (IoT) devices, coupled with the exponential growth of data generated daily, further propels the need for advanced analytics solutions to harness and interpret this information effectively.

A critical growth factor in the Big Data and Business Analytics market is the increasing reliance on data to gain a competitive edge. Organizations are now more than ever looking to uncover hidden patterns, correlations, and insights from the data they collect to make informed decisions. This trend is especially prominent in industries such as retail, where understanding consumer behavior can lead to personalized marketing strategies, and in healthcare, where data analytics can improve patient outcomes through precision medicine. Moreover, the integration of big data analytics with artificial intelligence and machine learning technologies is enabling more accurate predictions and real-time decision-making, further enhancing the value proposition of these analytics solutions.

Another key driver of market growth is the continuous technological advancements and innovations in data analytics tools and platforms. Companies are increasingly investing in advanced analytics capabilities, such as predictive analytics, prescriptive analytics, and real-time analytics, to gain deeper insights into their operations and market environments. The development of user-friendly and self-service analytics tools is also democratizing data access within organizations, empowering employees at all levels to leverage data in their daily decision-making processes. This democratization of data analytics is reducing the reliance on specialized data scientists, thereby accelerating the adoption of big data analytics across various business functions.

The increasing emphasis on regulatory compliance and data privacy is also driving growth in the Big Data and Business Analytics market. Strict regulations, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, require organizations to manage and analyze data responsibly. This is prompting businesses to invest in robust analytics solutions that not only help them comply with these regulations but also ensure data integrity and security. Additionally, as data breaches and cybersecurity threats continue to rise, organizations are turning to analytics solutions to identify potential vulnerabilities and mitigate risks effectively.

Regionally, North America remains a dominant player in the Big Data and Business Analytics market, benefiting from the presence of major technology companies and a high rate of digital adoption. The Asia Pacific region, however, is emerging as a significant growth area, driven by rapid industrialization, urbanization, and increasing investments in digital transformation initiatives. Europe also showcases a robust market, fueled by stringent data protection regulations and a strong focus on innovation. Meanwhile, the markets in Latin America and the Middle East & Africa are gradually gaining momentum as organizations in these regions are increasingly recognizing the value of data analytics in enhancing business outcomes and driving economic growth.

Component Analysis

The Big Data and Business Analytics market is segmented by components into software, services, and hardware, each playing a crucial role in the ecosystem. Software components, which include data management and analytics tools, are at the forefront, offering solutions that facilitate the collection, analysis, and visualization of large data sets. The software segment is driven by a demand for scalable solutions that can handle the increasing volume, velocity, and variety of data. As organizations strive to become more data-centric, there is a growing need for advanced analytics software that can provide actionable insights from complex data sets, leading to enhanced decision-making capabilities.

In the services segment, businesses are increasingly seeking consultation, implementation, and support services to effective

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. By leveraging advanced web scraping technology, this dataset delivers access to job market trends, salary insights, and in-demand skills. A valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence, this data helps businesses stay ahead in a dynamic job market.

Key Features:

✅ 214M+ Job Postings Tracked – Data sourced from 92 company websites worldwide. ✅ 7M+ Active Job Openings – Continuously updated to reflect real hiring demand. ✅ Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅ Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅ Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅ Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes in the Dataset:

General Information: - id (UUID) – Unique identifier for the job posting. - type (constant: "job_opening") – Object type. - title (string) – Job title. - description (string) – Full job description extracted from the job listing. - url (URL) – Direct link to the job posting. - first_seen_at (ISO 8601 date-time) – When the job was first detected. - last_seen_at (ISO 8601 date-time) – When the job was last observed. - last_processed_at (ISO 8601 date-time) – When the job data was last updated.

Job Metadata:

• contract_types (array of strings) – Employment type (full-time, part-time, contract).
• categories (array of strings) – Job industry categories (engineering, marketing, finance).
• seniority (string) – Seniority level (manager, non_manager).
• status (string) – Job status (open, closed).
• language (string) – Language of the job posting.

Location Data:

• location (string) – Full location details from the job description.
• location_data (array of objects) – Structured location details: -- city (string, nullable) – City where the job is located. -- state (string, nullable) – State or region. -- zip_code (string, nullable) – Postal/ZIP code. -- country (string, nullable) – Country. -- region (string, nullable) – Broader geographical region. -- continent (string, nullable) – Continent name. -- fuzzy_match (boolean) – Indicates if the location was inferred.

Salary Data:

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Salary currency (USD, EUR, GBP).
• salary_low_usd (float, nullable) – Minimum salary converted to USD.
• salary_high_usd (float, nullable) – Maximum salary converted to USD.
• salary_time_unit (string, nullable) – Time unit (year, month, hour).

Occupational Data (ONET):

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (Computer and Mathematical).
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (Python, JavaScript, AI).

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Job Openings Docs https://docs.predictleads.com/v3/guide/job_openings_dataset

County Business Patterns (CBP) is an annual series that provides economic data by industry at the U.S., State, County and Metropolitan Area levels. This series includes the number of establishments, employment during the week of March 12, first quarter payroll, and annual payroll. CBP provides statistics for businesses with paid employees for the U.S., Puerto Rico, and the Island Areas. Census Bureau staff identified a processing error that affects selected data from the 2014 County Business Patterns (CBP). As a result, we suppressed 2014 employment and payroll totals in the Health Care and Social Assistance sector (Sector 62) for the following geographies: U.S.; Michigan; Battle Creek, MI metro area; Calhoun County, MI; and the 3rd congressional district of Michigan. This processing error did not affect other sectors. While suppressed values can be derived by subtraction, we do not recommend using the derived values in any analyses. The Census Bureau plans to release revised statistics at a later date.

Techsalerator's Corporate Actions Dataset in South Korea offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 2445 companies traded on the Korea Stock Exchange (XKRX).

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Top 5 used data fields in the Corporate Actions Dataset for South Korea:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

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Top 5 corporate actions in South Korea:

Mergers and Acquisitions (M&A): South Korea's business landscape has seen various corporate actions related to mergers, acquisitions, and corporate restructuring, contributing to industry consolidation and market dynamics.

Technological Innovation: Corporate actions involving investments in technology, research and development, and innovation have been prominent in South Korea's efforts to maintain its position as a global technology leader.

Global Expansion: South Korean companies have undertaken corporate actions to expand their global footprint, including entering new markets, forming strategic partnerships, and exploring joint ventures.

Renewable Energy Initiatives: Corporate actions related to the renewable energy sector, including investments in solar, wind, and other green technologies, align with South Korea's push for sustainable development.

Financial Sector Developments: Corporate actions involving financial institutions, fintech advancements, and regulatory changes contribute to the modernization and competitiveness of South Korea's financial industry.

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Top 5 financial instruments with corporate action Data in South Korea

Seoul Stock Exchange (SSE) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Seoul Stock Exchange. This index would provide insights into the performance of the South Korean stock market.

Seoul Stock Exchange (SSE) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Seoul Stock Exchange, if foreign listings were present. This index would give an overview of foreign business involvement in South Korea.

KorMart: A South Korea-based online marketplace with operations in multiple regions. KorMart focuses on connecting buyers and sellers and contributing to the growth of e-commerce in South Korea.

FinanceKorea: A financial services provider in South Korea with a focus on promoting financial inclusion and access to banking services, particularly among underserved communities.

TechInnovate Korea: A company dedicated to advancing technological innovation in South Korea, focusing on research and development, and fostering a culture of innovation to support the country's technology sector.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for South Korea, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in South Korea?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

How complete is the Corporate Actions Dataset coverage in South Korea?

Techsalerator provides comprehensive coverage of Corporate Act...

This clean dataset is a refined version of our company datasets, consisting of 35M+ data records.

It’s an excellent data solution for companies with limited data engineering capabilities and those who want to reduce their time to value. You get filtered, cleaned, unified, and standardized B2B data. After cleaning, this data is also enriched by leveraging a carefully instructed large language model (LLM).

AI-powered data enrichment offers more accurate information in key data fields, such as company descriptions. It also produces over 20 additional data points that are very valuable to B2B businesses. Enhancing and highlighting the most important information in web data contributes to quicker time to value, making data processing much faster and easier.

For your convenience, you can choose from multiple data formats (Parquet, JSON, JSONL, or CSV) and select suitable delivery frequency (quarterly, monthly, or weekly).

Coresignal is a leading public business data provider in the web data sphere with an extensive focus on firmographic data and public employee profiles. More than 3B data records in different categories enable companies to build data-driven products and generate actionable insights. Coresignal is exceptional in terms of data freshness, with 890M+ records updated monthly for unprecedented accuracy and relevance.

Listing of all (active and inactive) businesses registered with the Office of Finance. An "active" business is defined as a registered business whose owner has not notified the Office of Finance of a cease of business operations. Update Interval: Monthly. NAICS Codes are from 2007 NAICS: https://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2007

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

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Big Data as a Service Market Size 2024-2028

The big data as a service market size is forecast to increase by USD 41.20 billion at a CAGR of 28.45% between 2023 and 2028.

The market is experiencing significant growth due to the increasing volume of data and the rising demand for advanced data insights. Machine learning algorithms and artificial intelligence are driving product quality and innovation in this sector. Hybrid cloud solutions are gaining popularity, offering the benefits of both private and public cloud platforms for optimal data storage and scalability. Industry standards for data privacy and security are increasingly important, as large amounts of data pose unique risks. The BDaaS market is expected to continue its expansion, providing valuable data insights to businesses across various industries.

What will be the Big Data as a Service Market Size During the Forecast Period?

Request Free Sample

Big Data as a Service (BDaaS) has emerged as a game-changer in the business world, enabling organizations to harness the power of big data without the need for extensive infrastructure and expertise. This service model offers various components such as data management, analytics, and visualization tools, enabling businesses to derive valuable insights from their data. BDaaS encompasses several key components that drive market growth. These include Business Intelligence (BI), Data Science, Data Quality, and Data Security. BI provides organizations with the ability to analyze data and gain insights to make informed decisions.



Data Science, on the other hand, focuses on extracting meaningful patterns and trends from large datasets using advanced algorithms. Data Quality is a critical component of BDaaS, ensuring that the data being analyzed is accurate, complete, and consistent. Data Security is another essential aspect, safeguarding sensitive data from cybersecurity threats and data breaches. Moreover, BDaaS offers various data pipelines, enabling seamless data integration and data lifecycle management. Network Analysis, Real-time Analytics, and Predictive Analytics are other essential components, providing businesses with actionable insights in real-time and enabling them to anticipate future trends. Data Mining, Machine Learning Algorithms, and Data Visualization Tools are other essential components of BDaaS.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Data analytics-as-a-Service
  Hadoop-as-a-service
  Data-as-a-service


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


Geography

  North America

    Canada
    US


  APAC

    China


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Type Insights

The data analytics-as-a-service segment is estimated to witness significant growth during the forecast period.

Big Data as a Service (BDaaS) is a significant market segment, highlighted by the availability of Hadoop-as-a-Service solutions. These offerings enable businesses to access essential datasets on-demand without the burden of expensive infrastructure. DAaaS solutions facilitate real-time data analysis, empowering organizations to make informed decisions. The DAaaS landscape is expanding rapidly as companies acknowledge its value in enhancing internal data. Integrating DAaaS with big data systems amplifies analytics capabilities, creating a vibrant market landscape. Organizations can leverage diverse datasets to gain a competitive edge, driving the growth of the global BDaaS market. In the context of digital transformation, cloud computing, IoT, and 5G technologies, BDaaS solutions offer optimal resource utilization.

However, regulatory scrutiny poses challenges, necessitating stringent data security measures. Retail and other industries stand to benefit significantly from BDaaS, particularly with distributed computing solutions. DAaaS adoption is a strategic investment for businesses seeking to capitalize on the power of external data for valuable insights.

Get a glance at the market report of share of various segments Request Free Sample

The Data analytics-as-a-Service segment was valued at USD 2.59 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 35% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions Request Free Sample

Big Data as a Service Market analysis, North America is experiencing signif

The Risk Management Agency (RMA) Summary of Business includes a variety of reports, data files, and an application that provide insurance experience for commodities grown and insured. This includes the most current information, some national reports, and the ability to create ad-hoc queries. Data for the past five years, which is updated each Monday, includes all of the business data that has been validated and accepted throughout the previous week with a cutoff every Friday. Data for the older years is static and no longer updated.

Crunchbase dataset to map your business ecosystem, make strategic decisions, and gather information on private and public companies. Common use cases include identifying investment opportunities, tracking company growth, and analyzing industry trends.

Use our Crunchbase Companies Information dataset to gain detailed insights into global startups and established companies across various industries. This dataset provides valuable company profiles, funding details, key executives, industry trends, and business performance, tailored for venture capitalists, market analysts, business development teams, and researchers.

By leveraging the Crunchbase Companies dataset, users can discover emerging startups, evaluate investment opportunities, track market growth, and perform competitive analysis. Whether you're seeking to enhance due diligence processes, identify new business prospects, or explore industry developments, this dataset empowers you to make data-driven decisions with confidence. Gain a deeper understanding of the business landscape and stay ahead in the competitive market by utilizing this essential dataset.

Dataset Features

Below is a breakdown of key dataset columns:
- name: The name of the company.
- url: Website or Crunchbase link for the company.
- id: Unique identifier for the company.
- cb_rank: Crunchbase ranking based on relevance and popularity.
- region: Geographic region where the company operates.
- about: Brief description of the company.
- industries: List of industries the company belongs to (e.g., photography, events, professional services).
- operating_status: Whether the company is active or inactive.
- company_type: Classification (e.g., for-profit, nonprofit).
- social_media_links: URLs to the company’s social media profiles.
- founded_date: Year or exact date when the company was founded.
- num_employees: Number of employees in the company.
- country_code: Country where the company is based.
- website: Official company website.
- contact_email: Contact email for the company.
- contact_phone: Contact phone number for the company.
- featured_list: Lists the company has been featured.
- full_description: Extended description of the company’s services or products.
- type: Type of organization (company, startup, etc.).
- uuid: Unique identifier for database tracking.
- active_tech_count: Number of technologies actively used by the company.
- builtwith_num_technologies_used: Number of technologies detected using BuiltWith.
- builtwith_tech: List of technologies used.
- ipo_status: Whether the company is public or private.
- similar_companies: URL of other companies similar to this one.
- image: Link to the company’s image or logo.
- monthly_visits: Estimated monthly web traffic.
- semrush_visits_latest_month: Website visits in the latest month according to SEMrush.
- semrush_last_updated: Last updated date for SEMrush traffic data.
- monthly_visits_growth: Change in web traffic over time.
- semrush_visits_mom_pct: Month-over-month percentage change in visits.
- num_contacts: Number of available contacts for the company.
- num_contacts_linkedin: Number of LinkedIn contacts.
- num_employee_profiles: Number of employee profiles available.
- total_active_products: Number of active products/services offered by the company.
- num_news: Number of news articles about the company.
- funding_rounds: Number of funding rounds the company has gone through.
- Bombora_last_updated: Bombora last updated date on website.
- num_investors: Number of investors associated with the company.
- legal_name: Official legal name of the company.
- num_event_appearances: Number of events the company has appeared in.
- num_acquisitions: Number of acquisitions made by the company.
- num_investments: Number of investments made by the company.
- num_advisor_positions: Number of advisor positions in the company.
- num_exits: Number of times the company has exited an investment.
- num_investments_lead: Number of times the company has led an investment round.
- num_sub_organizations: Number of sub-organizations under the company.
- num_alumni: Number of notable alumni from the company.
- Num_diversity_spotlight_investments: Number of diversity-focused investments.
- num_founder_alumni: Number of company founders who are alumni of a certain institution.
- num_funds: Number of investment funds the company has created.
- stock_symbol: Stock ticker symbol (if public).
- location: City and country where the company is headquartered.
- address: Full business address.
- contacts: List of business contacts.
- current_employees: Number of current employees.
- **semrush_loc

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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

The Business Dynamics Statistics (BDS) includes measures of establishment openings and closings, firm startups, job creation and destruction by firm size, age, and industrial sector, and several other statistics on business dynamics. The U.S. economy is comprised of over 6 million establishments with paid employees. The population of these businesses is constantly churning -- some businesses grow, others decline and yet others close. New businesses are constantly replenishing this pool. The BDS series provide annual statistics on gross job gains and losses for the entire economy and by industrial sector, state, and MSA. These data track changes in employment at the establishment level, and thus provide a picture of the dynamics underlying aggregate net employment growth.

There is a longstanding interest in the contribution of small businesses to job and productivity growth in the U.S. Some recent research suggests that it is business age rather than size that is the critical factor. The BDS permits exploring the respective contributions of both firm age and size.

BDS is based on data going back through 1976. This allows business dynamics to be tracked, measured and analyzed for young firms in their first critical years as well as for more mature firms including those that are in the process of reinventing themselves in an ever changing economic environment.

If you need help understanding the terms used, check out these definitions.

Data Dictionary

This data set provides geographic boundaries and basic information for Philadelphia’s 15 Business Improvement Districts (BID) as well the University City District and Sports Complex District. More information available here This data set may be helpful to property owners, property purchasers or title companies seeking to know if a property exists within a BID. Note that this dataset may include errors or outdated information. Therefore, it is strongly recommended that interested parties contact BID organizations directly with inquiries.

Techsalerator’s Business Funding Data for El Salvador

Techsalerator’s Business Funding Data for El Salvador provides a comprehensive and insightful collection of information essential for businesses, investors, and financial analysts. This dataset offers a deep dive into the funding activities of companies across various sectors in El Salvador, capturing and categorizing data related to their funding rounds, investment sources, and financial milestones.

If you need the full dataset, reach out to us at info@techsalerator.com or https://www.techsalerator.com/contact-us.

Techsalerator’s Business Funding Data for El Salvador

Techsalerator’s Business Funding Data for El Salvador delivers a thorough overview of crucial information for businesses, investors, and financial analysts. This dataset provides an in-depth examination of funding activities across various sectors in El Salvador, detailing data related to funding rounds, investment sources, and key financial milestones.

Top 5 Key Data Fields

Company Name: Identifies the company receiving funding. This information helps investors identify potential opportunities and allows analysts to monitor funding trends within specific industries.

Funding Amount: Shows the total amount of funding a company has received. Understanding these amounts reveals insights into the financial health and growth potential of businesses and the scale of investment activities.

Funding Round: Indicates the stage of funding, such as seed, Series A, Series B, or later stages. This helps investors assess a business’s maturity and growth trajectory.

Investor Name: Provides details about the investors or investment firms involved. Knowing the investors helps gauge the credibility of the funding source and their strategic interests.

Investment Date: Records when the funding was completed. The timing of investments can reflect market trends, investor confidence, and potential impacts on a company’s future.

Top 5 Funding Trends in El Salvador

Infrastructure Development: Investments are being made in infrastructure projects such as roads, bridges, and energy initiatives. These projects are crucial for the country’s economic development and stability.

Technology and Innovation: There is a growing focus on technology and innovation, with funding directed towards tech startups and digital transformation projects to drive economic growth and modernization.

Agriculture and Agritech: Agriculture remains a vital sector, and funding is aimed at enhancing agricultural practices through technology and sustainable solutions to improve productivity and efficiency.

Renewable Energy: Significant investments are being directed towards renewable energy projects, including solar and wind energy, to promote sustainability and reduce dependency on non-renewable resources.

Education and Workforce Development: Funding is being allocated to educational programs and vocational training initiatives to improve literacy, enhance skills, and support workforce development.

Top 5 Companies with Notable Funding Data in El Salvador

Tigo El Salvador: A leading telecommunications provider, Tigo has received substantial funding to expand its network, enhance digital services, and support technological advancements.

Grupo Calleja: This retail giant has attracted investment to support expansion efforts, improve operational efficiency, and invest in digital transformation.

Banco Agrícola: The financial institution has garnered significant funding to enhance its banking services, expand its reach, and promote financial inclusion.

Industrias La Constancia: Known for its manufacturing capabilities, this company has secured funding to support growth, innovation, and expansion of its product offerings.

Fundación Salvador del Mundo: A prominent non-profit organization, it has received funding to support community development projects, education programs, and healthcare initiatives.

Accessing Techsalerator’s Business Funding Data

To obtain Techsalerator’s Business Funding Data for El Salvador, contact info@techsalerator.com with your specific needs. Techsalerator will provide a customized quote based on the required data fields and records, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

Company Name Funding Amount Funding Round Investor Name Investment Date Funding Type (Equity, Debt, Grants, etc.) Sector Focus Deal Structure Investment Stage Contact Information For detailed insights into funding activities and financial trends in El Salvador, Techsalerator’s dataset is an invaluable resource for investors, business analysts, and financial professionals seeking informed, strategic decisions.