Mental Health in Tech Survey

Understanding Employee Mental Health Needs in the Tech Industry

By Stephen Myers [source]

About this dataset

This dataset contains survey responses from individuals in the tech industry about their mental health, including questions about treatment, workplace resources, and attitudes towards discussing mental health in the workplace. Mental health is an issue that affects all people of all ages, genders and walks of life. The prevalence of these issues within the tech industry–one that places hard demands on those who work in it–is no exception. By analyzing this dataset, we can better understand how prevalent mental health issues are among those who work in the tech sector.–and what kinds of resources they rely upon to find help–so that more can be done to create a healthier working environment for all.

This dataset tracks key measures such as age, gender and country to determine overall prevalence, along with responses surrounding employee access to care options; whether mental health or physical illness are being taken as seriously by employers; whether or not anonymity is protected with regards to seeking help; and how coworkers may perceive those struggling with mental illness issues such as depression or anxiety. With an ever-evolving landscape due to new technology advancing faster than ever before – these statistics have never been more important for us to analyze if we hope remain true promoters of a healthy world inside and outside our office walls

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How to use the dataset

In this dataset you will find data on age, gender, country, and state of survey respondents in addition to numerous questions that assess an individual's mental state including: self-employment status, family history of mental illness, treatment status and access or lack thereof; how their mental health condition affects their work; number of employees at the company they work for; remote work status; tech company status; benefit information from employers such as mental health benefits and wellness program availability; anonymity protection if seeking treatment resources for substance abuse or mental health issues ; ease (or difficulty) for medical leave for a mental health condition ; whether discussing physical or medical matters with employers have negative consequences. You will also find comments from survey participants.

To use this dataset effectively: - Clean the data by removing invalid responses/duplicates/missing values - you can do this with basic Pandas commands like .dropna() , .drop_duplicates(), .replace(). - Utilize descriptive statistics such as mean and median to draw general conclusions about patterns of responses - you can do this with Pandas tools such as .groupby() and .describe(). - Run various types analyses such as mean comparisons on different kinds of variables(age vs gender), correlations between different features etc using appropriate statistical methods - use commands like Statsmodels' OLS models (.smf) , calculate z-scores , run hypothesis tests etc depending on what analysis is needed. Make sure you are aware any underlying assumptions your analysis requires beforehand !
- Visualize your results with plotting libraries like Matplotlib/Seaborn to easily interpret these findings! Use boxplots/histograms/heatmaps where appropriate depending on your question !

Research Ideas

• Using the results of this survey, you could develop targeted outreach campaigns directed at underrepresented groups that answer “No” to questions about their employers providing resources for mental health or discussing it as part of wellness programs.
• Analyzing the employee characteristics (e.g., age and gender) of those who reported negative consequences from discussing their mental health in the workplace could inform employer policies to support individuals with mental health conditions and reduce stigma and discrimination in the workplace.
• Correlating responses to questions about remote work, leave policies, and anonymity with whether or not individuals have sought treatment for a mental health condition may provide insight into which types of workplace resources are most beneficial for supporting employees dealing with these issues

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redi...

This dataset provides an overview of emerging IT job roles, their domains, and the projected growth rates by 2030. It is designed to help identify high-growth areas in the tech industry, providing insights for career planning, workforce development, and market analysis.

Dataset Features

• IT_JOB_ID: Unique identifier for each IT job entry.
• Domain: The technology domain or specialization (e.g., cybersecurity, cloud computing).
• Job Title: Specific job titles within the domain (e.g., Engineer, Developer, Manager).
• Projected Growth by 2030: Predicted growth percentage for the job role by the year 2030.

Distribution

• Data Volume: 1048575 rows and 4 columns in the provided sample.
• Format: Tabular dataset with structured categorical and numerical data.

Usage

This dataset is ideal for a variety of applications:

• Career Planning: Identifying high-demand IT jobs for students and professionals.
• Workforce Development: Assisting policymakers and organizations in reskilling programs.
• Market Analysis: Evaluating technology trends for industry stakeholders.
• Predictive Modeling: Training models to predict future trends in job growth across domains.

Coverage

• Geographic Coverage: Global.
• Time Range: Projections valid through 2030.
• Demographics: Focuses on IT job roles and their growth potential across various tech domains.

License

CC0

Who Can Use It

• Data Scientists: To create predictive models for future job trends.
• Researchers: To analyze growth trends in emerging IT fields.
• Businesses: To strategize hiring, workforce planning, and investment in tech domains.
• Educators and Trainers: To align course offerings with future demand.

United States US: GDP: % of Manufacturing: Medium and High Tech Industry data was reported at 41.166 % in 2015. This stayed constant from the previous number of 41.166 % for 2014. United States US: GDP: % of Manufacturing: Medium and High Tech Industry data is updated yearly, averaging 49.199 % from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 51.786 % in 1998 and a record low of 38.398 % in 1996. United States US: GDP: % of Manufacturing: Medium and High Tech Industry data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Gross Domestic Product: Share of GDP. The proportion of medium and high-tech industry value added in total value added of manufacturing; ; United Nations Industrial Development Organization (UNIDO), Competitive Industrial Performance (CIP) database; ;

The tech industry had a rough start to 2024. Technology companies worldwide saw a significant reduction in their workforce in the first quarter of 2024, with over 57 thousand employees being laid off. By the second quarter, layoffs impacted more than 43 thousand tech employees. In the final quarter of the year around 12 thousand employees were laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of 167.6 thousand employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of 263 thousand laid off employees in the global tech sector by trhe end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

According to Cognitive Market Research, the global GPU Database market size will be USD 455 million in 2024 and will expand at a compound annual growth rate (CAGR) of 20.7% from 2024 to 2031. Market Dynamics of GPU Database Market Key Drivers for GPU Database Market Growing Demand for High-Performance Computing in Various Data-Intensive Industries- One of the main reasons the GPU Database market is growing demand for high-performance computing (HPC) across various data-intensive industries. These industries, including finance, healthcare, and telecommunications, require rapid data processing and real-time analytics, which GPU databases excel at providing. Unlike traditional CPU databases, GPU databases leverage the parallel processing power of GPUs to handle complex queries and large datasets more efficiently. This capability is crucial for applications such as machine learning, artificial intelligence, and big data analytics. The expansion of data and the increasing need for speed and scalability in processing are pushing enterprises to adopt GPU databases. Consequently, the market is poised for robust growth as organizations continue to seek solutions that offer enhanced performance, reduced latency, and greater computational power to meet their evolving data management needs. The increasing demand for gaining insights from large volumes of data generated across verticals to drive the GPU Database market's expansion in the years ahead. Key Restraints for GPU Database Market Lack of efficient training professionals poses a serious threat to the GPU Database industry. The market also faces significant difficulties related to insufficient security options. Introduction of the GPU Database Market The GPU database market is experiencing rapid growth due to the increasing demand for high-performance data processing and analytics. GPUs, or Graphics Processing Units, excel in parallel processing, making them ideal for handling large-scale, complex data sets with unprecedented speed and efficiency. This market is driven by the proliferation of big data, advancements in AI and machine learning, and the need for real-time analytics across industries such as finance, healthcare, and retail. Companies are increasingly adopting GPU-accelerated databases to enhance data visualization, predictive analytics, and computational workloads. Key players in this market include established tech giants and specialized startups, all contributing to a competitive landscape marked by innovation and strategic partnerships. As organizations continue to seek faster and more efficient ways to harness their data, the GPU database market is poised for substantial growth, reshaping the future of data management and analytics.< /p>

High-Tech Companies on NASDAQ

Market Capitalization and Performance Metrics

By [source]

About this dataset

This dataset offers an insightful look into the performance of high-tech companies listed on the NASDAQ exchange in the United States. With information pertaining to over 8,000 companies in the electronics, computers, telecommunications, and biotechnology sectors, this is an incredibly useful source of insight for researchers, traders, investors and data scientists interested in acquiring information about these firms.

The dataset includes detailed variables such as stock symbols and names to provide quick identification of individual companies along with pricing changes and percentages from the previous day’s value as well as sector and industry breakdowns for comprehensive analysis. Other metrics like market capitalization values help to assess a firm’s relative size compared to competitors while share volume data can give a glimpse into how actively traded each company is. Additionally provided numbers include earnings per share breakdowns to gauge profits along with dividend pay date symbols for yield calculation purposes as well as beta values that further inform risk levels associated with investing in particular firms within this high-tech sector. Finally this dataset also collects any potential errors found amongst such extensive scrapes of company performance data giving users valuable reassurance no sensitive areas are missed when assessing various firms on an individual basis or all together as part of an overarching system

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How to use the dataset

This dataset is invaluable for researchers, traders, investors and data scientists who want to obtain the latest information about high-tech companies listed on the NASDAQ exchange in the United States. It contains data on more than 8,000 companies from a wide range of sectors such as electronics, computers, telecommunications, biotechnology and many more. In this guide we will learn how to use this dataset effectively.

Basics: The basics of working with this dataset include understanding various columns like symbol, name, price,pricing_changes, pricing_percentage_changes,sector,industry,market_cap,share_volume,earnings_per_share. Each column is further described below: - Symbol: This column gives you the stock symbol of the company. (String) - Name: This column gives you the name of the company. (String)
- Price: The current price of each stock given by symbol is mentioned here.(Float) - Pricing Changes: This represents change in stock price from previous day.(Float) - Pricing Percentage Changes :This provides percentage change in stock prices from previous day.(Float) - Sector : It give information about sector in which company belongs .(String). - Industry : Describe industry in which company lies.(string). - Market Capitalization : Give market capitalization .(String). - Share Volume : It refers to number share traded last 24 hrs.(Integer). - Earnings Per Share : It refer to earnings per share per Stock yearly divided by Dividend Yield ,Symbol Yield and Beta .It also involves Errors related with Data Set so errors specified here proviedes details regarding same if any errors occured while collecting data set or manipulation on it.. (float/string )

Advanced Use Cases: Now that we understand what each individual feature stands for it's time to delve deeper into optimizing returns using this data set as basis for our decision making processes such as selecting right portfolio formation techniques or selecting stocks wisely contrarian investment style etc. We can do a comparison using multiple factors like Current Price followed by Price Change percentage or Earnings feedback loop which would help us identify Potentially Undervalued investments both Short Term & Long Term ones at same time and We could dive into analysis showing Relationship between Price & Volumne across Sectors and

Research Ideas

• Analyzing stock trends - The dataset enables users to make informed decisions by tracking and analyzing changes in indicators such as price, sector, industry or market capitalization trends over time.
• Exploring correlations between different factors - By exploring the correlation between different factors such as pricing changes, earning per share or beta etc., it enables us to get a better understanding of how these elements influence each other and what implications it may have on our investments

Acknowledgements

&g...

The size of the US Data Center Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 6.00% during the forecast period.A data center is a facility that keeps computer systems and networking equipment housed, processing, and transmitting data. It represents the infrastructure on which organizations carry out their IT operations and host websites, email servers, and database servers. Data centers, therefore, are imperative to any size business: small start-ups or large enterprise since they enable digital transformation, thus making business applications available.The US data center industry is one of the largest and most developed in the world. The country boasts robust digital infrastructure, abundant energy resources, and a highly skilled workforce, making it an attractive destination for data center operators. Some of the drivers of the US data center market are the growing trend of cloud computing, internet of things (IoT), and high-performance computing requirements.Top-of-the-line technology companies along with cloud service providers set up major data center footprints in the US, mostly in key regions such as Silicon Valley and Northern Virginia, Dallas, for example. These data centers support applications such as e-commerce-a manner of accessing streaming services-whose development depends on its artificial intelligence financial service type. As demand increases concerning data center capacity, therefore, the US data centre industry will continue to prosper as the world's hub for reliable and scalable solutions. Recent developments include: February 2023: The expansion of Souther Telecom to its data center in Atlanta, Georgia, at 345 Courtland Street, was announced by H5 Data Centers, a colocation and wholesale data center operator. One of the top communication service providers in the southeast is Southern Telecom. Customers in Alabama, Georgia, Florida, and Mississippi will receive better service due to the expansion of this low-latency fiber optic network.December 2022: DigitalBridge Group, Inc. and IFM Investors announced completing their previously announced transaction in which funds affiliated with the investment management platform of DigitalBridge and an affiliate of IFM Investors acquired all outstanding common shares of Switch, Inc. for USD approximately USD 11 billion, including the repayment of outstanding debt.October 2022: Three additional data centers in Charlotte, Nashville, and Louisville have been made available to Flexential's cloud customers, according to the supplier of data center colocation, cloud computing, and connectivity. By the end of the year, clients will have access to more than 220MW of hybrid IT capacity spread across 40 data centers in 19 markets, which is well aligned with Flexential's 2022 ambition to add 33MW of new, sustainable data center development projects.. Key drivers for this market are: , High Mobile penetration, Low Tariff, and Mature Regulatory Authority; Successful Privatization and Liberalization Initiatives. Potential restraints include: , Difficulties in Customization According to Business Needs. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.

The US data processing and hosting services industry is navigating a dynamic environment marked by rising demands and revolutionary trends. As digitalization accelerates, data centers have evolved from simple infrastructure to essential strategic assets. These hubs now power services ranging from cloud computing to advanced data analytics. In 2025, the data processing and hosting service market includes giants like Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). Industry revenue currently sits at $383.8 billion, growing robustly at a CAGR of 9.2% over the past five years, including a 6.2% surge in 2025 alone. Alongside leading tech firms, smaller specialized providers cater to sectors like healthcare, financial services and government agencies with precision-placed data storage solutions. Emerging trends significantly influence the evolution of the US data processing and hosting services industry. Prominent among these is edge computing, a decentralized approach that locates data centers closer to end-user devices. Along with AI and modern data centers, these innovations aim to reduce latency and enhance application performance by minimizing resource usage in data transmission, thereby promoting broader adoption of cloud computing. Despite this transformative growth, the US data processing and hosting services industry faces significant hurdles, including a skill gap, escalating energy costs and escalating cybersecurity threats. This scarcity has heightened the focus on software automation, leading many facilities to implement AI solutions. Though offshoring trends lead to lost business for many participants, this activity is limited and the industry still benefits from strong demand, leading to rising profit. The industry is projected to grow at a CAGR of 2.4% to $431.4 billion by 2030. The future holds a mix of challenges and opportunities for the industry. Strategic investments in human capital and advanced technologies will distinguish industry leaders from laggards. Compliance with evolving data sovereignty and privacy regulations will determine local market competitiveness. Continuous innovation is expected to drive this progress, solidifying data centers' roles as pivotal components shaping the digital landscape ahead.

Success.ai presents our Tech Install Data offering, a comprehensive dataset drawn from 28 million verified company profiles worldwide. Our meticulously curated Tech Install Data is designed to empower your sales and marketing strategies by providing in-depth insights into the technology stacks used by companies across various industries. Whether you're targeting small businesses or large enterprises, our data encompasses a diverse range of sectors, ensuring you have the necessary tools to refine your outreach and engagement efforts.

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Data Annotation Software Market Outlook

The global data annotation software market size was valued at USD 1.3 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 19.1% during the forecast period. The growth of this market is primarily driven by the increasing adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies across various industries, which necessitates the need for high-quality training data.

One of the key growth factors of the data annotation software market is the exponential rise in the volume of unstructured data. With the proliferation of digital technologies, organizations are generating vast amounts of data daily. This data needs to be labeled and annotated to be useful for AI and ML applications. Furthermore, the advancements in AI algorithms demand large datasets for training purposes, thereby significantly boosting the demand for data annotation tools. Another contributing factor is the growing trend of data-driven decision-making processes within enterprises. Companies are increasingly relying on data to enhance operational efficiency, customer experience, and strategic planning, which in turn drives the need for accurate data annotation.

Another major growth driver is the increasing use of data annotation in autonomous vehicles. The automotive industry, particularly self-driving cars, heavily relies on annotated data to train AI models for object detection, navigation, and decision-making. This has led to a surge in demand for specialized data annotation software tailored for automotive applications. Additionally, the healthcare sector is also witnessing substantial growth in the adoption of data annotation tools. From medical imaging to electronic health records, annotated data is crucial for training AI models that assist in diagnostics, treatment planning, and patient management. Innovations in healthcare AI are further propelling the demand for data annotation solutions.

Furthermore, the increasing investment in AI technology by various governments and private organizations is acting as a significant growth catalyst. Governments are recognizing the potential of AI to drive economic growth and are therefore investing in AI research and development, which includes the development of robust data annotation tools. Private investments, particularly venture capital funding, are also fueling the market growth. Startups specializing in data annotation software are attracting significant investments, further accelerating advancements in this domain. The combination of public and private sector investments is expected to create abundant growth opportunities in the coming years.

Regional analysis reveals that North America holds the largest share of the data annotation software market, followed by Europe and the Asia Pacific. The dominance of North America can be attributed to the early adoption of advanced technologies, the presence of major tech companies, and substantial investment in AI and machine learning research. Europe follows closely due to its strong focus on innovation, research, and development. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate, driven by rapid digitalization, increasing investments in AI, and the growing presence of tech startups. Each region presents unique growth opportunities influenced by local market dynamics and technological advancements.

In this evolving landscape, Manual Data Annotation Tools play a crucial role in ensuring the accuracy and quality of labeled data. These tools are indispensable for projects where nuanced human judgment is required to interpret complex data sets. Unlike automated tools, manual annotation allows for a more detailed and context-aware approach, which is particularly beneficial in fields such as medical diagnostics and legal document analysis. As AI models become more sophisticated, the need for precise and contextually relevant data annotation becomes even more critical. Manual Data Annotation Tools provide the flexibility and adaptability needed to handle diverse data types and complex annotation tasks, ensuring that AI models are trained on high-quality data.

Component Analysis

The data annotation software market can be segmented into software and services. The software segment primarily includes platforms and tools used for annotating data, while the services segment encompasses managed services, consulting, and support services. The

Time Series Databases Software Market Outlook

The global time series databases software market is experiencing significant expansion, with market size estimated at approximately USD 1.5 billion in 2023 and projected to reach USD 4.2 billion by 2032, registering a robust compound annual growth rate (CAGR) of 12.5% during the forecast period. This growth is driven by the increasing need for real-time analytics and the management of time-stamped data across various industry verticals. The proliferation of IoT devices and the growing importance of time-stamped data in decision-making processes are key factors contributing to this upward trajectory. As businesses seek to leverage these capabilities, the demand for efficient time series databases continues to rise.

One of the major growth factors driving the time series databases software market is the burgeoning IoT ecosystem. With millions of devices generating vast amounts of data every second, there is an unprecedented demand for systems that can efficiently process, store, and analyze time-stamped data. IoT applications, such as smart cities, connected vehicles, and industrial automation, rely heavily on real-time data insights to optimize operations and improve outcomes. Consequently, organizations are investing in advanced time series databases to harness the potential of IoT-driven data streams effectively. This trend is expected to accelerate as IoT adoption continues to grow across various sectors.

Another pivotal growth factor is the increasing emphasis on predictive analytics and machine learning across industries. Time series databases play a crucial role in these areas by enabling businesses to analyze historical data patterns and predict future trends. In sectors like finance, healthcare, and energy, the ability to forecast future events accurately can lead to improved decision-making and strategic planning. For instance, financial institutions utilize time series databases for stock market analysis, while healthcare providers use them for patient monitoring and prognosis. This growing reliance on predictive analytics is expected to fuel the demand for time series database solutions in the coming years.

The need for high-performance and scalable data architectures is also contributing to market growth. Traditional relational databases are often ill-equipped to handle the unique challenges posed by time-stamped data, such as high write and query loads and the need for efficient compression and data retention strategies. Time series databases are specifically designed to address these challenges, offering features such as efficient storage, fast retrieval, and seamless integration with analytics tools. As organizations grapple with increasingly large datasets, the adoption of time series databases is anticipated to rise, driven by the demand for scalable and cost-effective solutions.

Regionally, North America holds a significant share of the time series databases software market, driven by the presence of numerous tech-savvy industries and a strong focus on digital transformation. The Asia Pacific region is expected to witness the highest growth rate, fueled by rapid industrialization, the expansion of smart city initiatives, and increasing investments in IoT infrastructure. Europe also presents substantial growth prospects due to the growing adoption of advanced analytics solutions across various sectors. Meanwhile, Latin America and the Middle East & Africa are gradually embracing these technologies, albeit at a slower pace, as infrastructure and digital initiatives continue to develop. Each region's growth trajectory is influenced by local economic conditions, technology adoption rates, and regulatory frameworks.

Deployment Type Analysis

The analysis of deployment types in the time series databases software market reveals a dynamic landscape shaped by varying organizational needs and technological preferences. On-premises deployment remains a viable option for many businesses, particularly those in regulated industries where data security and control are paramount. Organizations in sectors such as finance and healthcare often prefer on-premises solutions to maintain stringent control over their data environments. These deployments offer the advantage of complete data custody and the flexibility to tailor configurations to specific organizational requirements. However, these benefits come with the trade-offs of higher upfront costs and the need for in-house technical expertise to manage and maintain the infrastructure effectively.

On the other hand, the cloud-based deployment model is witnessing

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Relational Database Software Market Outlook

In 2023, the global market size for relational database software is valued at approximately $61.5 billion, with an anticipated growth to $113.9 billion by 2032, reflecting a robust CAGR of 7.1%. This impressive growth is mainly driven by the increasing volume of data generated across industries and the need for efficient data management solutions. The expanding application of relational database software in various sectors such as BFSI, healthcare, and telecommunications is also a significant contributor to market growth. Furthermore, the transition from legacy systems to modern, scalable database solutions is propelling this market forward.

The proliferation of data from diverse sources, including IoT devices, social media, and enterprise applications, is one of the primary growth factors for the relational database software market. Organizations are increasingly adopting advanced database management systems to handle large volumes of structured and unstructured data efficiently. This necessity aligns with the growing trend of digital transformation, where data plays a crucial role in driving business insights and decision-making processes. Additionally, the rise of big data analytics and artificial intelligence necessitates robust database solutions that can manage and process vast amounts of data in real-time.

Another significant growth driver for this market is the increasing reliance on cloud-based solutions. Cloud computing offers scalable, flexible, and cost-effective database management options, making it an attractive choice for enterprises of all sizes. The adoption of cloud-based relational database software is accelerating as it reduces the need for physical infrastructure, lowers maintenance costs, and provides seamless access to data from any location. Moreover, cloud providers are continually enhancing their offerings with advanced features such as automated backups, disaster recovery, and high availability, further boosting the market demand.

The integration of relational database software with emerging technologies such as blockchain, machine learning, and internet of things (IoT) is also fueling market growth. These integrations enable enhanced data security, improved data analytics capabilities, and efficient data management, which are crucial for modern enterprises. For instance, blockchain technology can provide a secure and transparent way of handling transactions and records within a relational database, while machine learning algorithms can optimize queries and database performance. As these technologies evolve, their synergy with relational database software is expected to create new opportunities and drive further market expansion.

In addition to the growing significance of relational databases, Object-Oriented Databases Software is gaining traction as businesses seek more flexible and efficient ways to manage complex data structures. Unlike traditional relational databases that rely on tables and rows, object-oriented databases store data in objects, similar to how data is organized in object-oriented programming. This approach allows for a more intuitive mapping of real-world entities and relationships, making it particularly beneficial for applications that require complex data representations, such as computer-aided design (CAD), multimedia systems, and telecommunications. As industries continue to evolve and demand more sophisticated data management solutions, the adoption of object-oriented databases is expected to rise, complementing the existing relational database landscape.

Region-wise, North America holds a significant share of the relational database software market, driven by the presence of leading technology companies, high adoption of advanced IT solutions, and substantial investments in research and development. Europe follows closely, with strong growth observed in cloud-based solutions and regulatory frameworks favoring data security and privacy. The Asia Pacific region is projected to exhibit the highest growth rate, attributed to the rapid digitalization of economies, increasing IT expenditures, and expanding tech-savvy population. Conversely, Latin America and the Middle East & Africa regions are also experiencing growth, albeit at a slower pace, due to growing awareness and gradual adoption of database management solutions.

Deployment Mode Analysis

The deployment mode segment of the relational database software market can be bifur

The Forager.ai - US Company Dataset is a leading source of firmographic data, backed by advanced AI and offering the highest refresh rate in the industry.

| Volume and Stats |

• Over 16M total US company records, the highest volume in the industry today.
• Every company record refreshed twice a month, offering an unparalleled update frequency.
• Delivery is made every hour, ensuring you have the latest data at your fingertips.
• Each record is the result of an advanced AI-driven process, ensuring high-quality, accurate data.

| Use Cases |

Sales Platforms, ABM and Intent Data Platforms, Identity Platforms, Data Vendors:

• Map the comprehensive universe of companies globally, and acquire the latest firmographic data for all those records.
• Track company size and measure growth to determine sales propensity or to create an internal growth score.
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B2B Tech Companies:

• Enrich leads that sign-up through the Company Search API (available separately).
• Identify and map every company that fits your core personas and ICP.
• Build audiences to target, using key fields like location, company size, industry, and description.

Venture Capital and Private Equity:

• Discover new investment opportunities using company descriptions and industry-level data.
• Review the growth of private companies and benchmark their strength against competitors.
• Create high-level views of companies competing in popular verticals for investment.

| Delivery Options |

• Flat files via S3 or GCP
• PostgreSQL Shared Database
• PostgreSQL Managed Database
• API
• Other options available upon request, depending on the scale required

Our dataset provides a unique blend of volume, freshness, and detail that is perfect for Sales Platforms, B2B Tech, VCs & PE firms, Marketing Automation, ABM & Intent. It stands as a cornerstone in our broader data offering, ensuring you have the information you need to drive decision-making and growth.

Tags: Company Data, Company Profiles, Employee Data, Firmographic Data, AI-Driven Data, High Refresh Rate, Company Classification, Private Market Intelligence, Workforce Intelligence, Public Companies.

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FAQs

1. Can licensed data be resold or redistributed? Answer: No, The customer shall not, directly or indirectly, sell, distribute, license, or otherwise make available the licensed data to any third party that intends to resell, sublicense, or redistribute the data. The Customer must take reasonable steps to ensure that any recipient of the licensed data is using it for internal purposes only and not for resale or redistribution. Any breach of this provision shall be considered a material breach of this Order Form and may result in the immediate termination of the Customer's rights under this agreement, as well as any applicable remedies available under law.

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3. What happens if the customer misuses the data? Answer: The data can be used without limits for a period of one year or multiple years (depending on the contract tenure); however, Thomson Data actively monitors its usage. If any unusual activity is detected, Thomson Data reserves the right to terminate the account.

4. How frequently is the data updated? Answer: The data is updated on a quarterly basis and fresh records added on a monthly basis

5. What is the accuracy rate of the data? Answer: Customers can expect 90% accuracy for all data points, with email accuracy ranging between 85% and 90%. Cell phone data accuracy is around 80%.

6. What types of information are included in the data? Answer: Thomson Data provides over 70+ data points, including contact details (name, job title, LinkedIn profile, cell number, email address, education, certifications, work experience, etc.), company information, department/team sizes, SIC and NAICS codes, industry classification, technographic detai...

Release Date: 2021-03-11.The Census Bureau has reviewed this data product for unauthorized disclosure of confidential information and has approved the disclosure avoidance practices applied (Approval ID: CBDRB-FY20-424)...Release Schedule:.Data in this file come from estimates of technology use of employer firms from the 2019 Annual Business Survey (ABS) collection. Data are also obtained from administrative records, the 2017 Economic Census, and other economic surveys...Note: The collection year is the year in which the data are collected. A reference year is the year that is referenced in the questions on the survey and in which the statistics are tabulated. For example, the 2019 ABS collection year produces statistics for the 2018 reference year. The "Year" column in the table is the reference year...For more information about ABS planned data product releases, see Tentative ABS Schedule...Key Table Information:.This is one of twenty tables in the 2019 ABS technology series to provide detailed technology use and production statistics with select economic and demographic characteristics of businesses (TCB) for U.S. employer firms that reported the sex, ethnicity, race, and veteran status for up to four persons owning the largest percentage(s) of the business. The data include U.S. firms with paid employees operating during the reference year with receipts of $1,000 or more, which are classified in the North American Industry Classification System (NAICS), Sectors 11 through 99, except for NAICS 111, 112, 482, 491, 521, 525, 813, 814, and 92 which are not covered. Employer firms with more than one domestic establishment are counted in each geographic area and industry in which they operate, but only once in the U.S. and state totals for all sectors. Firms are asked to report their employees as of the March 12 pay period...Data Items and Other Identifying Records:.Data include estimates on:.Number of employer firms (firms with paid employees). Percent of employer firms (%). Sales and receipts of employer firms (reported in $1,000s of dollars). Percent of sales and receipts of employer firms (%). Number of employees (during the March 12 pay period). Percent of employees (%). Annual payroll (reported in $1,000s of dollars). Percent of annual payroll (%)...Data Notes:.. For each technology group, this table shows estimates from only the subsample of firms that reported low use, moderate use, or high use for the corresponding technology tabulated in this table, AB1800TCB01A, for reference year 2018. For example, the firms that reported either low, moderate, or high use of Artificial Intelligence technology from table AB1800TCB01A are the subsample of firms that also responded to and are tabulated for the Artificial Intelligence estimates in this table.. Percentage statistics are based on the share of firms that reported data in each technology group. The total number of firms that reported data for each technology are captured in the Total Reporting counts. For example, the total number of firms that selected any response for the Motivation for Artificial Intelligence Technology Use on the 2019 ABS questionnaire, represent the number of firms in Artificial Intelligence: Total reporting statistic....Technology Characteristics:.The ABS was designed to include select questions about technology, innovation, and research and development from multiple reference periods and to incorporate new content each survey year based on topics of relevance...Estimates are derived from firms reporting the characteristics tabulated in this dataset. Percentages are always based on total reporting (defined above) and are not recalculated when the dataset is resorted...Industry and Geography Coverage:.The data are shown for the total for all sectors (00) and the 2-digit NAICS code levels for:..United States. States and the District of Columbia...Footnotes:.Footnote 660 - Agriculture, forestry, fishing and hunting (Sector 11): Crop and Animal Production (NAICS 111 and 112) are out of scope..Footnote 661 - Transportation and warehousing (Sector 48-49): Rail Transportation (NAICS 482) and the Postal Service (NAICS 491) are out of scope..Footnote 662 - Finance and insurance (Sector 52): Monetary Authorities-Central Banks (NAICS 521) and Funds, Trusts, and Other Financial Vehicles (NAICS 525) are out of scope..Footnote 663 - Other services, except public administration (Sector 81): Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813) and Private Households (NAICS 814) are out of scope...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/abs/data/2018/AB1800TCB02A.zip...API Information:.Annual Business Survey data are housed in the Census Bureau API. For more information, see https://api.census.gov/data/2018/abstcb.html...Methodology:.To maintain confidentiality, the Census Bureau suppresses data to protect the identity of any business or indiv...

China GDP: % of Manufacturing: Medium and High Tech Industry data was reported at 41.451 % in 2019. This stayed constant from the previous number of 41.451 % for 2018. China GDP: % of Manufacturing: Medium and High Tech Industry data is updated yearly, averaging 41.451 % from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 43.881 % in 2002 and a record low of 35.226 % in 1993. China GDP: % of Manufacturing: Medium and High Tech Industry data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Gross Domestic Product: Share of GDP. The proportion of medium and high-tech industry value added in total value added of manufacturing; ; United Nations Industrial Development Organization (UNIDO), Competitive Industrial Performance (CIP) database; ;

Techsalerator's News Events Data for Cambodia: A Comprehensive Overview

Techsalerator's News Events Data for Cambodia provides an essential resource for businesses, researchers, and media organizations. This dataset compiles information on key news events across Cambodia, drawing from a diverse array of media sources, including news outlets, online publications, and social media platforms. It offers valuable insights for those interested in tracking trends, analyzing public sentiment, or monitoring industry-specific developments.

Key Data Fields - Event Date: Records the precise date of the news event, crucial for trend analysis over time or for businesses reacting to market changes. - Event Title: A concise headline describing the event, enabling users to quickly gauge and categorize news content based on relevance. - Source: Identifies the news outlet or platform reporting the event, helping users track credible sources and evaluate the event's reach and influence. - Location: Provides geographic details about where the event occurred within Cambodia, valuable for regional analysis or targeted marketing. - Event Description: Offers a detailed summary of the event, including key developments, participants, and potential impact, aiding in understanding the context and implications.

Top 5 News Categories in Cambodia - Politics: Covers major news on government decisions, political movements, elections, and policy changes affecting the national landscape. - Economy: Focuses on Cambodia’s economic indicators, trade activities, inflation rates, and corporate news impacting business and finance sectors. - Social Issues: Highlights news on public health, education, social protests, and other societal concerns driving public discourse. - Sports: Features events in popular sports, such as football and martial arts, attracting considerable attention and engagement. - Technology and Innovation: Reports on tech advancements, startups, and innovations in Cambodia’s evolving tech sector.

Top 5 News Sources in Cambodia - The Phnom Penh Post: One of Cambodia's leading English-language newspapers, offering comprehensive coverage of politics, economy, and social issues. - Cambodia Daily: A well-regarded source for news related to national affairs, business, and cultural events. - Fresh News: A prominent online news platform providing real-time updates on breaking news, sports, and entertainment. - Koh Santepheap Daily: A major Khmer-language newspaper known for its extensive reporting on current affairs and local issues. - VOD (Voice of Democracy): An independent news outlet focusing on in-depth coverage of politics, social issues, and investigative journalism.

Accessing Techsalerator’s News Events Data for Cambodia To access Techsalerator’s News Events Data for Cambodia, please contact info@techsalerator.com with your specific needs. We will provide a customized quote based on the data fields and records you require, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields - Event Date - Event Title - Source - Location - Event Description - Event Category (Politics, Economy, Sports, etc.) - Participants (if applicable) - Event Impact (Social, Economic, etc.)

Techsalerator’s dataset is a valuable tool for tracking significant events in Cambodia. It supports informed decision-making, whether for business strategy, market analysis, or academic research, offering a comprehensive view of the country’s news landscape.

The Forager.ai Global Dataset is a leading source of firmographic data, backed by advanced AI and offering the highest refresh rate in the industry.

| Volume and Stats |

• Over 70M total records, the highest volume in the industry today.
• Every company record refreshed twice a month, offering an unparalleled update frequency.
• Delivery is made every hour, ensuring you have the latest data at your fingertips.
• Each record is the result of an advanced AI-driven process, ensuring high-quality, accurate data.

| Use Cases |

Sales Platforms, ABM and Intent Data Platforms, Identity Platforms, Data Vendors:

Example applications include:

1. Uncover trending technologies or tools gaining popularity.

2. Pinpoint lucrative business prospects by identifying similar solutions utilized by a specific company.

3. Study a company's tech stacks to understand the technical capability and skills available within that company.

B2B Tech Companies:

• Enrich leads that sign-up through the Company Search API (available separately).
• Identify and map every company that fits your core personas and ICP.
• Build audiences to target, using key fields like location, company size, industry, and description.

Venture Capital and Private Equity:

• Discover new investment opportunities using company descriptions and industry-level data.
• Review the growth of private companies and benchmark their strength against competitors.
• Create high-level views of companies competing in popular verticals for investment.

| Delivery Options |

• Flat files via S3 or GCP
• PostgreSQL Shared Database
• PostgreSQL Managed Database
• API
• Other options available upon request, depending on the scale required

Our dataset provides a unique blend of volume, freshness, and detail that is perfect for Sales Platforms, B2B Tech, VCs & PE firms, Marketing Automation, ABM & Intent. It stands as a cornerstone in our broader data offering, ensuring you have the information you need to drive decision-making and growth.

Tags: Company Data, Company Profiles, Employee Data, Firmographic Data, AI-Driven Data, High Refresh Rate, Company Classification, Private Market Intelligence, Workforce Intelligence, Public Companies.

The Forager.ai Global Private Equity (PE) Funding Data Set is a leading source of firmographic data, backed by advanced AI and offering the highest refresh rate in the industry.

| Volume and Stats |

• Every company record refreshed twice a month, offering an unparalleled update frequency.
• Delivery is made every hour, ensuring you have the latest data at your fingertips.
• Each record is the result of an advanced AI-driven process, ensuring high-quality, accurate data.

| Use Cases |

Sales Platforms, ABM and Intent Data Platforms, Identity Platforms, Data Vendors:

Example applications include:

1. Uncover trending technologies or tools gaining popularity.

2. Pinpoint lucrative business prospects by identifying similar solutions utilized by a specific company.

3. Study a company's tech stacks to understand the technical capability and skills available within that company.

B2B Tech Companies:

• Enrich leads that sign-up through the Company Search API (available separately).
• Identify and map every company that fits your core personas and ICP.
• Build audiences to target, using key fields like location, company size, industry, and description.

Venture Capital and Private Equity:

• Discover new investment opportunities using company descriptions and industry-level data.
• Review the growth of private companies and benchmark their strength against competitors.
• Create high-level views of companies competing in popular verticals for investment.

| Delivery Options |

• Flat files via S3 or GCP
• PostgreSQL Shared Database
• PostgreSQL Managed Database
• API
• Other options available upon request, depending on the scale required

Our dataset provides a unique blend of volume, freshness, and detail that is perfect for Sales Platforms, B2B Tech, VCs & PE firms, Marketing Automation, ABM & Intent. It stands as a cornerstone in our broader data offering, ensuring you have the information you need to drive decision-making and growth.

Tags: Company Data, Company Profiles, Employee Data, Firmographic Data, AI-Driven Data, High Refresh Rate, Company Classification, Private Market Intelligence, Workforce Intelligence, Public Companies.