H-1B visa sponsorship trends for Data Analyst, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

IT spending worldwide is projected to reach over 5.7 trillion U.S. dollars in 2025, over a nine percent increase on 2024 spending. Smaller companies spending a greater share on hardware According to the results of a survey, hardware projects account for a fifth of IT budgets across North America and Europe. Larger companies tend to allocate a smaller share of their budget to hardware projects. Companies employing between one and 99 people allocated 31 percent of the budget to hardware, compared with 29 percent in companies of five thousand people or more. This could be explained by the greater need to spend money on managed services in larger companies. Not all companies can reduce their spending While COVID-19 has the overall effect of reducing IT spending, not all companies will face the same experiences. Setting up employees to comfortably work from home can result in unexpected costs, as can adapting to new operational requirements. In a recent survey of IT buyers, 18 percent of the respondents said they expected their IT budgets to increase in 2020. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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Generative AI In Data Analytics Market Size 2025-2029

The generative ai in data analytics market size is valued to increase by USD 4.62 billion, at a CAGR of 35.5% from 2024 to 2029. Democratization of data analytics and increased accessibility will drive the generative ai in data analytics market.

Market Insights

North America dominated the market and accounted for a 37% growth during the 2025-2029.
By Deployment - Cloud-based segment was valued at USD 510.60 billion in 2023
By Technology - Machine learning segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 621.84 million 
Market Future Opportunities 2024: USD 4624.00 million
CAGR from 2024 to 2029 : 35.5%

Market Summary

The market is experiencing significant growth as businesses worldwide seek to unlock new insights from their data through advanced technologies. This trend is driven by the democratization of data analytics and increased accessibility of AI models, which are now available in domain-specific and enterprise-tuned versions. Generative AI, a subset of artificial intelligence, uses deep learning algorithms to create new data based on existing data sets. This capability is particularly valuable in data analytics, where it can be used to generate predictions, recommendations, and even new data points. One real-world business scenario where generative AI is making a significant impact is in supply chain optimization. In this context, generative AI models can analyze historical data and generate forecasts for demand, inventory levels, and production schedules. This enables businesses to optimize their supply chain operations, reduce costs, and improve customer satisfaction. However, the adoption of generative AI in data analytics also presents challenges, particularly around data privacy, security, and governance. As businesses continue to generate and analyze increasingly large volumes of data, ensuring that it is protected and used in compliance with regulations is paramount. Despite these challenges, the benefits of generative AI in data analytics are clear, and its use is set to grow as businesses seek to gain a competitive edge through data-driven insights.

What will be the size of the Generative AI In Data Analytics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleGenerative AI, a subset of artificial intelligence, is revolutionizing data analytics by automating data processing and analysis, enabling businesses to derive valuable insights faster and more accurately. Synthetic data generation, a key application of generative AI, allows for the creation of large, realistic datasets, addressing the challenge of insufficient data in analytics. Parallel processing methods and high-performance computing power the rapid analysis of vast datasets. Automated machine learning and hyperparameter optimization streamline model development, while model monitoring systems ensure continuous model performance. Real-time data processing and scalable data solutions facilitate data-driven decision-making, enabling businesses to respond swiftly to market trends. One significant trend in the market is the integration of AI-powered insights into business operations. For instance, probabilistic graphical models and backpropagation techniques are used to predict customer churn and optimize marketing strategies. Ensemble learning methods and transfer learning techniques enhance predictive analytics, leading to improved customer segmentation and targeted marketing. According to recent studies, businesses have achieved a 30% reduction in processing time and a 25% increase in predictive accuracy by implementing generative AI in their data analytics processes. This translates to substantial cost savings and improved operational efficiency. By embracing this technology, businesses can gain a competitive edge, making informed decisions with greater accuracy and agility.

Unpacking the Generative AI In Data Analytics Market Landscape

In the dynamic realm of data analytics, Generative AI algorithms have emerged as a game-changer, revolutionizing data processing and insights generation. Compared to traditional data mining techniques, Generative AI models can create new data points that mirror the original dataset, enabling more comprehensive data exploration and analysis (Source: Gartner). This innovation leads to a 30% increase in identified patterns and trends, resulting in improved ROI and enhanced business decision-making (IDC).

Data security protocols are paramount in this context, with Classification Algorithms and Clustering Algorithms ensuring data privacy and compliance alignment. Machine Learning Pipelines and Deep Learning Frameworks facilitate seamless integration with Predictive Modeling Tools and Automated Report Generation on Cloud

The Big Data Analytics in Banking Market is Segmented by Type of Solutions (Data Discovery and Visualization (DDV) and Advanced Analytics (AA)), and Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD Million) for all the Above Segments.

The North America Next Generation Sequencing (NGS) Data Analysis report features an extensive regional analysis, identifying market penetration levels across major geographic areas. It highlights regional growth trends and opportunities, allowing businesses to tailor their market entry strategies and maximize growth in specific regions.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

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US Used Car Market Size 2025-2029

The US used car market size is forecast to increase by USD 40.2 billion, at a CAGR of 4.3% between 2024 and 2029.

The used car market in the US is witnessing significant growth, driven by the excellent value proposition that used cars offer to consumers. The increasing popularity of websites dedicated to selling used cars has expanded market reach and convenience, allowing consumers to browse and purchase vehicles online. Stringent emission regulations are restricting the sales of non-compliant used cars, necessitating investments in upgrading and maintaining commercial vehicle fleets to meet regulatory requirements. These regulations necessitate investments in emission testing and certification processes, increasing operational costs for dealers. To capitalize on opportunities, dealers can focus on offering certified pre-owned vehicles and implementing robust emission testing procedures.
Additionally, leveraging digital marketing strategies and offering flexible financing options can help attract and retain customers. Overall, the used car market presents both challenges and opportunities for players, requiring strategic planning and innovation to succeed.

What will be the size of the US Used Car Market during the forecast period?

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The used car market in the US continues to evolve, with various sectors adapting to emerging trends and technologies. Vehicle data analysis plays a pivotal role in understanding vehicle depreciation curves and return on investment for dealers. Payment processing systems streamline sales transactions, while sales performance metrics and customer lifetime value inform strategic decision-making. Fraud detection systems ensure compliance with legal standards, and insurance cost factors influence acquisition channel efficiency. Inventory turnover rate, a key performance indicator, varies across dealerships. Compliance audits and dealer training programs maintain legal compliance and improve customer satisfaction. Market penetration rate and resale value prediction help dealers optimize pricing models.
Consumer protection laws and financing product offerings shape customer trust and loyalty. Operating costs analysis, customer service feedback, and sales conversion rates contribute to profit margin calculation. Risk assessment models, employee performance metrics, marketing spend efficiency, and pricing model validation are essential for long-term success. A recent study reveals a 5% increase in sales for dealerships implementing advanced data analytics. Industry growth is expected to reach 3% annually, driven by these evolving market dynamics.

How is this market segmented?

The US used car market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Distribution Channel

  3P channel sales
  OEM channel sales


Product

  Mid size
  Full size
  Compact size


Vendor Type

  Organized
  Unorganized


Fuel Type

  Diesel
  Petrol


Geography

  North America

    US

By Distribution Channel Insights

The 3P channel sales segment is estimated to witness significant growth during the forecast period.

The used car market in the US is an active and dynamic sector, driven by various factors. With the constant launch of new vehicle models, the supply of used cars increases, resulting in lower prices compared to new cars. This trend encourages car owners to sell their vehicles and upgrade to newer models, shortening the average ownership cycle. Online advertising platforms play a significant role in connecting buyers and sellers. Pre-purchase inspections and vehicle history reports ensure transparency and build trust. Repairs cost estimation and parts sourcing networks help in managing the expenses of used car ownership. Market segmentation strategies cater to different customer needs, while customer relationship management tools foster loyalty.

Emissions testing standards ensure the environmental sustainability of used vehicles. Auto appraisal value tools help in determining fair prices, and loan term comparison aids in financing decisions. Marketing campaign effectiveness is measured through customer acquisition cost and interest rate calculation. Mobile apps offer functionalities like mechanical inspection checklists, paint depth measurement, and damage assessment tools. Dealer inventory management, detailing services, and vehicle photography techniques enhance the sales process. Industry growth is expected to continue, with the used car market projected to expand by 3% annually. For instance, a dealership successfully increased its sales by 15% thr

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry 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.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector, gover

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Big Data As A Service Market Size 2025-2029

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

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

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

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

How is this Big Data As A Service Industry segmented?

The big data as a service industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

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


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


End-user

  Large enterprises
  SMEs


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Type Insights

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

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

The United States Data Center Construction Market is Segmented by Tier Type (Tier 1 and 2, Tier 3 and Tier 4), Data Center Type(Colocation, Self-Built Hyperscalers (CSPs), Enterprise, and Edge), and Infrastructure (Electrical Infrastructure, Mechanical Infrastructure). The Market Forecasts are Provided in Terms of Value (USD).

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Next Generation Sequencing Data Analysis Market Size 2024-2028

The global next generation sequencing data analysis market size is estimated to grow by USD 1.90 billion at a CAGR of 22.58% between 2023 and 2028. The market's growth hinges on several factors, including the escalating demand for personalized medicine, the increasing need for early diagnosis of genetic disorders, and the expanding applications in genomics research. Personalized medicine, tailored to individual genetic makeup, is gaining traction for its targeted and more effective treatment approach. The emphasis on early diagnosis of genetic disorders is driving the demand for advanced genetic testing technologies. Moreover, the broadening applications in genomics research, particularly in understanding genetic mechanisms and disease pathways, are fueling market expansion. These trends collectively highlight the growing significance of genetic testing and personalized medicine in healthcare, underscoring the market's growth trajectory.

What will be the Size of the Next Generation Sequencing Data Analysis Market During the Forecast Period?

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Key Companies & Market Insights

Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market. The report also includes detailed analyses of the competitive landscape of the market and information about key companies, including:

Agilent Technologies Inc., Alphabet Inc., BGI Genomics Co. Ltd., Bio Rad Laboratories Inc., Bionivid Technology Pvt. Ltd., Congenica Ltd., Corewell Health, DNAnexus Inc., DNASTAR Inc., Eurofins Scientific SE, F. Hoffmann La Roche Ltd., Fabric Genomics Inc., Golden Helix Inc., HiberCell Inc., Illumina Inc., Invitae Corp., Macrogen Inc., Oxford Nanopore Technologies plc, Pacific Biosciences of California Inc., Partek Inc., PierianDx Inc., QIAGEN NV, SciGenom Labs Pvt. Ltd., Takara Bio Inc., Thermo Fisher Scientific Inc., and Vela Diagnostics

Qualitative and quantitative analysis of companies has been conducted to help clients understand the wider business environment as well as the strengths and weaknesses of key market players. Data is qualitatively analyzed to categorize companies as pure play, category-focused, industry-focused, and diversified; it is quantitatively analyzed to categorize companies as dominant, leading, strong, tentative, and weak.

Market Segmentation

By End-user

The market share growth by the academic research segment will be significant during the forecast period. The market encompasses DNA sequencing technologies used in genomic science, academic research, and clinical diagnostics. Academic institutions utilize NGS for various applications, such as drug discovery, personalized medicine, and clinical diagnostics.

Get a glance at the market contribution of various segments Download PDF Sample

The academic research segment was valued at USD 221.1 million in 2018. Key drivers include decreasing sequencing costs, user-friendly software, and the demand for precision medicine. NGS enables the analysis of genomic patterns, epigenetics, and biological processes through sequence analysis tools and algorithms. Applications include oncology, genetic research, and tumor genotyping. NGS protocols aid in identifying somatic driver mutations, germline mutations, and resistance mutations. Cancer-related illnesses, financial irregularities, and healthcare professionals benefit from these tools, machine learning techniques, and cloud-based solutions. Additionally, NGS is applied in agriculture, forensics, and genomic studies. Key technologies include Whole-Genome Sequencing, array-based technologies, and clinical. Hence, these factors are expected to drive the market during the forecast period.

By Product

Services play an important role in the market, providing specialized expertise and support to users in analyzing and interpreting their NGS data. The market encompasses various services for Exome Sequencing, Targeted Resequencing, De Novo Sequencing, and Methyl Sequencing. Biotechnology and pharmaceutical companies, along with contract research organizations, utilize these services to analyze and interpret their NGS data. The process involves raw data preprocessing, alignment, variant calling, and annotation, employing advanced tools and algorithms. Service providers ensure accuracy and reliability through quality control measures and optimization of parameters. Technologies like Synthesis (SBS) are integral part. Hence, these factors are expected to drive the growth of the services segment in the market during the forecast period.

Regional Analysis

For more insights about the market share of various regions Download PDF Sample

North America is estimated to contribute 49% to the growth of the global market during t

Context

Mexico is in the Top 5 countries with more COV19 deaths. many patients are dying every day in the hospitals. But here's the thing:

How can we know what people are at greater risk of dying by COV19? and then what can we do with that information?

This dataset was collected by Mexican health authorities and contains all registers about COV19 patients by the time this dataset was downloaded (April, 2021).

Content

What's inside is data about the patients, and there are many things we can know about the patients such as where did they were hospitalized, in what state of Mexico, them age and the date of the death.

Also there are many interesting columns that tell us is the patient has a health issue apart from COVID19 and there's a long list about this issues such as obesity, hypertension, asthma, diabetes, ....

And there are other important features: if the patient smokes, if the patient was diagnosed with pneumonia or whether the patient was intubated.

Acknowledgements

We wouldn't be here without the help of others. This dataset was collected by the Mexican health authorities. Thanks to them we have this dataset today.

The dataset was downloaded from the government page

Inspiration

This dataset contains many data that could help us to answer many questions, for example:

• How probability of dying increase with the patient's age?
• How that probability increases if the patient has another health issue?
• How dying probability increases when the patient has been intubated?
• Is it true that smoking increases the probability of dying by COV19?
• In what state there are more deaths?

There are many other questions that you can answer.

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Business Information Market Size 2025-2029

The business information market size is forecast to increase by USD 79.6 billion, at a CAGR of 7.3% between 2024 and 2029.

The market is characterized by the increasing demand for customer-centric solutions as enterprises adapt to evolving customer preferences. This shift necessitates the provision of real-time, accurate, and actionable insights to facilitate informed decision-making. However, this market landscape is not without challenges. The threat of data misappropriation and theft looms large, necessitating robust security measures to safeguard sensitive business information. As businesses continue to digitize their operations and rely on external data sources, ensuring data security becomes a critical success factor. Companies must invest in advanced security technologies and implement stringent data protection policies to mitigate these risks. Navigating this complex market requires a strategic approach that balances the need for customer-centric solutions with the imperative to secure valuable business data.

What will be the Size of the Business Information Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In today's data-driven business landscape, the continuous and evolving nature of market dynamics plays a pivotal role in shaping various sectors. Data integration solutions enable seamless data flow between different systems, enhancing cloud-based business applications' functionality. Data quality management ensures data accuracy and consistency, crucial for strategic planning and customer segmentation. Data infrastructure, data warehousing, and data pipelines form the backbone of business intelligence, facilitating data storytelling and digital transformation. Data lineage and data mining reveal valuable insights, fueling data analytics platforms and business intelligence infrastructure. Data privacy regulations necessitate robust data management tools, ensuring compliance and protecting sensitive information.

Sales forecasting and business intelligence consulting offer valuable industry analysis and data-driven decision making. Data governance frameworks and data cataloging maintain order and ethics in the vast expanse of big data analytics. Machine learning algorithms, predictive analytics, and real-time analytics drive business intelligence reporting and process modeling, leading to business process optimization and financial reporting software. Sentiment analysis and marketing automation cater to customer needs, while lead generation and data ethics ensure ethical business practices. The ongoing unfolding of market activities and evolving patterns necessitate the integration of various tools and frameworks, creating a dynamic interplay that fuels business growth and innovation.

How is this Business Information Industry segmented?

The business information industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  BFSI
  Healthcare and life sciences
  Manufacturing
  Retail
  Others


Application

  B2B
  B2C


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW). 

By End-user Insights

The bfsi segment is estimated to witness significant growth during the forecast period.

In the dynamic business landscape, data-driven insights have become essential for strategic planning and decision-making across various industries. The market caters to this demand by offering solutions that integrate and manage data from multiple sources. These include cloud-based business applications, data quality management tools, data warehousing, data pipelines, and data analytics platforms. Data storytelling and digital transformation are key trends driving the market's growth, enabling businesses to derive meaningful insights from their data. Data governance frameworks and policies are crucial components of the business intelligence infrastructure. Data privacy regulations, such as GDPR and HIPAA, are shaping the market's development.

Data mining, predictive analytics, and machine learning algorithms are increasingly being used for sales forecasting, customer segmentation, and churn prediction. Business intelligence consulting and industry analysis provide valuable insights for organizations seeking competitive advantage. Data visualization dashboards, market research databases, and data discovery tools facilitate data-driven decision making. Sentiment analysis and predictive analytics are essential for marketing automation and business process

SpaceKnow uses satellite (SAR) data to capture activity in electric vehicles and automotive factories.

Data is updated daily, has an average lag of 4-6 days, and history back to 2017.

The insights provide you with level and change data that monitors the area which is covered with assembled light vehicles in square meters.

We offer 3 delivery options: CSV, API, and Insights Dashboard

Available companies Rivian (NASDAQ: RIVN) for employee parking, logistics, logistic centers, product distribution & product in the US. (See use-case write up on page 4) TESLA (NASDAQ: TSLA) indices for product, logistics & employee parking for Fremont, Nevada, Shanghai, Texas, Berlin, and Global level Lucid Motors (NASDAQ: LCID) for employee parking, logistics & product in US

Why get SpaceKnow's EV datasets?

Monitor the company’s business activity: Near-real-time insights into the business activities of Rivian allow users to better understand and anticipate the company’s performance.

Assess Risk: Use satellite activity data to assess the risks associated with investing in the company.

Types of Indices Available Continuous Feed Index (CFI) is a daily aggregation of the area of metallic objects in square meters. There are two types of CFI indices. The first one is CFI-R which gives you level data, so it shows how many square meters are covered by metallic objects (for example assembled cars). The second one is CFI-S which gives you change data, so it shows you how many square meters have changed within the locations between two consecutive satellite images.

How to interpret the data SpaceKnow indices can be compared with the related economic indicators or KPIs. If the economic indicator is in monthly terms, perform a 30-day rolling sum and pick the last day of the month to compare with the economic indicator. Each data point will reflect approximately the sum of the month. If the economic indicator is in quarterly terms, perform a 90-day rolling sum and pick the last day of the 90-day to compare with the economic indicator. Each data point will reflect approximately the sum of the quarter.

Product index This index monitors the area covered by manufactured cars. The larger the area covered by the assembled cars, the larger and faster the production of a particular facility. The index rises as production increases.

Product distribution index This index monitors the area covered by assembled cars that are ready for distribution. The index covers locations in the Rivian factory. The distribution is done via trucks and trains.

Employee parking index Like the previous index, this one indicates the area covered by cars, but those that belong to factory employees. This index is a good indicator of factory construction, closures, and capacity utilization. The index rises as more employees work in the factory.

Logistics index The index monitors the movement of materials supply trucks in particular car factories.

Logistics Centers index The index monitors the movement of supply trucks in warehouses.

Where the data comes from: SpaceKnow brings you information advantages by applying machine learning and AI algorithms to synthetic aperture radar and optical satellite imagery. The company’s infrastructure searches and downloads new imagery every day, and the computations of the data take place within less than 24 hours.

In contrast to traditional economic data, which are released in monthly and quarterly terms, SpaceKnow data is high-frequency and available daily. It is possible to observe the latest movements in the EV industry with just a 4-6 day lag, on average.

The EV data help you to estimate the performance of the EV sector and the business activity of the selected companies.

The backbone of SpaceKnow’s high-quality data is the locations from which data is extracted. All locations are thoroughly researched and validated by an in-house team of annotators and data analysts.

Each individual location is precisely defined so that the resulting data does not contain noise such as surrounding traffic or changing vegetation with the season.

We use radar imagery and our own algorithms, so the final indices are not devalued by weather conditions such as rain or heavy clouds.

→ Reach out to get a free trial

Use Case - Rivian:

SpaceKnow uses the quarterly production and delivery data of Rivian as a benchmark. Rivian targeted to produce 25,000 cars in 2022. To achieve this target, the company had to increase production by 45% by producing 10,683 cars in Q4. However the production was 10,020 and the target was slightly missed by reaching total production of 24,337 cars for FY22.

SpaceKnow indices help us to observe the company’s operations, and we are able to monitor if the company is set to meet its forecasts or not. We deliver five different indices for Rivian, and these indices observe logistic centers, employee parking lot, logistics, product, and prod...

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Industry: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 0 households where the householder is under 25 years old, 18(15.52%) households with a householder aged between 25 and 44 years, 74(63.79%) households with a householder aged between 45 and 64 years, and 24(20.69%) households where the householder is over 65 years old.
• The age group of 65 years and over exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Industry, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Industry median household income by age. You can refer the same here

This dataset contains information on fatal police deaths in the United States. The data includes the victim's rank, name, department, date of death, and cause of death. The data spans from 1791 to the present day. This dataset will be updated on monthly basis. Data Scrapped from this website :- https://www.odmp.org/

New Version Features -> With the new web scrapper I have upgraded dataset with more information. 1) The new dataset version is "police_deaths_USA_v6.csv" and "k9_deaths_USA_v6.csv". 2) Splitted the dataset into 2 different datasets 1 for Human Unit and other for K9 Unit. 3) Check out the new web scrapper code in this file "final_scrapper_program_with_comments.ipynb". 4) Also added the correction file which is needed to adjust some data points from K9 dataset. 5) Extended data of Human Unit dataset to 13 Features. 6) Extended data of K9 Unit dataset to 14 Features.

The police_deaths dataset contains 13 variables:

1) Rank -> Rank assigned or achieved by the police throughout their tenure.

2) Name -> The name of the person.

3) Age -> Age of the person.

4) End_Of_Watch -> The death date on which the the person declared as dead.

5) Day_Of_Week -> The day of the week [Sunday, Monday, etc.].

6) Cause -> The cause of the death.

7) Department -> The department's name where the person works.

8) State -> The state where the department is situated.

9) Tour -> The Duration of there Tenure.

10) Badge -> Badge of the person.

11) Weapon -> The Weapon by which the officer has been killed.

12) Offender -> Offender / Killer this says what happened to the offender after the incident was he/she [Arrested, Killed, etc.].

13) Summary -> Summary of the police officer and also the summary of the incident of what happened ? How he/she died ?, etc.

The k9_deaths dataset contains 14 variables:

1) Rank -> Rank assigned or achieved by the K9 throughout their tenure.

2) Name -> The name of the K9.

3) Breed -> Breed of the K9.

4) Gender -> Gender of the K9.

5) Age -> Age of the K9.

6) End_Of_Watch -> The death date on which the the person declared as dead.

7) Day_Of_Week -> The day of the week [Sunday, Monday, etc.].

8) Cause -> The cause of the death.

9) Department -> The department's name where the K9 was assigned.

10) State -> The state where the department is situated.

11) Tour -> The Duration of there Tenure.

12) Weapon -> The Weapon by which the officer has been killed.

13) Offender -> Offender / Killer this says what happened to the offender after the incident was he/she [Arrested, Killed, etc.].

14) Summary -> Summary of the K9 dog and also the summary of the incident of what happened ? How he/she died ?, etc.

Acknowledgements:

The original dataset was collected by FiveThirtyEight and it contains police death data from 1791 to 2016. Here is the link -> https://data.world/fivethirtyeight/police-deaths.

The reason I made this dataset is because it had not been updated since 2016 and the scrapping script was outdated, so I decided to make a new scrapper and update the dataset till present. I got this idea from the FiveThirtyEight group and a fellow kaggler, Satoshi Datamoto, who uploaded the dataset on kaggle. Thank you for inspiration.

Tableau Visualization link :- https://public.tableau.com/app/profile/mayuresh.koli/viz/USALawEnforcementLineofDutyDeaths/main_dashboard

US Congress Legislators Historical Data

A Detailed Dataset on Past and Present US Legislators' Profiles and Terms

By GovTrack [source]

About this dataset

This dataset provides comprehensive information on current and historical US legislators and their terms. The data includes diverse details, such as biographical data - names, gender, religion - along with IDs from multiple systems like Bioguide ID, FEC ID, GovTrack ID along with an official full name according to the House or Senate. It also captures alternate names used officially by legislators if they undergo a legal name change.

Moreover, the dataset also contains legislator identifiers from other websites such as OpenSecrets.org (an alphanumeric ID), VoteSmart.org (numeric stored as integer), VoteView.com (numeric stored as integer), C-SPAN's video website(numeric stored as integer), Wikipedia page names (alphanumeric), Ballotpedia page names(alphanumeric) and maplight.org(numeric).

Regarding the terms of each election held for legislators, key information found in this package includes state inclination in two-letter USPS abbreviation format alongside district numbers for representatives' service areas. For senators' specifics - there are inputs about their election class(1 2 or 3). Additionally captured are details around leadership roles – titles within parties plus dates of service.

Also included is rich contextual tell-tale about a legislator's political associations – party affiliations at both start & end dates indicating any switches during legislative term tenures.

The dataset extends itself beyond just being an academic resource; it helps build intuitive connections via RSS feeds URLs while offering details around their Washington DC office contact points – address suitably detailed room-wise plus phone/fax numbers alongside web URLs besides standalone contact page pointers.

Lastly but uniquely marks out official social media presence which includes Twitter handles/IDs & Facebook usernames/IDs further improving handle-based access for tools driven by API communication suggesting its utility not confined to structured academic research alone but extending to unstructured data handling digital companies specializing in sentiment analysis over multiple platforms/sources offering end-to-end integration or maybe be it organizations cross vérifying objective details over federal election claims by mapping FEC IDs to social media campaigns.

The dataset serves a wide array of researchers, policy analysts, political theorists, and technology centric analytics businesses. Conversely it can also help the curious public in learning about historical & current political landscapes in the US while checking their representatives' official web presence thereby fostering community engagement not just around elections but also during legislative tenures

How to use the dataset

This comprehensive dataset contains information on current and historical US Legislators and their terms. It can be used in a multitude of ways, such as academic research, journalism, policy making or for general interest. Here's a guide on how you can use this data:

Broad Overview:

Firstly, it's helpful to examine the broad layout of the data by taking an overall look at all files in the set: legislators-current.csv, legislators-historical.csv, legislators-current-terms.csv and legislators-historical-terms.csv.

The 'current' and 'historical' datasets pertain to sitting members of congress or those from past terms respectively.

The legislator files contain biographical information such as names (including possible name changes), gender and religion of each member whereas the term files hold details about their political careers including term type (senate or representative), state represented, district if relevant along with party affiliation.

Biographical Research:

You could use this data to create biographies for every legislator by collating personal information from first\_name, middle\_name, last\_name, suffix\_name, gender (gender_bio), birth date (birthday_bio) along with other identifying fields such as wikipedia_id and ballotpedia_id.

For instance - if you wanted to understand representation across genders over time, leverage the field gender_bio.

Political Trends Analysis:

Each legislator's movements through political roles over time is documented meticulously in these datasets. By filtering on specific IDs (like Thomas ID) you can get a chronological overview of their progression. Use this feature to understand shifting political trends within states or districts.

Through cross-referencing this dataset with...

Dataset Title: Urban Air Quality and Health Impact Dataset: A Comprehensive Overview of U.S. Cities

Description:

This dataset provides an extensive collection of synthetic data related to urban air quality and its potential health impacts across major U.S. cities. The data has been augmented to include a wide range of features, making it a valuable resource for research and analysis in the fields of environmental science, public health, and urban studies.

Features:

• DateTime: Timestamp of the recorded data.
• City: The U.S. city where the data was recorded (e.g., Phoenix, San Diego, New York City).
• Temp_Max: Maximum temperature for the day (°F).
• Temp_Min: Minimum temperature for the day (°F).
• Temp_Avg: Average temperature for the day (°F).
• Feels_Like_Max: Maximum "feels like" temperature for the day (°F).
• Feels_Like_Min: Minimum "feels like" temperature for the day (°F).
• Feels_Like_Avg: Average "feels like" temperature for the day (°F).
• Dew_Point: Dew point temperature (°F).
• Humidity: Relative humidity percentage.
• Precipitation: Total precipitation for the day (inches).
• Precip_Prob: Probability of precipitation (percentage).
• Precip_Cover: Coverage of precipitation (percentage).
• Precip_Type: Type of precipitation (e.g., rain, snow).
• Snow: Amount of snowfall (inches).
• Snow_Depth: Snow depth (inches).
• Wind_Gust: Maximum wind gust speed (mph).
• Wind_Speed: Average wind speed (mph).
• Wind_Direction: Wind direction (degrees).
• Pressure: Atmospheric pressure (hPa).
• Cloud_Cover: Cloud cover percentage.
• Visibility: Visibility distance (miles).
• Solar_Radiation: Solar radiation (W/m²).
• Solar_Energy: Solar energy received (kWh).
• UV_Index: UV index level.
• Severe_Risk: Risk level of severe weather (e.g., low, moderate, high).
• Sunrise: Sunrise time (HH:MM:SS).
• Sunset: Sunset time (HH:MM:SS).
• Moon_Phase: Phase of the moon (e.g., new moon, full moon).
• Conditions: General weather conditions (e.g., clear, cloudy).
• Description: Detailed description of the weather conditions.
• Icon: Weather icon representation.
• Stations: Weather stations reporting data.
• Source: Data source information.
• Temp_Range: Temperature range for the day (difference between max and min temperatures).
• Heat_Index: Heat index value for the day.
• Severity_Score: Score representing the severity of weather conditions.
• Condition_Code: Code representing specific weather conditions.
• Month: Month of the year.
• Season: Season of the year (e.g., winter, spring).
• Day_of_Week: Day of the week.
• Is_Weekend: Indicator if the day is a weekend.
• Health_Risk_Score: Score representing the potential health risk based on weather and air quality conditions.

Usage:

This dataset is intended for researchers, data scientists, and analysts interested in studying the relationships between air quality, weather conditions, and public health. It can be used for developing predictive models, conducting statistical analyses, and creating visualizations to better understand urban environmental impacts.

Source:

The data is synthesized and augmented based on real-world weather data from major U.S. cities and is intended to serve as a comprehensive resource for urban air quality and health impact studies.

Notes:

• The dataset is synthetic and has been generated to provide a broad range of scenarios for analysis.
• Ensure to validate any findings with real-world data when applying the insights to practical applications. .