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Alternative Data Market Size 2025-2029

The alternative data market size is forecast to increase by USD 60.32 billion at a CAGR of 52.5% between 2024 and 2029.

The market is experiencing significant growth due to the increased availability and diversity of data sources. This trend is driven by the rise of alternative data-driven investment strategies, which offer unique insights and opportunities for businesses and investors. However, challenges persist in the form of issues related to data quality and standardization. big data analytics and machine learning help businesses gain insights from vast amounts of data, enabling data-driven innovation and competitive advantage. Data governance, data security, and data ethics are crucial aspects of managing alternative data.
As more data becomes available, ensuring its accuracy and consistency is crucial for effective decision-making. The market analysis report provides an in-depth examination of these factors and their impact on the growth of the market. With the increasing importance of data-driven strategies, staying informed about the latest trends and challenges is essential for businesses looking to remain competitive in today's data-driven economy.

What will be the Size of the Alternative Data Market During the Forecast Period?

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Alternative data, the non-traditional information sourced from various industries and domains, is revolutionizing business landscapes by offering new opportunities for data monetization. This trend is driven by the increasing availability of data from various sources such as credit card transactions, IoT devices, satellite data, social media, and more. Data privacy is a critical consideration in the market. With the increasing focus on data protection regulations, businesses must ensure they comply with stringent data privacy standards. Data storytelling and data-driven financial analysis are essential applications of alternative data, providing valuable insights for businesses to make informed decisions. Data-driven product development and sales prediction are other significant areas where alternative data plays a pivotal role.
Moreover, data management platforms and analytics tools facilitate data integration, data quality, and data visualization, ensuring data accuracy and consistency. Predictive analytics and data-driven risk management help businesses anticipate trends and mitigate risks. Data enrichment and data-as-a-service are emerging business models that enable businesses to access and utilize alternative data. Economic indicators and data-driven operations are other areas where alternative data is transforming business processes.

How is the Alternative Data Market Segmented?

The 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.

Type

  Credit and debit card transactions
  Social media
  Mobile application usage
  Web scrapped data
  Others


End-user

  BFSI
  IT and telecommunication
  Retail
  Others


Geography

  North America

    Canada
    Mexico
    US


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    India
    Japan


  South America



  Middle East and Africa

By Type Insights

The credit and debit card transactions segment is estimated to witness significant growth during the forecast period.

Alternative data derived from card and debit card transactions offers valuable insights into consumer spending behaviors and lifestyle choices. This data is essential for market analysts, financial institutions, and businesses seeking to enhance their strategies and customer experiences. The two primary categories of card transactions are credit and debit. Credit card transactions provide information on discretionary spending, luxury purchases, and credit management skills. In contrast, debit card transactions reveal essential spending habits, budgeting strategies, and daily expenses. By analyzing this data using advanced methods, businesses can gain a competitive advantage, understand market trends, and cater to consumer needs effectively. IT & telecommunications companies, hedge funds, and other organizations rely on web scraped data, social and sentiment analysis, and public data to supplement their internal data sources. Adhering to GDPR regulations ensures ethical data usage and compliance.

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The credit and debit card transactions segment was valued at USD 228.40 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 56% to the growth of the global market during the forecast period.

T

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Big Data In Manufacturing Market Size 2024-2028

The big data in manufacturing market size is forecast to increase by USD 17.32 billion at a CAGR of 25.86% between 2023 and 2028.

The big data market in manufacturing is experiencing significant growth due to several key trends. The increasing adoption of Industry 4.0 and the emergence of artificial intelligence (AI) and machine learning (ML) are major drivers. The complexity of big data analytics is also fueling market growth. Industry 4.0, also known as the Fourth Industrial Revolution, is transforming manufacturing processes through automation and data-driven decision making. AI and ML are essential tools in this digital transformation, enabling predictive maintenance, quality control, and supply chain optimization. The analysis of vast amounts of data generated by these technologies is crucial for manufacturers to gain insights, improve efficiency, and remain competitive.
However, the challenges of managing and processing large volumes of data, ensuring data security, and integrating various data sources remain significant barriers to entry. Despite these challenges, the potential benefits of big data analytics in manufacturing are substantial, making it an exciting and dynamic market to watch.

What will be the Size of the Big Data In Manufacturing Market During the Forecast Period?

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The big data market in manufacturing is experiencing robust growth, driven by the increasing adoption of advanced technologies such as M2M communication, IoT, RFIDs, sensors, barcode readers, robots, automation, artificial intelligence (AI), and machine learning. OEMs are integrating these technologies into their production processes to enhance operational efficiency, reduce costs, and improve product quality. ERP systems are being upgraded with real-time analytics capabilities to enable data-driven decision-making. Processing power and storage capacity are no longer limiting factors, as cloud-based solutions offer virtually unlimited resources. Industrial digitalization is transforming the manufacturing landscape, with IT teams shifting focus from on-premises to cloud-based apps.
Open-source initiatives and descriptive analytics are gaining traction, enabling organizations to derive insights from their data and optimize performance. Connected devices and RFID technology are revolutionizing supply chain management and inventory control. Overall, the manufacturing industry is evolving into a metrics-based, data-driven sector, where AI and machine learning are becoming essential tools for competitive advantage.

How is this Big Data In Manufacturing Industry segmented and which is the largest segment?

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

Type

  Services
  Solutions


Deployment

  On-premises
  Cloud-based
  Hybrid


Geography

  North America

    Canada
    US


  APAC

    China


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Type Insights

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

In the manufacturing sector, the services segment led the big data market in 2023 due to the increasing adoption of data analytics for cost savings, resource optimization, and operational efficiency. The manufacturing industry generates massive data from various sources, including sensors, machines, production lines, and supply chain operations. This data is a valuable asset, enabling predictive maintenance, real-time product quality monitoring, and inventory optimization. Big data services facilitate these applications, enabling manufacturers to minimize downtime, reduce defects, and optimize resource allocation. Leading OEMs, ERP systems, and M2M communication providers, such as John Deere, Oracle Corporation, and SAS Institute Inc, are integrating big data analytics into their offerings.

IoT, RFIDs, sensors, barcode readers, robots, and AI are key technologies driving industrial digitalization. Big data analytics solutions from Altair, Snowflake, Clustering, Regression, and Fair Isaac Corporation facilitate predictive asset management, inventory management, and supply chain analysis. The manufacturing industry's transition to connected factories and automation is accelerating, with cloud-based solutions from IBM, Cerner, and others enabling on-premises and cloud-based deployments.

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The Services segment was valued at USD 2.5 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 45% to the growth of the glo

Snapshot img

Big Data Market Size 2024-2028

The big data market size is forecast to increase by USD 508.73 billion at a CAGR of 21.46% between 2023 and 2028.

The market is experiencing significant growth due to the growth in data generation from various sources, including IoT platforms and digital transformation services. This data deluge presents opportunities for businesses to leverage advanced analytics tools for applications such as fraud detection and prevention, workforce analytics, and business intelligence. However, the increasing adoption of big data implementation also brings challenges, including the need for data security and privacy measures. Quantum computing and blockchain technology are emerging trends In the big data landscape, offering potential solutions to complex data processing and security issues. In healthcare analytics, data protection regulations are driving the need for secure data management and sharing.
Additionally, supply chain optimization is another area where big data can bring significant value, enabling real-time monitoring and predictive analytics. Overall, the market is poised for continued growth, driven by the need to extract valuable insights from the vast amounts of data being generated.

What will be the Size of the Big Data Market During the Forecast Period?

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The market is experiencing growth as businesses increasingly leverage information from vast datasets to drive strategic decision-making, enhance customer experiences, and improve operational efficiency. The digital revolution has led to an exponential increase in data creation, fueling demand for advanced analytics capabilities, real-time processing, and data protection and privacy solutions. Hardware and software companies offer on-premise and cloud-based systems to accommodate various industry needs, including customer analytics in retail and e-commerce, supply chain analytics in manufacturing, marketing analytics, pricing analytics, spatial analytics, workforce analytics, risk and credit analytics, transportation analytics, healthcare, energy and utilities, and IT and telecom. Big data applications span numerous sectors, enabling organizations to gain valuable insights from their data to optimize operations, mitigate risks, and innovate new products and services.

How is this Big Data Industry segmented and which is the largest segment?

The big data 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.

Deployment

  On-premises
  Cloud-based
  Hybrid


Type

  Services
  Software


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period. On-premises big data software solutions involve the installation of hardware and software by the end-user, granting them complete control over the system. Despite the high upfront costs, on-premises solutions offer advantages such as full ownership and operational efficiency. In contrast, cloud-based solutions require recurring monthly payments and involve data storage on companies' servers, increasing security concerns. Advanced analytics, real-time processing, and integrated analytics are key features driving the market. Data creation from digital transformation, customer experiences, and various industries like retail, healthcare, and finance, fuel the demand for scalable infrastructure and user-friendly interfaces. Technologies such as quantum computing, blockchain, AI-driven analytics platforms, and automation are transforming business intelligence solutions.

Ensuring data protection and privacy, accessibility, and seamless data transactions are crucial in this data-driven era. Key technologies include distributed computing, visualization tools, and social media. Target audiences range from decision-makers to various industries, including transportation, energy, and consumer engagement.

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The On-premises segment was valued at USD 86.53 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 47% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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

The market in North America is experiencing significant growth due to digital transformation initiatives by enterprises in sectors such as healthcare, retail

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of 1244.08; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

Alzheimer Disease Neuroimaging Initiative dataset.

The media analysis data was collected for commercial purposes. They are used in media planning as well as in the advertising planning of the different media genres (radio, press media, TV, poster and since 2010 also online). They are cross-sections that are merged together for one year. ag.ma kindly provides the data for scientific use on an annual basis – with a two-year notice period – to GESIS. In addition, agof has provided documentation regarding data collection (questionnaires, code plans, etc.) for the preparation of the MA IntermediaPlus online bundle.

In order to make the data accessible for scientific use, the datasets of the individual years were harmonized and pooled into a longitudinal data set starting in 2014 as part of the dissertation project ´Audience and Market Fragmentation online´ of the Digital Society research program NRW at the Heinrich-Heine-University (HHU) and the University of Applied Sciences Düsseldorf (HSD), funded by the Ministry of Culture and Science of the German State of North Rhine-Westphalia.
The prepared Longitudinal IntermediaPlus dataset 2014 to 2016 is a ´big data´, which is why the entire dataset will only be available in the form of a database (MySQL). In this database, the information of different variables of a respondent is organized in one column, one row per variable. The present data documentation shows the total database for online media use of the years 2014 to 2016. The data contains all variables of socio demography, free-time activities, additional information on a respondent and his household as well as the interview-specific variables and weights. Only the variables concerning the respondent´s media use are a selection:

The online media use of all full online as well as their single entities for all genres whose business model is the provision of content is included - e-commerce, games, etc. were excluded. The media use of radio, print and TV is not included.

Preparation for further years is possible, as is the preparation of cross-media media use for radio, press media and TV. Harmonization is available for radio and press media up to 2015 waiting to be applied. The digital process chain developed for data preparation and harmonization is published at GESIS and available for further projects updating the time series for further years. Recourse to these documents - Excel files, scripts, harmonization plans, etc. - is strongly recommended.

The process and harmonization for the Longitudinal IntermediaPlus for 2014 to 2016 database was made available in accordance with the FAIR principles (Wilkinson et al. 2016). By harmonizing and pooling the cross-sectional datasets to one longitudinal dataset – which is being carried out by Inga Brentel and Céline Fabienne Kampes as part of the dissertation project ´Audience and Market Fragmentation online´ –, the aim is to make the data source of the media analysis, accessible for research on social and media change in Germany.

This is the updated version of the dataset from 10.5281/zenodo.6320761 Information The diverse publicly available compound/bioactivity databases constitute a key resource for data-driven applications in chemogenomics and drug design. Analysis of their coverage of compound entries and biological targets revealed considerable differences, however, suggesting benefit of a consensus dataset. Therefore, we have combined and curated information from five esteemed databases (ChEMBL, PubChem, BindingDB, IUPHAR/BPS and Probes&Drugs) to assemble a consensus compound/bioactivity dataset comprising 1144648 compounds with 10915362 bioactivities on 5613 targets (including defined macromolecular targets as well as cell-lines and phenotypic readouts). It also provides simplified information on assay types underlying the bioactivity data and on bioactivity confidence by comparing data from different sources. We have unified the source databases, brought them into a common format and combined them, enabling an ease for generic uses in multiple applications such as chemogenomics and data-driven drug design. The consensus dataset provides increased target coverage and contains a higher number of molecules compared to the source databases which is also evident from a larger number of scaffolds. These features render the consensus dataset a valuable tool for machine learning and other data-driven applications in (de novo) drug design and bioactivity prediction. The increased chemical and bioactivity coverage of the consensus dataset may improve robustness of such models compared to the single source databases. In addition, semi-automated structure and bioactivity annotation checks with flags for divergent data from different sources may help data selection and further accurate curation. This dataset belongs to the publication: https://doi.org/10.3390/molecules27082513 Structure and content of the dataset Dataset structure ChEMBL ID PubChem ID IUPHAR ID Target Activity type Assay type Unit Mean C (0) ... Mean PC (0) ... Mean B (0) ... Mean I (0) ... Mean PD (0) ... Activity check annotation Ligand names Canonical SMILES C ... Structure check (Tanimoto) Source The dataset was created using the Konstanz Information Miner (KNIME) (https://www.knime.com/) and was exported as a CSV-file and a compressed CSV-file. Except for the canonical SMILES columns, all columns are filled with the datatype ‘string’. The datatype for the canonical SMILES columns is the smiles-format. We recommend the File Reader node for using the dataset in KNIME. With the help of this node the data types of the columns can be adjusted exactly. In addition, only this node can read the compressed format. Column content: ChEMBL ID, PubChem ID, IUPHAR ID: chemical identifier of the databases Target: biological target of the molecule expressed as the HGNC gene symbol Activity type: for example, pIC50 Assay type: Simplification/Classification of the assay into cell-free, cellular, functional and unspecified Unit: unit of bioactivity measurement Mean columns of the databases: mean of bioactivity values or activity comments denoted with the frequency of their occurrence in the database, e.g. Mean C = 7.5 *(15) -> the value for this compound-target pair occurs 15 times in ChEMBL database Activity check annotation: a bioactivity check was performed by comparing values from the different sources and adding an activity check annotation to provide automated activity validation for additional confidence no comment: bioactivity values are within one log unit; check activity data: bioactivity values are not within one log unit; only one data point: only one value was available, no comparison and no range calculated; no activity value: no precise numeric activity value was available; no log-value could be calculated: no negative decadic logarithm could be calculated, e.g., because the reported unit was not a compound concentration Ligand names: all unique names contained in the five source databases are listed Canonical SMILES columns: Molecular structure of the compound from each database Structure check (Tanimoto): To denote matching or differing compound structures in different source databases match: molecule structures are the same between different sources; no match: the structures differ. We calculated the Jaccard-Tanimoto similarity coefficient from Morgan Fingerprints to reveal true differences between sources and reported the minimum value; 1 structure: no structure comparison is possible, because there was only one structure available; no structure: no structure comparison is possible, because there was no structure available. Source: From which databases the data come from

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

About this dataset

Context

The dataset tabulates the Big Stone City population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Big Stone City.

Content

The dataset constitues the following two datasets across these two themes

• Big Stone City, SD Population Breakdown by Gender
• Big Stone City, SD Population Breakdown by Gender and Age

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/.

Context

NYC Open Data is an opportunity to engage New Yorkers in the information that is produced and used by City government. We believe that every New Yorker can benefit from Open Data, and Open Data can benefit from every New Yorker. Source: https://opendata.cityofnewyork.us/overview/

Content

Thanks to NYC Open Data, which makes public data generated by city agencies available for public use, and Citi Bike, we've incorporated over 150 GB of data in 5 open datasets into Google BigQuery Public Datasets, including:

• Over 8 million 311 service requests from 2012-2016

• More than 1 million motor vehicle collisions 2012-present

• Citi Bike stations and 30 million Citi Bike trips 2013-present

• Over 1 billion Yellow and Green Taxi rides from 2009-present

• Over 500,000 sidewalk trees surveyed decennially in 1995, 2005, and 2015

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

https://opendata.cityofnewyork.us/

https://cloud.google.com/blog/big-data/2017/01/new-york-city-public-datasets-now-available-on-google-bigquery

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://data.cityofnewyork.us/ - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

By accessing datasets and feeds available through NYC Open Data, the user agrees to all of the Terms of Use of NYC.gov as well as the Privacy Policy for NYC.gov. The user also agrees to any additional terms of use defined by the agencies, bureaus, and offices providing data. Public data sets made available on NYC Open Data are provided for informational purposes. The City does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set made available on NYC Open Data, nor are any such warranties to be implied or inferred with respect to the public data sets furnished therein.

The City is not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set, or application utilizing such data set, provided by any third party.

Banner Photo by @bicadmedia from Unplash.

Inspiration

On which New York City streets are you most likely to find a loud party?

Can you find the Virginia Pines in New York City?

Where was the only collision caused by an animal that injured a cyclist?

What’s the Citi Bike record for the Longest Distance in the Shortest Time (on a route with at least 100 rides)?

https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png

Snapshot img

Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the integration of artificial intelligence (AI) and machine learning (ML). This enhancement enables more advanced data analysis and prediction capabilities, making data science platforms an essential tool for businesses seeking to gain insights from their data. Another trend shaping the market is the emergence of containerization and microservices in platforms. This development offers increased flexibility and scalability, allowing organizations to efficiently manage their projects. 
However, the use of platforms also presents challenges, particularly In the area of data privacy and security. Ensuring the protection of sensitive data is crucial for businesses, and platforms must provide strong security measures to mitigate risks. In summary, the market is witnessing substantial growth due to the integration of AI and ML technologies, containerization, and microservices, while data privacy and security remain key challenges.

What will be the Size of the Data Science Platform Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for advanced data analysis capabilities in various industries. Cloud-based solutions are gaining popularity as they offer scalability, flexibility, and cost savings. The market encompasses the entire project life cycle, from data acquisition and preparation to model development, training, and distribution. Big data, IoT, multimedia, machine data, consumer data, and business data are prime sources fueling this market's expansion. Unstructured data, previously challenging to process, is now being effectively managed through tools and software. Relational databases and machine learning models are integral components of platforms, enabling data exploration, preprocessing, and visualization.
Moreover, Artificial intelligence (AI) and machine learning (ML) technologies are essential for handling complex workflows, including data cleaning, model development, and model distribution. Data scientists benefit from these platforms by streamlining their tasks, improving productivity, and ensuring accurate and efficient model training. The market is expected to continue its growth trajectory as businesses increasingly recognize the value of data-driven insights.

How is this Data Science Platform Industry segmented and which is the largest segment?

The 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.

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    India
    Japan


  South America

    Brazil


  Middle East and Africa

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

On-premises deployment is a traditional method for implementing technology solutions within an organization. This approach involves purchasing software with a one-time license fee and a service contract. On-premises solutions offer enhanced security, as they keep user credentials and data within the company's premises. They can be customized to meet specific business requirements, allowing for quick adaptation. On-premises deployment eliminates the need for third-party providers to manage and secure data, ensuring data privacy and confidentiality. Additionally, it enables rapid and easy data access, and keeps IP addresses and data confidential. This deployment model is particularly beneficial for businesses dealing with sensitive data, such as those in manufacturing and large enterprises. While cloud-based solutions offer flexibility and cost savings, on-premises deployment remains a popular choice for organizations prioritizing data security and control.

Get a glance at the Data Science Platform Industry report of share of various segments. Request Free Sample

The on-premises segment was valued at USD 38.70 million in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 48% to the growth of the global market during the forecast period.

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

For more insights on the market share of various regions, Request F

The global big data in healthcare market size is estimated to grow from USD 78 billion in 2024 to USD 540 billion by 2035, representing a CAGR of 19.20% during the forecast period till 2035.

About this dataset

Context

The dataset tabulates the Big Stone City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Big Stone City. The dataset can be utilized to understand the population distribution of Big Stone City by age. For example, using this dataset, we can identify the largest age group in Big Stone City.

Key observations

The largest age group in Big Stone City, SD was for the group of age 75 to 79 years years with a population of 115 (18.88%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Big Stone City, SD was the 5 to 9 years years with a population of 3 (0.49%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Big Stone City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Big Stone City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Big Stone City Population by Age. You can refer the same here

BigFreeTailedBatOverallRange is an ESRI SDE Feature Class encompassing the observed and predicted range of a population of Big Free-tailed Bats in Colorado. This information was derived from species experts. A variety of data capture techniques were used including implementation of the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing at various scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Various sources were referenced in developing these data including areas delineated as 50% or higher predicted occupancy as modeled in MaxEnt using various Colorado bat site collection records, telemetry, historic records noted in Armstrong et al. 1994, and CPW Scientific Collection data.This generalized graphic representation of species range data is provided for informational purposes only and has not been prepared for, nor is it suitable for, any type of legal, regulatory, or site specific planning purposes. These data are subject to errors and change. Users of the information displayed in this map service are strongly cautioned to verify all information and contact local CPW Biologists before making any decisions.These data were last updated in January 2019.

Market Overview:

United States digital twin market size is projected to exhibit a growth rate (CAGR) of 33.86% during 2024-2032. The increasing availability of cloud computing resources and improved connectivity, which allows for the storage, processing, and sharing of large volumes of data, making it feasible to implement and scale digital twin applications, is driving the market.

A digital twin is a virtual representation of a physical object, system, or process that enables real-time monitoring, analysis, and simulation. It integrates data from various sources, such as sensors, IoT devices, and historical records, to create a dynamic and detailed digital counterpart. This virtual model allows for a comprehensive understanding of the physical entity's behavior, performance, and conditions. Digital twins are employed across diverse fields, including manufacturing, healthcare, and infrastructure, to optimize operations, predict potential issues, and facilitate informed decision-making. By mirroring their real-world counterpart, digital twins enhance efficiency, enable predictive maintenance, and support innovation by providing a holistic view that aids in design improvements and problem-solving.

United States Digital Twin Market Trends:

The digital twin market in the United States is experiencing robust growth, driven by a confluence of factors that underscore its transformative potential. Firstly, the escalating demand for efficient and optimized processes across industries has propelled the adoption of digital twin technology. As companies seek to enhance operational performance and minimize downtime, the digital twin's ability to simulate real-world scenarios becomes indispensable. Moreover, the rise of the IoT has significantly contributed to the surge in digital twin implementation. The seamless integration of IoT devices with digital twins enables real-time data acquisition, fostering a dynamic and responsive ecosystem. This interconnectedness enhances predictive maintenance capabilities, enabling businesses to preemptively address issues before they escalate. Additionally, advancements in artificial intelligence (AI) and machine learning (ML) play a pivotal role in driving the digital twin market forward. These technologies empower digital twins to evolve beyond mere replicas, becoming intelligent entities capable of autonomous decision-making. As AI algorithms continue to refine and learn from data inputs, the digital twin's analytical capabilities become increasingly sophisticated, offering unparalleled insights into system behavior and performance. In conclusion, the confluence of efficiency demands, IoT proliferation, and advancements in AI and ML collectively propel the digital twin market in the United States, making it a cornerstone in the era of Industry 4.0.

United States Digital Twin Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2024-2032. Our report has categorized the market based on type, technology, and end use.

Type Insights:

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• Product Digital Twin
• Process Digital Twin
• System Digital Twin
  

The report has provided a detailed breakup and analysis of the market based on the type. This includes product digital twin, process digital twin, and system digital twin.

Technology Insights:

• IoT and IIoT
• Blockchain
• Artificial Intelligence and Machine Learning
• Augmented Reality, Virtual Reality and Mixed Reality
• Big Data Analytics
• 5G
  

A detailed breakup and analysis of the market based on the technology have also been provided in the report. This includes IoT and IIoT, blockchain, artificial intelligence and machine learning, augmented reality, virtual reality and mixed reality, big data analytics, and 5G.

End Use Insights:

• Aerospace and Defense
• Automotive and Transportation
• Healthcare
• Energy and Utilities
• Oil and Gas
• Agriculture
• Residential and Commercial
• Retail and Consumer Goods
• Telecommunication
• Others
  

The report has provided a detailed breakup and analysis of the market based on the end use. This includes aerospace and defense, automotive and transportation, healthcare, energy and utilities, oil and gas, agriculture, residential and commercial, retail and consumer goods, telecommunication, and others.

Regional Insights:

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• Northeast
• Midwest
• South
• West
  

The report has also provided a comprehensive analysis of all the major regional markets, which include Northeast, Midwest, South, and West.

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape in the market. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

United States Digital Twin Market Report Coverage:

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

The Digital Geologic-GIS Map of the Big Pine 15' Quadrangle, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (bigp_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (bigp_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (bigp_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (seki_manz_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (seki_manz_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (bigp_geology_metadata_faq.pdf). Please read the seki_manz_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (bigp_geology_metadata.txt or bigp_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

About this dataset

Context

The dataset tabulates the Big Flat population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Big Flat. The dataset can be utilized to understand the population distribution of Big Flat by age. For example, using this dataset, we can identify the largest age group in Big Flat.

Key observations

The largest age group in Big Flat, AR was for the group of age 15 to 19 years years with a population of 16 (25.81%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Big Flat, AR was the 5 to 9 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Big Flat is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Big Flat total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Big Flat Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Big Sandy population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Big Sandy. The dataset can be utilized to understand the population distribution of Big Sandy by age. For example, using this dataset, we can identify the largest age group in Big Sandy.

Key observations

The largest age group in Big Sandy, MT was for the group of age 5 to 9 years years with a population of 90 (10.66%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Big Sandy, MT was the 85 years and over years with a population of 7 (0.83%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Big Sandy is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Big Sandy total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Big Sandy Population by Age. You can refer the same here

Snapshot img

Analytics As A Service Market Size 2024-2028

The analytics as a service market size is forecast to increase by USD 61.6 billion at a CAGR of 30.08% between 2023 and 2028.

The market is experiencing significant growth due to several key factors. The increasing availability and complexity of data are driving businesses to adopt AaaS solutions for gaining valuable insights. Additionally, the rising use of Internet of Things (IoT) analytics in enterprises is contributing to market expansion. However, data privacy and security concerns remain a challenge for AaaS providers, necessitating robust security measures to protect sensitive information.
These trends and challenges are shaping the future growth of the AaaS market. Organizations in North America are increasingly adopting AaaS solutions to gain a competitive edge by leveraging data-driven insights for informed decision-making. The market is expected to continue its growth trajectory, offering numerous opportunities for companies and investors alike.

What will be the Size of the Analytics As A Service (Aaas) Market During the Forecast Period?

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The market continues to experience robust growth, fueled by the increasing demand for advanced analytic techniques to derive insights from big data. Digital transformation initiatives across various industries drive the adoption of AaaS solutions, enabling real-time analytics, data reporting, and data democratization. Big data from IoT devices and artificial intelligence (AI) and machine learning (ML) technologies are key drivers, requiring advanced query accelerators and data connectivity to multi-cloud environments. Data volumes continue to expand, necessitating data integration and data accuracy, while real-time analytics and flexibility are essential for businesses to remain competitive. Generative AI and data reporting offer new opportunities for gaining valuable insights, but also present challenges related to data privacy, security, and data volumes.
Technological players In the AaaS market are addressing these complexities through robust encryption, compliance capabilities, and advanced analytic techniques. Overall, the AaaS market is expected to grow significantly, providing valuable solutions for businesses seeking to harness the power of their data.

How is this Analytics As A Service (Aaas) Industry segmented and which is the largest segment?

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

Type

  Predictive analytics
  Prescriptive analytics
  Diagnostic analytics
  Descriptive analytics


End-user

  BSFI
  Manufacturing
  Retail
  Healthcare
  Others


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  Middle East and Africa



  South America

By Type Insights

The predictive analytics segment is estimated to witness significant growth during the forecast period. Predictive analytics, a subset of advanced analytics, utilizes artificial intelligence (AI) and machine learning techniques to make future predictions and assessments based on historical data. The adoption of predictive analytics is on the rise in various sectors, including finance, retail, healthcare, and manufacturing, driving market growth. Enterprises worldwide are implementing predictive analytics to enhance productivity, mitigate risks, boost customer engagement, and minimize errors, leading to superior business outcomes. The proliferation of cloud computing and AI technology is further fueling the segment's expansion. Predictive analytics enables businesses to optimize processes, make informed decisions, and identify new opportunities in real-time. Big Data, IoT devices, and multiple data sources add to the complexity of data integration, requiring scalable and flexible analytics solutions.

AI and machine learning capabilities, such as advanced query accelerator, SAS Analytics, BigQuery, and Generative AI, are essential for handling large data volumes and diverse data formats. Data privacy, security, and compliance capabilities are also critical considerations for AaaS solutions. The data analytics market is expected to grow at a compound annual rate during the forecast period, driven by automation, optimization of processes, and the increasing use of machine learning technologies in various industries.

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The Predictive analytics segment was valued at USD 4.68 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 34% to the growth of the global market during the forecast period. Technavio's an