Note:- Only publicly available data can be worked upon

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APISCRAPY's forte lies in its ability to unearth valuable Ecommerce market data. We recognize that understanding the market dynamics, trends, and fluctuations is essential for making informed decisions.

APISCRAPY's AI-driven ecommerce data scraping service presents several advantages for individuals and businesses seeking comprehensive insights into the ecommerce market. Here are key benefits associated with their advanced data extraction technology:

1. Ecommerce Product Data: APISCRAPY's AI-driven approach ensures the extraction of detailed Ecommerce Product Data, including product specifications, images, and pricing information. This comprehensive data is valuable for market analysis and strategic decision-making.

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Brazilian E-Commerce Public Dataset by Olist

Welcome! This is a Brazilian ecommerce public dataset of orders made at Olist Store. The dataset has information of 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil. Its features allows viewing an order from multiple dimensions: from order status, price, payment and freight performance to customer location, product attributes and finally reviews written by customers. We also released a geolocation dataset that relates Brazilian zip codes to lat/lng coordinates.

This is real commercial data, it has been anonymised, and references to the companies and partners in the review text have been replaced with the names of Game of Thrones great houses.

Join it With the Marketing Funnel by Olist

We have also released a Marketing Funnel Dataset. You may join both datasets and see an order from Marketing perspective now!

Instructions on joining are available on this Kernel.

Context

This dataset was generously provided by Olist, the largest department store in Brazilian marketplaces. Olist connects small businesses from all over Brazil to channels without hassle and with a single contract. Those merchants are able to sell their products through the Olist Store and ship them directly to the customers using Olist logistics partners. See more on our website: www.olist.com

After a customer purchases the product from Olist Store a seller gets notified to fulfill that order. Once the customer receives the product, or the estimated delivery date is due, the customer gets a satisfaction survey by email where he can give a note for the purchase experience and write down some comments.

Attention

1. An order might have multiple items.
2. Each item might be fulfilled by a distinct seller.
3. All text identifying stores and partners where replaced by the names of Game of Thrones great houses.

Example of a product listing on a marketplace

https://i.imgur.com/JuJMns1.png" alt="Example of a product listing on a marketplace">

Data Schema

The data is divided in multiple datasets for better understanding and organization. Please refer to the following data schema when working with it: https://i.imgur.com/HRhd2Y0.png" alt="Data Schema">

Classified Dataset

We had previously released a classified dataset, but we removed it at Version 6. We intend to release it again as a new dataset with a new data schema. While we don't finish it, you may use the classified dataset available at the Version 5 or previous.

Inspiration

Here are some inspiration for possible outcomes from this dataset.

NLP:
This dataset offers a supreme environment to parse out the reviews text through its multiple dimensions.

Clustering:
Some customers didn't write a review. But why are they happy or mad?

Sales Prediction:
With purchase date information you'll be able to predict future sales.

Delivery Performance:
You will also be able to work through delivery performance and find ways to optimize delivery times.

Product Quality:
Enjoy yourself discovering the products categories that are more prone to customer insatisfaction.

Feature Engineering:
Create features from this rich dataset or attach some external public information to it.

Acknowledgements

Thanks to Olist for releasing this dataset.

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

https://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

https://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

https://user-images.githubusercontent.com/91852182/145270229-514f0983-3bbb-4cd3-be64-980e92656a02.png"> https://user-images.githubusercontent.com/91852182/145270251-6f6f6472-8817-435c-a995-9bc4bfef10d1.png">

After we will clear our data frame, will remove missing values.

https://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

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 increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This fusion enables organizations to derive deeper insights from their data, fueling business innovation and decision-making. Another trend shaping the market is the emergence of containerization and microservices in data science platforms. This approach offers enhanced flexibility, scalability, and efficiency, making it an attractive choice for businesses seeking to streamline their data science operations. However, the market also faces challenges. Data privacy and security remain critical concerns, with the increasing volume and complexity of data posing significant risks. Ensuring robust data security and privacy measures is essential for companies to maintain customer trust and comply with regulatory requirements. Additionally, managing the complexity of data science platforms and ensuring seamless integration with existing systems can be a daunting task, requiring significant investment in resources and expertise. Companies must navigate these challenges effectively to capitalize on the market's opportunities and stay competitive in the rapidly evolving data landscape.

What will be the Size of the Data Science Platform 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.
Request Free SampleThe market continues to evolve, driven by the increasing demand for advanced analytics and artificial intelligence solutions across various sectors. Real-time analytics and classification models are at the forefront of this evolution, with APIs integrations enabling seamless implementation. Deep learning and model deployment are crucial components, powering applications such as fraud detection and customer segmentation. Data science platforms provide essential tools for data cleaning and data transformation, ensuring data integrity for big data analytics. Feature engineering and data visualization facilitate model training and evaluation, while data security and data governance ensure data privacy and compliance. Machine learning algorithms, including regression models and clustering models, are integral to predictive modeling and anomaly detection. Statistical analysis and time series analysis provide valuable insights, while ETL processes streamline data integration. Cloud computing enables scalability and cost savings, while risk management and algorithm selection optimize model performance. Natural language processing and sentiment analysis offer new opportunities for data storytelling and computer vision. Supply chain optimization and recommendation engines are among the latest applications of data science platforms, demonstrating their versatility and continuous value proposition. Data mining and data warehousing provide the foundation for these advanced analytics capabilities.

How is this Data Science Platform Industry segmented?

The data science platform 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. DeploymentOn-premisesCloudComponentPlatformServicesEnd-userBFSIRetail and e-commerceManufacturingMedia and entertainmentOthersSectorLarge enterprisesSMEsApplicationData PreparationData VisualizationMachine LearningPredictive AnalyticsData GovernanceOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the dynamic the market, businesses increasingly adopt solutions to gain real-time insights from their data, enabling them to make informed decisions. Classification models and deep learning algorithms are integral parts of these platforms, providing capabilities for fraud detection, customer segmentation, and predictive modeling. API integrations facilitate seamless data exchange between systems, while data security measures ensure the protection of valuable business information. Big data analytics and feature engineering are essential for deriving meaningful insights from vast datasets. Data transformation, data mining, and statistical analysis are crucial processes in data preparation and discovery. Machine learning models, including regression and clustering, are employed for model training and evaluation. Time series analysis and natural language processing are valuable tools for understanding trends and customer sen

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

Alternative Data Market size was valued at USD 16.13 Billion in 2024 and is projected to reach USD 408.72 Billion by 2031, growing at a CAGR of 54.92% from 2024 to 2031.

Global Alternative Data Market Drivers

Growing Need for Alpha Generation: Investors are continuously looking for fresh sources of alpha, or excess returns over a benchmark, in the fiercely competitive financial markets. Insights from alternative data are distinct from those from traditional sources, which helps investors spot opportunities and obtain a competitive advantage. Technological Developments: The mass gathering and examination of alternative data has been made easier by technological developments, especially in fields like artificial intelligence, machine learning, and big data analytics. These technologies improve the value proposition of alternative data for investors by enabling complex data processing, pattern detection, and predictive modeling. Proliferation of Data Sources: Beyond traditional financial and economic indicators, there is a proliferation of data sources due to the internet and digital technology. Web traffic, satellite imagery, social media feeds, consumer transactions, and sensor data are just a few examples of the many sources that make up alternative data, which offers deep and varied insights into a number of fields and industries. Regulatory Environment: The gathering, storing, and use of alternative data may be affected by changes in regulations, such as the General Data Protection Regulation (GDPR) of the European Union and other comparable data protection legislation across the globe. Adherence to regulatory mandates is crucial for alternative data providers and consumers, as it molds the market environment and impacts data procurement tactics.

Metrics from individual Marketplaces during the current reporting period. The report includes data for the states using HealthCare.gov. As of August 2024, CMS is no longer releasing the “HealthCare.gov” metrics. Historical data between July 2023-July 2024 will remain available. The “HealthCare.gov Transitions” metrics, which are the CAA, 2023 required metrics, will continue to be released. Sources: HealthCare.gov application and policy data through May 5, 2024, and T-MSIS Analytic Files (TAF) through March 2024 (TAF version 7.1 with T-MSIS enrollment through the end of March 2024). Data include consumers in HealthCare.gov states where the first unwinding renewal cohort is due on or after the end of reporting month (state identification based on HealthCare.gov policy and application data). State data start being reported in the month when the state's first unwinding renewal cohort is due. April data include Arizona, Arkansas, Florida, Indiana, Iowa, Kansas, Nebraska, New Hampshire, Ohio, Oklahoma, South Dakota, Utah, West Virginia, and Wyoming. May data include the previous states and the following new states: Alaska, Delaware, Georgia, Hawaii, Montana, North Dakota, South Carolina, Texas, and Virginia. June data include the previous states and the following new states: Alabama, Illinois, Louisiana, Michigan, Missouri, Mississippi, North Carolina, Tennessee, and Wisconsin. July data include the previous states and Oregon. All HealthCare.gov states are included in this version of the report. Notes: This table includes Marketplace consumers who: 1) submitted a HealthCare.gov application on or after the start of each state’s first reporting month; and 2) who can be linked to an enrollment record in TAF that shows Medicaid or CHIP enrollment between March 2023 and the latest reporting month. Cumulative counts show the number of unique consumers from the included population who had a Marketplace application submitted or a HealthCare.gov Marketplace policy on or after the start of each state’s first reporting month through the latest reporting month. Net counts show the difference between the cumulative counts through a given reporting month and previous reporting months. The data used to produce the metrics are organized by week. Reporting months start on the first Monday of the month and end on the first Sunday of the next month when the last day of the reporting month is not a Sunday. For example, the April 2023 reporting period extends from Monday, April 3 through Sunday, April 30. Data are preliminary and will be restated over time to reflect consumers most recent HealthCare.gov status. Data may change as states resubmit T-MSIS data or data quality issues are identified. Data do not represent Marketplace consumers who had a confirmed Medicaid/CHIP loss. Future reporting will look at coverage transitions for people who lost Medicaid/CHIP. See the data and methodology documentation for a full description of the data sources, measure definitions, and general data limitations. Data notes: Virginia operated a Federally Facilitated Exchange (FFE) on the HealthCare.gov platform during 2023. In 2024, the state started operating a State Based Marketplace (SBM) platform. This table only includes data on 2023 applications and policies obtained through the HealthCare.gov Marketplace. Due to limited Marketplace activity on the HealthCare.gov platform in December 2023, data from December 2023 onward are excluded. The cumulative count and percentage for Virginia and the HealthCare.gov total reflect Virginia data from April 2023 through November 2023. The report may include negative 'net counts,' which reflect that there were cumulatively fewer counts from one month to the next. Wyoming has negative ‘net counts’ for most of its metrics in March 2024, including 'Marketplace Consumers with Previous M

The global Big Data in E-commerce market is experiencing robust growth, driven by the increasing volume of e-commerce transactions and the need for businesses to leverage data for improved decision-making, personalized marketing, and enhanced customer experiences. Let's assume, for illustrative purposes, a 2025 market size of $50 billion and a Compound Annual Growth Rate (CAGR) of 15% for the forecast period 2025-2033. This implies significant expansion, reaching an estimated market value of approximately $150 billion by 2033. Key drivers include the proliferation of mobile commerce, the rise of omnichannel strategies, and the increasing adoption of advanced analytics technologies like AI and machine learning to extract actionable insights from vast datasets. Furthermore, the growing demand for real-time data processing and predictive analytics for inventory management, fraud detection, and personalized recommendations fuels this expansion. While data security concerns and the complexity of implementing Big Data solutions present challenges, the overall market trajectory indicates a promising future for Big Data applications in the e-commerce sector. The competitive landscape comprises established technology giants like Amazon Web Services, Microsoft, and IBM, alongside specialized Big Data analytics providers, creating a dynamic market with opportunities for innovation and consolidation. The segment analysis (specific segments not provided) is crucial for identifying high-growth areas within this market. For example, segments focused on real-time analytics for customer experience or AI-powered predictive modeling for marketing campaigns are likely to witness particularly strong growth. Regional variations in e-commerce adoption and technological infrastructure also influence market dynamics. North America and Europe currently hold substantial market share but regions like Asia-Pacific are showing rapid growth potential, due to the expanding e-commerce ecosystem and increasing digital literacy. The continued development and refinement of Big Data technologies, coupled with the growing sophistication of e-commerce businesses in utilizing data-driven strategies, will ensure a sustained expansion of this market in the coming years.

The Data Monetization Market size was valued at USD 2.99 USD Billion in 2023 and is projected to reach USD 8.76 USD Billion by 2032, exhibiting a CAGR of 16.6 % during the forecast period. Data Monetization refers to the process of using data to obtain quantifiable economic benefit. Internal or indirect methods include using data to make measurable business performance improvements and inform decisions. External or direct methods include data sharing to gain beneficial terms or conditions from business partners, information bartering, selling data outright (via a data broker or independently), or offering information products and services (for example, including information as a value-added component of an existing offering). Data can be sold in its raw form or in a form that already incorporates insights and analysis. A simple example of direct data monetization is contact lists of potential business prospects that influence buyers’ businesses or trades. Indirect or internal data monetization involves making measurable business performance improvements and informed decisions using data. Recent developments include: October 2023 – Narrative I/O collaborated with Snowflake to display data within the Trade Desk through the Snowflake Marketplace. It helps create effective, data-driven digital media campaigns. This initiative by Narrative and Snowflake will provide joint customers with a cost-effective solution for activating and distributing audiences from the Snowflake Marketplace., January 2023 – Inbounds.com completed the acquisition of Data Prosper, which provides data list management services, data prosper, data brokerage, and data monetization, among others. This acquisition resulted in enhancements to inbound data analytics and improvement in the client’s return on investments to boost business growth., December 2022 – Wipro launched Wipro Data Intelligence Suite, which is a solution that provides advanced functionalities concerning monetization and cloud modernization. With the additional cloud support, the company aimed to boost digital transformation and enable business growth., March 2022 – Silverback United Inc. completed the acquisition of Darwin Data Capital Inc., which was a provider of data-backed asset solutions and products with major end-users of large industries. This acquisition resulted in a geographical expansion in the U.S., July 2021 – Axia collaborated with UDBI to monetize data and enhance business growth. This collaboration resulted in internal and external monetization of data to improve the consumer experience by implementing personalized marketing and enhancing the consumer experience.. Key drivers for this market are: Increasing Practice of Data Generation and Data Collection within Businesses to Drive the Market Growth. Potential restraints include: Fewer Skilled Workforce and Talent Management Issues to Limit the Market Growth. Notable trends are: Increasing Demand for Private Data and Analytics by Emerging Industries to Fuel Market Growth.

B2B Data Exchange Market size was valued at USD 15 Billion in 2024 and is projected to reach USD 32.15 Billion by 2031, growing at a CAGR of 10% from 2024 to 2031.

The B2B Data Exchange Market is primarily driven by the increasing need for efficient and secure data sharing between businesses. The growing complexity of supply chains, the rise of digital transformation initiatives, and the increasing regulatory compliance requirements are fueling the demand for robust data exchange solutions. Additionally, the advancements in data integration and interoperability technologies are enabling seamless data flow between different systems and organizations. Furthermore, the increasing adoption of cloud-based solutions and API-driven architectures is further propelling the growth of the B2B Data Exchange Market.

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

With 56 Million Businesses in the United States of America, Techsalerator has access to the highest B2B count of Data/ Business Data in the country.

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

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

We cover all states and cities in the country : Example covered.

All states :

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho IllinoisIndiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri MontanaNebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon PennsylvaniaRhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

A few cities : New York City NY Los Angeles CA Chicago IL Houston TX Phoenix AZ Philadelphia PA San Antonio TX San Diego CA Dallas TX Austin TX San Jose CA Fort Worth TX Jacksonville FL Columbus OH Charlotte NC Indianapolis IN San Francisco CA Seattle WA Denver CO Washington DC Boston MA El Paso TX Nashville TN Oklahoma City OK Las Vegas NV Detroit MI Portland OR Memphis TN Louisville KY Milwaukee WI Baltimore MD Albuquerque NM Tucson AZ Mesa AZ Fresno CA Sacramento CA Atlanta GA Kansas City MO Colorado Springs CO Raleigh NC Omaha NE Miami FL Long Beach CA Virginia Beach VA Oakland CA Minneapolis MN Tampa FL Tulsa OK Arlington TX Wichita KS Bakersfield CA Aurora CO New Orleans LA Cleveland OH Anaheim CA Henderson NV Honolulu HI Riverside CA Santa Ana CA Corpus Christi TX Lexington KY San Juan PR Stockton CA St. Paul MN Cincinnati OH Greensboro NC Pittsburgh PA Irvine CA St. Louis MO Lincoln NE Orlando FL Durham NC Plano TX Anchorage AK Newark NJ Chula Vista CA Fort Wayne IN Chandler AZ Toledo OH St. Petersburg FL Reno NV Laredo TX Scottsdale AZ North Las Vegas NV Lubbock TX Madison WI Gilbert AZ Jersey City NJ Glendale AZ Buffalo NY Winston-Salem NC Chesapeake VA Fremont CA Norfolk VA Irving TX Garland TX Paradise NV Arlington VA Richmond VA Hialeah FL Boise ID Spokane WA Frisco TX Moreno Valley CA Tacoma WA Fontana CA Modesto CA Baton Rouge LA Port St. Lucie FL San Bernardino CA McKinney TX Fayetteville NC Santa Clarita CA Des Moines IA Oxnard CA Birmingham AL Spring Valley NV Huntsville AL Rochester NY Cape Coral FL Tempe AZ Grand Rapids MI Yonkers NY Overland Park KS Salt Lake City UT Amarillo TX Augusta GA Columbus GA Tallahassee FL Montgomery AL Huntington Beach CA Akron OH Little Rock AR Glendale CA Grand Prairie TX Aurora IL Sunrise Manor NV Ontario CA Sioux Falls SD Knoxville TN Vancouver WA Mobile AL Worcester MA Chattanooga TN Brownsville TX Peoria AZ Fort Lauderdale FL Shreveport LA Newport News VA Providence RI Elk Grove CA Rancho Cucamonga CA Salem OR Pembroke Pines FL Santa Rosa CA Eugene OR Oceanside CA Cary NC Fort Collins CO Corona CA Enterprise NV Garden Grove CA Springfield MO Clarksville TN Bayamon PR Lakewood CO Alexandria VA Hayward CA Murfreesboro TN Killeen TX Hollywood FL Lancaster CA Salinas CA Jackson MS Midland TX Macon County GA Kansas City KS Palmdale CA Sunnyvale CA Springfield MA Escondido CA Pomona CA Bellevue WA Surprise AZ Naperville IL Pasadena TX Denton TX Roseville CA Joliet IL Thornton CO McAllen TX Paterson NJ Rockford IL Carrollton TX Bridgeport CT Miramar FL Round Rock TX Metairie LA Olathe KS Waco TX

With 6.2 Million Businesses in Spain , Techsalerator has access to the highest B2B count of Data/Business Data in the country.

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

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

At Techsalerator, we cover all regions and cities in Spain with Business Data.

A few example of regions in Spain : Andalusia Catalonia Community of Madrid Valencian Community Galicia Castile and León Basque Country Castilla-La Mancha Canary Islands Region of Murcia Aragon Extremadura Balearic Islands Asturias Navarre Cantabria La Rioja

A few examples of cities in Spain : Madrid Barcelona Valencia Sevilla Zaragoza Malaga Murcia Palma Las Palmas de Gran Canaria Bilbao Alicante Cordoba Valladolid Vigo Gijon Eixample L'Hospitalet de Llobregat Latina Carabanchel A Coruna Puente de Vallecas Sant Marti Gasteiz / Vitoria Granada Elche Ciudad Lineal Oviedo Santa Cruz de Tenerife Fuencarral-El Pardo Badalona Cartagena Terrassa Jerez de la Frontera Sabadell Mostoles Alcala de Henares Pamplona Fuenlabrada Almeria Leganes San Sebastian Sants-Montjuic Santander Castello de la Plana Burgos Albacete Horta-Guinardo Alcorcon Getafe Nou Barris Hortaleza San Blas-Canillejas Salamanca Tetuan de las Victorias Logrono La Laguna City Center Huelva Arganzuela Badajoz Sarria-Sant Gervasi Sant Andreu Salamanca Chamberi Usera Tarragona Chamartin Lleida Marbella Leon Villaverde Cadiz Retiro Dos Hermanas Mataro Gracia Santa Coloma de Gramenet Torrejon de Ardoz Jaen Moncloa-Aravaca Algeciras Parla Delicias Ourense Alcobendas Reus Moratalaz Ciutat Vella Torrevieja Telde Barakaldo Lugo San Fernando Girona Santiago de Compostela Caceres Lorca Coslada Talavera de la Reina El Puerto de Santa Maria Cornella de Llobregat Las Rozas de Madrid Orihuela Aviles El Ejido Guadalajara Roquetas de Mar Palencia Algorta Pozuelo de Alarcon Sant Boi de Llobregat Toledo Les Corts Pontevedra Getxo Gandia Sant Cugat del Valles Ceuta Arona Torrent Chiclana de la Frontera Manresa San Sebastian de los Reyes Ferrol Velez-Malaga Ciudad Real Mijas Melilla

The Synthetic Data Generation Marketsize was valued at USD 288.5 USD Million in 2023 and is projected to reach USD 1920.28 USD Million by 2032, exhibiting a CAGR of 31.1 % during the forecast period.Synthetic data generation stands for the generation of fake datasets that resemble real datasets with reference to their data distribution and patterns. It refers to the process of creating synthetic data points utilizing algorithms or models instead of conducting observations or surveys. There is one of its core advantages: it can maintain the statistical characteristics of the original data and remove the privacy risk of using real data. Further, with synthetic data, there is no limitation to how much data can be created, and hence, it can be used for extensive testing and training of machine learning models, unlike the case with conventional data, which may be highly regulated or limited in availability. It also helps in the generation of datasets that are comprehensive and include many examples of specific situations or contexts that may occur in practice for improving the AI system’s performance. The use of SDG significantly shortens the process of the development cycle, requiring less time and effort for data collection as well as annotation. It basically allows researchers and developers to be highly efficient in their discovery and development in specific domains like healthcare, finance, etc. Key drivers for this market are: Growing Demand for Data Privacy and Security to Fuel Market Growth. Potential restraints include: Lack of Data Accuracy and Realism Hinders Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

Healthcare Data Storage Market size was valued at USD 3.97 Billion in 2024 and is projected to reach USD 10.27 Billion by 2032, growing at a CAGR of 13.90% during the forecast period 2026-2032.Global Healthcare Data Storage Market DriversThe market drivers for the Healthcare Data Storage Market can be influenced by various factors. These may include:Growing volume of healthcare data: The amount of data produced by healthcare providers has increased dramatically as a result of the digitalization of medical records. This covers genomic information, medical imaging, electronic health records (EHRs), and more. To handle this data, healthcare institutions need effective and safe storage options.Severe laws and compliance requirements: HIPAA (Health Insurance Portability and Accountability Act) in the US and GDPR (General Data Protection Regulation) in Europe are two examples of the severe laws that apply to healthcare data. In order to protect patient information, these requirements mandate that healthcare organisations employ secure data storage solutions.Cloud storage is becoming more and more popular since it is affordable, flexible, and scalable, which appeals to healthcare institutions. Adoption is accelerated by cloud storage companies' provision of specialised healthcare cloud solutions that meet legal and regulatory standards.Technological developments: Artificial intelligence (AI), machine learning (ML), and big data analytics are some of the technologies that are revolutionising healthcare. To handle the massive volumes of data collected and analysed, these technologies need reliable data storage systems.Growing need for data interoperability: In order to enhance patient care coordination and results, healthcare providers are placing a greater emphasis on interoperability. This calls for the smooth transfer of medical data between various systems, which calls for trustworthy data storage options.Escalating healthcare expenses: There is pressure on healthcare institutions to save expenses without sacrificing care quality. Healthcare data management and storage operations can be made more cost-effective with the use of efficient data storage solutions.Growing comprehension of data security's significance Healthcare data breaches may result in severe repercussions, such as monetary losses and reputational harm. To safeguard patient data from online dangers, healthcare institutions are investing in secure data storage solutions.

According to Cognitive Market Research, the global Data Preparation Tools market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Volume of Data and Growing Adoption of Business Intelligence (BI) and Analytics Driving the Data Preparation Tools Market

As organizations grow more data-driven, the integration of data preparation tools with Business Intelligence (BI) and advanced analytics platforms is becoming a critical driver of market growth. Clean, well-structured data is the foundation for accurate analysis, predictive modeling, and data visualization. Without proper preparation, even the most advanced BI tools may deliver misleading or incomplete insights. Businesses are now realizing that to fully capitalize on the capabilities of BI solutions such as Power BI, Qlik, or Looker, their data must first be meticulously prepared. Data preparation tools bridge this gap by transforming disparate raw data sources into harmonized, analysis-ready datasets. In the financial services sector, for example, firms use data preparation tools to consolidate customer financial records, transaction logs, and third-party market feeds to generate real-time risk assessments and portfolio analyses. The seamless integration of these tools with analytics platforms enhances organizational decision-making and contributes to the widespread adoption of such solutions. The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into data preparation tools has significantly improved their efficiency and functionality. These technologies automate complex tasks like anomaly detection, data profiling, semantic enrichment, and even the suggestion of optimal transformation paths based on patterns in historical data. AI-driven data preparation not only speeds up workflows but also reduces errors and human bias. In May 2022, Alteryx introduced AiDIN, a generative AI engine embedded into its analytics cloud platform. This innovation allows users to automate insights generation and produce dynamic documentation of business processes, revolutionizing how businesses interpret and share data. Similarly, platforms like DataRobot integrate ML models into the data preparation stage to improve the quality of predictions and outcomes. These innovations are positioning data preparation tools as not just utilities but as integral components of the broader AI ecosystem, thereby driving further market expansion. Data preparation tools address these needs by offering robust solutions for data cleaning, transformation, and integration, enabling telecom and IT firms to derive real-time insights. For example, Bharti Airtel, one of India’s largest telecom providers, implemented AI-based data preparation tools to streamline customer data and automate insights generation, thereby improving customer support and reducing operational costs. As major market players continue to expand and evolve their services, the demand for advanced data analytics powered by efficient data preparation tools will only intensify, propelling market growth. The exponential growth in global data generation is another major catalyst for the rise in demand for data preparation tools. As organizations adopt digital technologies and connected devices proliferate, the volume of data produced has surged beyond what traditional tools can handle. This deluge of information necessitates modern solutions capable of preparing vast and complex datasets efficiently. According to a report by the Lin...

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Data quality tools market in APAC overview

The need to improve customer engagement is the primary factor driving the growth of data quality tools market in APAC. The reputation of a company gets hampered if there is a delay in product delivery or response to payment-related queries. To avoid such issues organizations are integrating their data with software such as CRM for effective communication with customers. To capitalize on market opportunities, organizations are adopting data quality strategies to perform accurate customer profiling and improve customer satisfaction.

Also, by using data quality tools, companies can ensure that targeted communications reach the right customers which will enable companies to take real-time action as per the requirements of the customer. Organizations use data quality tool to validate e-mails at the point of capture and clean their database of junk e-mail addresses. Thus, the need to improve customer engagement is driving the data quality tools market growth in APAC at a CAGR of close to 23% during the forecast period.

Top data quality tools companies in APAC covered in this report

The data quality tools market in APAC is highly concentrated. To help clients improve their revenue shares in the market, this research report provides an analysis of the market’s competitive landscape and offers information on the products offered by various leading companies. Additionally, this data quality tools market in APAC analysis report suggests strategies companies can follow and recommends key areas they should focus on, to make the most of upcoming growth opportunities.

The report offers a detailed analysis of several leading companies, including:

IBM
Informatica
Oracle
SAS Institute
Talend

Data quality tools market in APAC segmentation based on end-user

Banking, financial services, and insurance (BFSI)
Telecommunication
Retail
Healthcare
Others

BFSI was the largest end-user segment of the data quality tools market in APAC in 2018. The market share of this segment will continue to dominate the market throughout the next five years.

Data quality tools market in APAC segmentation based on region

China
Japan
Australia
Rest of Asia

China accounted for the largest data quality tools market share in APAC in 2018. This region will witness an increase in its market share and remain the market leader for the next five years.

Key highlights of the data quality tools market in APAC for the forecast years 2019-2023:

CAGR of the market during the forecast period 2019-2023
Detailed information on factors that will accelerate the growth of the data quality tools market in APAC during the next five years
Precise estimation of the data quality tools market size in APAC and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
The growth of the data quality tools market in APAC across China, Japan, Australia, and Rest of Asia
A thorough analysis of the market’s competitive landscape and detailed information on several vendors
Comprehensive details on factors that will challenge the growth of data quality tools companies in APAC

We can help! Our analysts can customize this market research report to meet your requirements. Get in touch

Data Fusion Solutions Market Outlook

The global data fusion solutions market size is anticipated to grow significantly from USD 10.2 billion in 2023 to USD 25.7 billion by 2032, with a compound annual growth rate (CAGR) of 11.2% during the forecast period. This robust growth is primarily driven by the increasing demand for real-time data analysis, the integration of advanced technologies such as AI and machine learning, and the rising need for comprehensive data management solutions across various industries.

One of the primary growth factors for the data fusion solutions market is the exponential increase in data generation and the subsequent need for effective data management and analysis tools. As businesses and government entities increasingly rely on data-driven decision-making, the ability to amalgamate diverse data sources into a coherent and actionable format becomes crucial. Technologies like IoT, AI, and machine learning are further augmenting this demand by enabling more sophisticated data fusion capabilities, thereby providing deeper insights and fostering innovation across sectors.

Another significant driver is the growing complexity and diversity of data types that organizations need to manage. Traditional data management systems are often inadequate for handling the vast volumes and varieties of data generated today. Data fusion solutions, which integrate data from multiple sources to produce more accurate and comprehensive information, are becoming essential. This is particularly true in industries such as healthcare, defense, and transportation, where timely and accurate data integration can lead to better outcomes and operational efficiencies.

The third major growth factor is the critical role of data fusion in enhancing security and surveillance systems. In the defense and surveillance sector, for example, data fusion technologies are employed to combine inputs from various sensors, cameras, and other sources to provide a complete situational awareness picture. This capability is not only vital for national security but also for public safety, traffic management, and disaster response. The growing investments in smart cities and intelligent transportation systems are further propelling the demand for advanced data fusion solutions.

Regionally, North America is expected to dominate the data fusion solutions market throughout the forecast period. This can be attributed to the high adoption rate of advanced technologies, significant investments in R&D, and the presence of major market players in the region. Europe and Asia Pacific are also anticipated to witness substantial growth, driven by technological advancements, increasing government initiatives, and the rapid expansion of industries such as healthcare, transportation, and defense in these regions.

Component Analysis

The data fusion solutions market is segmented by components into software, hardware, and services. The software segment is expected to hold the largest market share, driven by the increasing demand for advanced data analytics and management tools. These software solutions are versatile and can be tailored to meet the specific needs of various industries, thereby enhancing their appeal. Moreover, the integration of AI and machine learning technologies into data fusion software is providing more sophisticated and accurate data analysis capabilities, which is further fuelling market growth.

Hardware components, although not as dominant as software, still play a crucial role in the data fusion ecosystem. The hardware segment includes sensors, data storage devices, and processing units that are essential for collecting, storing, and analyzing vast amounts of data. Advances in sensor technology and the increasing deployment of IoT devices are driving the demand for more robust and high-performance hardware solutions. Additionally, the development of edge computing technologies is enhancing the capability of hardware to process data closer to the source, thereby reducing latency and improving real-time decision-making.

The services segment encompasses various support services such as consulting, implementation, and maintenance, which are vital for the successful deployment and operation of data fusion solutions. As businesses increasingly invest in data fusion technologies, the demand for specialized services to ensure seamless integration and optimal performance

Metrics from individual Marketplaces during the current reporting period. The report includes data for the states using State-based Marketplaces (SBMs) that use their own eligibility and enrollment platforms
Source: State-based Marketplace (SBM) operational data submitted to CMS. Each monthly reporting period occurs during the first through last day of the reported month. SBMs report relevant Marketplace activity from April 2023 (when unwinding-related renewals were initiated in most SBMs) through the end of a state’s Medicaid unwinding renewal period and processing timeline, which will vary by SBM. Some SBMs did not receive unwinding-related applications during reporting period months in April or May 2023 due to renewal processing timelines. SBMs that are no longer reporting Marketplace activity due to the completion of a state’s Medicaid unwinding renewal period are marked as NA. Some SBMs may revise data from a prior month and thus this data may not align with that previously reported. For April, Idaho’s reporting period was from February 1, 2023 to April 30, 2023.

Notes:

1. This table represents consumers whose Medicaid/CHIP coverage was denied or terminated following renewal and 1) whose applications were processed by an SBM through an integrated Medicaid, CHIP, and Marketplace eligibility system or 2) whose applications/information was sent by a state Medicaid or CHIP agency to an SBM through an account transfer process. Consumers who submitted applications to an SBM that can be matched to a Medicaid/CHIP record are also included. See the "Data Sources and Metrics Definition Overview" at http://www.medicaid.gov for a full description of the differences between the SBM operating systems and resulting data metrics, measure definitions, and general data limitations. As of the September 2023 report, this table was updated to differentiate between SBMs with an integrated Medicaid, CHIP, and Marketplace eligibility system and those with an account transfer process to better represent the percentage of QHP selections in relation to applicable consumers received and processed by the relevant SBM. State-specific variations are:
   - Maine’s data and Nevada’s April and May 2023 data report all applications with Medicaid/CHIP denials or terminations, not only those part of the annual renewal process. - Connecticut, Massachusetts, and Washington also report applications with consumers determined ineligible for Medicaid/CHIP due to procedural reasons. - Minnesota and New York report on eligibility and enrollment for their Basic Health Programs (BHP). Effective April 1, 2024, New York transitioned its BHP to a program operated under a section 1332 waiver, which expands eligibility to individuals with incomes up to 250% of FPL. As of the March 2024 data, New York reports on consumers with expanded eligibility and enrollment under the section 1332 waiver program in the BHP data. - Idaho’s April data on consumers eligible for a QHP with financial assistance do not depict a direct correlation to consumers with a QHP selection. - Virginia transitioned from using the HealthCare.gov platform in Plan Year 2023 to an SBM using its own eligibility and enrollment platform in Plan Year 2024. Virginia's data are reported in the HealthCare.gov and HeathCare.gov Transitions Marketplace Medicaid Unwinding Reports through the end of 2024 and is available in SBM reports as of the April 2024 report. Virginia's SBM data report all applications with Medicaid/CHIP denials or terminations, not only those part of the annual renewal process, and as a result are not directly comparable to their data in the HealthCare.gov data reports. - Only SBMs with an automatic plan assignment process have and report automatic QHP selections. These SBMs make automatic plan assignments into a QHP for a subset of individuals and provide a notification of options regarding active selection of an alternative plan and/or, if applicable, making the first month’s premium payment.
2. SBMs report on all applicable applications received and not on the Medicaid-defined cohorts of individuals whose renewal is due in a given month. The data in this table are not cumulative and count unique Marketplace activities during the reporting period month in which the relevant activity occurs. As such, activities by any one consumer may be included across reporting months. For example, a consumer who submits an application and receives a determination of QHP eligibility may be counted in one month but his/her plan selection could be counted in a later month. Thus, the percentages do not necessarily depict a direct correlation to the count of consumers on applications received during the current reporting month. Total counts represent activity across the reporting months and are not cumulative as of the latest reporting month. Updated applications in the reporting month are only counted once. Updated applications in a following month may be counted again in that applicable month only if the consumer obtains a new Medicaid/CHIP renewal.
3. SBMs have different operational processes and eligibility systems for handling QHP, Medicaid and CHIP eligibility determinations. While CMS works with SBMs to align the metric definitions across the Marketplaces there can be limitations and anomalies among the SBM data due to different SBM system capabilities. Additionally, variances in the data may be attributable to differences in how states are conducting unwinding renewals including processing timelines and whether states are staging applications for population cohorts (e.g., over age 65).

Percentages shown are of consumers on applications whose Medicaid/CHIP coverage was denied or terminated following renewal during the reporting period month.
Percentages shown are of consumers on account transfers whose Medicaid/CHIP coverage was denied or terminated following renewal during the reporting period month. The percentages for SBMs with integrated eligibility systems and the SBM Total - Integrated and SBM Total data record groups are marked as not available (NA) because SBMs with integrated eligibility systems do not receive account transfers. Additionally, some SBMs with account transfer processes adjusted, with guidance from CMS, the count of "Consumers on Account Transfers Associated with a Medicaid/CHIP Coverage Denial or Termination Following Renewal" to include all consumers on applications who are associated with a Medicaid/CHIP coverage denial or termination following renewal for purposes of calculating the Percent - Account Transfer data record group. For example, Pennsylvania conducts automatic QHP eligibility determinations for Marketplace plans for some account transfers and as of the December 2023 report, revised its data to report those consumers in the account transfer and application metrics. As of the April 2024 report, New Jersey revised its data to report consumers on account transfers and direct applications in the account transfer and application metrics to reflect overall SBM activity in the Percent - Account Transfer data record group.
APTC: Advance Premium Tax Credit; CHIP: Children's Health Insurance Program; QHP: Qualified Health Plan; BHP: Basic Health Program

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