CompanyData.com, powered by BoldData, delivers high-quality, verified B2B company information from official trade registers around the world. Our India company database includes 32,468,995 verified business records, giving you powerful insight into one of the fastest-growing economies on the planet.

Each company profile is rich with firmographic data, including company name, CIN (Corporate Identification Number), registration number, legal status, industry classification (NIC codes), revenue range, and employee size. Many records are enhanced with contact details such as email addresses, phone numbers, and names of key decision-makers, supporting direct outreach and smarter segmentation.

Our India dataset is designed for a wide range of business applications — from KYC and AML compliance, due diligence, and regulatory checks, to B2B sales, lead generation, marketing campaigns, CRM enrichment, and AI model training. Whether you’re targeting local startups or large enterprises, our data helps you connect with the right businesses at the right time.

Delivery is flexible to suit your needs. Choose from customized lists, full databases in Excel or CSV, access via our real-time API, or our intuitive self-service platform. We also offer data enrichment and cleansing services to refresh and improve your existing datasets with accurate, up-to-date company information from India.

With access to 32,468,995 verified companies across more than 200 countries, CompanyData.com helps businesses grow confidently — in India and beyond. Rely on our precise, structured data to fuel your strategies and scale with speed and accuracy.

Obtain unmatched insights into the Indian market using our database of over 2.5 million company profiles. Our Xverum team is client-oriented and we focus on delivering structured and fresh B2B and brand data, enabling you to make informed decisions and drive your business forward.

      Where this Indian B2B data can benefit you?

Competitor Analysis: Assess potential risks associated with specific real-time brand data, enabling you to mitigate financial and reputational risks in your business dealings.

Direct Marketing: Utilize our business data to target your marketing efforts with precision, reaching the right audience for your products and services.

B2B List Validation: Ensure the accuracy and legitimacy of your B2B contact lists, optimizing your lead generation and sales efforts.

With our comprehensive database of over 2.5 million Indian company profiles, you have the power to make informed decisions, drive your business strategy, and achieve success in the dynamic Indian market.

4 key features of our Indian data: - 40+ Data Attributes per Company Profile - Structured and Raw Data - Easily Integrated into Your Solutions - 100% Safe Source Promise

Contact our Xverum team and we'll be glad to find the best option due to your data requirements.

Please Note: Our dataset does not include PII and/or phone numbers.

With 19.7 Million Businesses in India , Techsalerator has access to the highest B2B count of Data/Business Data in the country. .

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

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

We cover all cities and regions in India ( example ) :

Mumbai Maharashtra Delhi Delhi Bangalore Karnataka Hyderabad Telangana Ahmedabad Gujarat Chennai Tamil Nadu Kolkata West Bengal Surat Gujarat Pune Maharashtra Jaipur Rajasthan Lucknow Uttar Pradesh Kanpur Uttar Pradesh Nagpur Maharashtra Indore Madhya Pradesh Thane Maharashtra Bhopal Madhya Pradesh Visakhapatnam[4] Andhra Pradesh Pimpri-Chinchwad Maharashtra Patna Bihar Vadodara Gujarat Ghaziabad Uttar Pradesh Ludhiana Punjab Agra Uttar Pradesh Nashik Maharashtra Ranchi Jharkhand Faridabad Haryana Meerut Uttar Pradesh Rajkot Gujarat Kalyan-Dombivli Maharashtra Vasai-Virar Maharashtra Varanasi Uttar Pradesh Srinagar Jammu and Kashmir Aurangabad Maharashtra Dhanbad Jharkhand Gurgaon[5] Haryana Amritsar Punjab Navi Mumbai Maharashtra Allahabad Uttar Pradesh[6] Howrah West Bengal Gwalior Madhya Pradesh Jabalpur Madhya Pradesh Coimbatore Tamil Nadu Vijayawada Andhra Pradesh Jodhpur Rajasthan Madurai Tamil Nadu Raipur Chhattisgarh Kota[8] Rajasthan Chandigarh Chandigarh Guwahati Assam Solapur Maharashtra Hubli–Dharwad Karnataka Mysore[9][10][11] Karnataka Tiruchirappalli[12] Tamil Nadu Bareilly Uttar Pradesh Aligarh Uttar Pradesh Tiruppur Tamil Nadu Moradabad Uttar Pradesh Jalandhar Punjab Bhubaneswar Odisha Salem Tamil Nadu Warangal[13][14] Telangana Mira-Bhayandar Maharashtra Jalgaon Maharashtra Guntur[15] Andhra Pradesh Thiruvananthapuram Kerala Bhiwandi Maharashtra Tirupati Andhra Pradesh Saharanpur Uttar Pradesh Gorakhpur Uttar Pradesh Bikaner Rajasthan Amravati Maharashtra Noida Uttar Pradesh Jamshedpur Jharkhand Bhilai Chhattisgarh Cuttack Odisha Firozabad Uttar Pradesh Kochi Kerala Nellore[16][17] Andhra Pradesh Bhavnagar Gujarat Dehradun Uttarakhand Durgapur West Bengal Asansol West Bengal Rourkela Odisha Nanded Maharashtra Kolhapur Maharashtra Ajmer Rajasthan Akola Maharashtra Gulbarga Karnataka Jamnagar Gujarat Ujjain Madhya Pradesh Loni Uttar Pradesh Siliguri West Bengal Jhansi Uttar Pradesh Ulhasnagar Maharashtra Jammu[18] Jammu and Kashmir Sangli-Miraj & Kupwad Maharashtra Mangalore Karnataka Erode[19] Tamil Nadu Belgaum Karnataka Kurnool[20] Andhra Pradesh Ambattur Tamil Nadu Rajahmundry[21][22] Andhra Pradesh Tirunelveli Tamil Nadu Malegaon Maharashtra Gaya Bihar Udaipur Rajasthan Karur Tamilnadu Kakinada Andhra Pradesh Davanagere Karnataka Kozhikode Kerala Maheshtala West Bengal Rajpur Sonarpur West Bengal Bokaro Jharkhand South Dumdum West Bengal Bellary Karnataka Patiala Punjab Gopalpur West Bengal Agartala Tripura Bhagalpur Bihar Muzaffarnagar Uttar Pradesh Bhatpara West Bengal Panihati West Bengal Latur Maharashtra Dhule Maharashtra Rohtak Haryana Sagar Madhya Pradesh Korba Chhattisgarh Bhilwara Rajasthan Berhampur Odisha Muzaffarpur Bihar Ahmednagar Maharashtra Mathura Uttar Pradesh Kollam Kerala Avadi Tamil Nadu Kadapa[23] Andhra Pradesh Anantapuram[24] Andhra Pradesh Kamarhati West Bengal Bilaspur Odisha Sambalpur Odisha Shahjahanpur Uttar Pradesh Satara Maharashtra Bijapur Karnataka Rampur Uttar Pradesh Shimoga Karnataka Chandrapur Maharashtra Junagadh Gujarat Thrissur Kerala Alwar Rajasthan Bardhaman West Bengal Kulti West Bengal Nizamabad Telangana Parbhani Maharashtra Tumkur Karnataka Khammam Telangana Uzhavarkarai Puducherry Bihar Sharif Bihar Panipat Haryana Darbhanga Bihar Bally West Bengal Aizawl Mizoram Dewas Madhya Pradesh Ichalkaranji Maharashtra Karnal Haryana Bathinda Punjab Jalna Maharashtra Eluru[25] Andhra Pradesh Barasat West Bengal Kirari Suleman Nagar Delhi Purnia[26] Bihar Satna Madhya Pradesh Mau Uttar Pradesh Sonipat Haryana Farrukhabad Uttar Pradesh Durg Chhattisgarh Imphal Manipur Ratlam Madhya Pradesh Hapur Uttar Pradesh Arrah Bihar Anantapur Andhra Pradesh Karimnagar Telangana Etawah Uttar Pradesh Ambarnath Maharashtra North Dumdum West Bengal Bharatpur Rajasthan Begusarai Bihar New Delhi Delhi Gandhidham Gujarat Baranagar West Bengal Tiruvottiyur Tamil Nadu Pondicherry Puducherry Sikar Rajasthan Thoothukudi Tamil Nadu Rewa Madhya Pradesh Mirzapur Uttar Pradesh Raichur Karnataka Pali Rajasthan Ramagundam[27] Telangana Silchar Assam Haridwar Uttarakhand Vijayanagaram Andhra Pradesh Tenali Andhra Pradesh Nagercoil Tamil Nadu Sri Ganganagar Rajasthan ...

Comprehensive dataset containing 2,446 verified Duty free store businesses in India with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 1,701 verified Free clinic businesses in India with complete contact information, ratings, reviews, and location data.

The company level data includes 1. Company Name 2. Website 3. Industry segmentation and sub-segmentation 4. Sector 5. Revenue Range 6. Employee size 7. Address line 8. City 9. State 10. Pin-code 11. Company Landline

POC Level Information 1. Name (Mapped to relevant company) 2. Position (CEO, MD, HR) 3. Exact Designation 4. Email ID

The dataset contains year- and company-wise compiled data on total amount of money spent under Corporate Social Responsibility (CSR) by companies registered under it.

Note:

Year-wise count of companies in this dataset may not match with that of the original source as the duplicate entries for each year were removed in this dataset. The original source had duplicate rows for hundreds of companies.

Comprehensive dataset containing 189 verified Gluten-free restaurant businesses in India with complete contact information, ratings, reviews, and location data.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Comprehensive dataset containing 6 verified Free clinic businesses in Himachal Pradesh, India with complete contact information, ratings, reviews, and location data.

This vast repository houses crucial information on international trade transactions, capturing the intricate details of both export and import activities of India. The Export Database contains meticulous records of outbound shipments, offering valuable insights into the products, exporters, and destinations involved in each transaction. On the other hand, the Import Database provides a comprehensive view of inbound shipments, shedding light on the importers, origins, and details of the products acquired. Together, these two databases present a holistic perspective on global trade dynamics, encompassing critical metadata such as dates, product descriptions, quantities, values, and transportation specifics. Whether you are an analyst, researcher, or business professional, this comprehensive database will undoubtedly prove to be an invaluable resource for gaining a deep understanding of international trade patterns and market dynamics. Explore the wealth of information within and unlock new opportunities in the world of trade and commerce. The Export Database contains information related to export transactions. Each entry in the database represents a specific export event. The metadata fields in this database hold crucial details about the exported products and the transaction itself. The "DATE" field indicates the date of the export. "EXPORTER NAME" refers to the name of the entity or company responsible for exporting the goods. "DESTINATION COUNTRY" indicates the country to which the products are being shipped. The "HS CODE" represents the Harmonized System code, a standardized numerical system used to classify traded products. The "PRODUCT DESCRIPTION" field provides a brief description of the exported item. The "BRAND" field specifies the brand associated with the product. "QUANTITY" indicates the total quantity of the product being exported, and "UNIT OF QUANTITY" represents the measurement unit used for quantity. "SUBITEM QUANTITY" refers to the quantity of a subitem within the main exported product. The "PACKAGES" field indicates the number of packages used for shipment. "GROSS WEIGHT" represents the total weight of the exported products. "SUBITEM FOB VALUE" and "TOTAL FOB VALUE" denote the Free on Board (FOB) value of the subitem and the total FOB value of the export, respectively. "TOTAL CIF VALUE" indicates the total cost, insurance, and freight value. "ITEM NUMBER" is a unique identifier for each product item. "TRANSPORT TYPE" specifies the mode of transportation used for the export. "INCOTERMS" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. "CUSTOMS" indicates the customs information related to the export. "VARIETY" and "ATTRIBUTES" hold additional details about the product. The "OPERATION TYPE" field indicates the type of export operation, such as direct export or re-export. "MONTH" and "YEAR" represent the month and year when the export occurred. The Import Database contains information related to import transactions. Each entry in the database represents a specific import event. The metadata fields in this database hold crucial details about the imported products and the transaction itself. The "DATE" field indicates the date of the import. "IMPORTER NAME" refers to the name of the entity or company responsible for importing the goods. "SALES COUNTRY" indicates the country from which the products are being purchased. "ORIGIN COUNTRY" denotes the country where the imported products originate. The "HS CODE" represents the Harmonized System code, a standardized numerical system used to classify traded products. The "PRODUCT DESCRIPTION" field provides a brief description of the imported item. "QUANTITY" indicates the total quantity of the product being imported, and "UNIT OF QUANTITY" represents the measurement unit used for quantity. "SUBITEM QUANTITY" refers to the quantity of a subitem within the main imported product. The "PACKAGES" field indicates the number of packages used for shipment. "GROSS WEIGHT" represents the total weight of the imported products. "TOTAL CIF VALUE" indicates the total cost, insurance, and freight value. "TOTAL FREIGHT VALUE" and "TOTAL INSURANCE VALUE" represent the respective values for freight and insurance. "ITEM FOB VALUE," "SUBITEM FOB VALUE," and "ITEM CIF VALUE" denote the Free on Board (FOB) value of the item, subitem, and the cost, insurance, and freight value of the item, respectively. "ORIGIN PORT" specifies the port from which the products were shipped. "TRANSPORT TYPE" specifies the mode of transportation used for the import. "INCOTERMS" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. "ITEM NUMBER" is a unique identifier for each product item. "CUSTOMS" indicates the customs information related to the import. "OPERATION TYPE" field indicates the type of import operation, such as direct import...

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Comprehensive dataset containing 108 verified Free clinic businesses in Tamil Nadu, India with complete contact information, ratings, reviews, and location data.

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Data Science Platform Market Size 2025-2029

The data science platform market size is valued to increase USD 763.9 million, at a CAGR of 40.2% from 2024 to 2029. Integration of AI and ML technologies with data science platforms will drive the data science platform market.

Major Market Trends & Insights

North America dominated the market and accounted for a 48% growth during the forecast period.
By Deployment - On-premises segment was valued at USD 38.70 million in 2023
By Component - Platform segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 763.90 million
CAGR : 40.2%
North America: Largest market in 2023

Market Summary

The market represents a dynamic and continually evolving landscape, underpinned by advancements in core technologies and applications. Key technologies, such as machine learning and artificial intelligence, are increasingly integrated into data science platforms to enhance predictive analytics and automate data processing. Additionally, the emergence of containerization and microservices in data science platforms enables greater flexibility and scalability. However, the market also faces challenges, including data privacy and security risks, which necessitate robust compliance with regulations.
According to recent estimates, the market is expected to account for over 30% of the overall big data analytics market by 2025, underscoring its growing importance in the data-driven business landscape.

What will be the Size of the Data Science Platform Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Data Science Platform Market Segmented and what are the key trends of market segmentation?

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.

Deployment

  On-premises
  Cloud


Component

  Platform
  Services


End-user

  BFSI
  Retail and e-commerce
  Manufacturing
  Media and entertainment
  Others


Sector

  Large enterprises
  SMEs


Application

  Data Preparation
  Data Visualization
  Machine Learning
  Predictive Analytics
  Data Governance
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Deployment Insights

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

In the dynamic and evolving the market, big data processing is a key focus, enabling advanced model accuracy metrics through various data mining methods. Distributed computing and algorithm optimization are integral components, ensuring efficient handling of large datasets. Data governance policies are crucial for managing data security protocols and ensuring data lineage tracking. Software development kits, model versioning, and anomaly detection systems facilitate seamless development, deployment, and monitoring of predictive modeling techniques, including machine learning algorithms, regression analysis, and statistical modeling. Real-time data streaming and parallelized algorithms enable real-time insights, while predictive modeling techniques and machine learning algorithms drive business intelligence and decision-making.

Cloud computing infrastructure, data visualization tools, high-performance computing, and database management systems support scalable data solutions and efficient data warehousing. ETL processes and data integration pipelines ensure data quality assessment and feature engineering techniques. Clustering techniques and natural language processing are essential for advanced data analysis. The market is witnessing significant growth, with adoption increasing by 18.7% in the past year, and industry experts anticipate a further expansion of 21.6% in the upcoming period. Companies across various sectors are recognizing the potential of data science platforms, leading to a surge in demand for scalable, secure, and efficient solutions.

API integration services and deep learning frameworks are gaining traction, offering advanced capabilities and seamless integration with existing systems. Data security protocols and model explainability methods are becoming increasingly important, ensuring transparency and trust in data-driven decision-making. The market is expected to continue unfolding, with ongoing advancements in technology and evolving business needs shaping its future trajectory.

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The On-premises segment was valued at USD 38.70 million in 2019 and showed

Comprehensive dataset containing 1 verified Free clinic businesses in Sikkim, India with complete contact information, ratings, reviews, and location data.

Abstract

The Central Statistical Organization (CSO) conducted fifth Economic Census in 2005 in all the States/UTs in collaboration with State Directorates of Economics and Statistics. The first Economic Census was conducted in 1977 covering only non- agricultural establishments and the three Economic Censuses subsequently carried out in 1980, 1990 and 1998 covered all agricultural and non-agricultural enterprises excepting those engaged in crop production and plantation. There was no change in the coverage of the fifth Economic Census as compared to the fourth Economic Census. Economic Census not only provides updated frame for detailed follow-up surveys but also gives basic entrepreneurial data for planning and development specially for unorganized sector of the economy.

There are certain new features in the fifth Economic Census. Addresses of the enterprises employing 10 workers or more were collected for the first time in the fifth Economic Census through Address Slip. At present the country does not maintain a Business Register. The directory of enterprises to be generated from the Address Slip would be the basic input for preparation of a Business Register. For the first time, data collected in the fifth Economic Census are processed through Intelligent Character Recognition (ICR) Technology.

The results of EC-2005 "ALL INDIA REPORT" contains the all India figures on the number of enterprises and their employment, cross-classified according to their locations, major activity groups, type of the establishments, size-class of the employment, etc. The disaggregated data for States/UTs are also included in the report.

Geographic coverage

All the States/UTs. in the country

Analysis unit

Establishment

Universe

Economic Census (EC) is the complete count of all entrepreneurial units located within the geographical boundaries of the country. All units engaged in the production or distribution of goods or services other than for the sole purpose of own consumption are counted. While all units engaged in nonagricultural activities are covered, in the agricultural sector units in crop production and plantation activities are excluded.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

All questionaires are provided as external resources

Cleaning operations

Intelligent Character Recognition (ICR) technology, which is also known as Automated Forms Processing, was used to process the EC-2005 data. Automated Forms Processing technology enables the user to process documents from their images or directly from paper and convert them to computer readable data.

The schedules of the Fifth EC were scanned/digitized at the fifteen regional Data Processing Centres of Registrar General of India (RGI). After running the edit programme, the error list files were handed over to the State Governments for corrections. The DES officials of the State Government corrected the error files in two/three cycles and then sent the data files to RGI Headquarters to give final touch before sending to Computer Centre, MOSPI. The data files were made further error free by applying auto corrections at the Computer Centre.

Snapshot img

Big Data Services Market Size 2025-2029

The big data services market size is forecast to increase by USD 604.2 billion, at a CAGR of 54.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of big data in various industries, particularly in blockchain technology. The ability to process and analyze vast amounts of data in real-time is revolutionizing business operations and decision-making processes. However, this market is not without challenges. One of the most pressing issues is the need to cater to diverse client requirements, each with unique data needs and expectations. This necessitates customized solutions and a deep understanding of various industries and their data requirements. Additionally, ensuring data security and privacy in an increasingly interconnected world poses a significant challenge. Companies must navigate these obstacles while maintaining compliance with regulations and adhering to ethical data handling practices. To capitalize on the opportunities presented by the market, organizations must focus on developing innovative solutions that address these challenges while delivering value to their clients. By staying abreast of industry trends and investing in advanced technologies, they can effectively meet client demands and differentiate themselves in a competitive landscape.

What will be the Size of the Big Data Services 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 ever-increasing volume, velocity, and variety of data being generated across various sectors. Data extraction is a crucial component of this dynamic landscape, enabling entities to derive valuable insights from their data. Human resource management, for instance, benefits from data-driven decision making, operational efficiency, and data enrichment. Batch processing and data integration are essential for data warehousing and data pipeline management. Data governance and data federation ensure data accessibility, quality, and security. Data lineage and data monetization facilitate data sharing and collaboration, while data discovery and data mining uncover hidden patterns and trends. Real-time analytics and risk management provide operational agility and help mitigate potential threats. Machine learning and deep learning algorithms enable predictive analytics, enhancing business intelligence and customer insights. Data visualization and data transformation facilitate data usability and data loading into NoSQL databases. Government analytics, financial services analytics, supply chain optimization, and manufacturing analytics are just a few applications of big data services. Cloud computing and data streaming further expand the market's reach and capabilities. Data literacy and data collaboration are essential for effective data usage and collaboration. Data security and data cleansing are ongoing concerns, with the market continuously evolving to address these challenges. The integration of natural language processing, computer vision, and fraud detection further enhances the value proposition of big data services. The market's continuous dynamism underscores the importance of data cataloging, metadata management, and data modeling for effective data management and optimization.

How is this Big Data Services Industry segmented?

The big data services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentSolutionServicesEnd-userBFSITelecomRetailOthersTypeData storage and managementData analytics and visualizationConsulting servicesImplementation and integration servicesSupport and maintenance servicesSectorLarge enterprisesSmall and medium enterprises (SMEs)GeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW).

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period.Big data services have become indispensable for businesses seeking operational efficiency and customer insight. The vast expanse of structured and unstructured data presents an opportunity for organizations to analyze consumer behaviors across multiple channels. Big data solutions facilitate the integration and processing of data from various sources, enabling businesses to gain a deeper understanding of customer sentiment towards their products or services. Data governance ensures data quality and security, while data federation and data lineage provide transparency and traceability. Artificial intelligenc

Comprehensive global trade data including customs shipment records, HS codes, port activity, and global buyer/supplier data.

Description for each of the variables:

1. Brands: The brands of smartphones included in the dataset.
2. Colors: The colors available for the smartphones.
3. Memory: The storage capacity of the smartphones, typically measured in gigabytes (GB) or megabytes (MB).
4. Storage: The internal storage capacity of the smartphones, often measured in gigabytes (GB) or megabytes (MB).
5. Rating: The user ratings or scores assigned to the smartphones, reflecting user satisfaction or performance.
6. Selling Price: The price at which the smartphones are sold to consumers.
7. Original Price: The original or list price of the smartphones before any discounts or promotions.
8. Mobile: Indicates whether the device is a mobile phone.
9. Discount: The discount applied to the original price to calculate the selling price.
10. Discount percentage: The percentage discount applied to the original price to calculate the selling price.

Snapshot img

GIS In Utility Industry Market Size 2025-2029

The gis in utility industry market size is forecast to increase by USD 3.55 billion, at a CAGR of 19.8% between 2024 and 2029.

The utility industry's growing adoption of Geographic Information Systems (GIS) is driven by the increasing need for efficient and effective infrastructure management. GIS solutions enable utility companies to visualize, analyze, and manage their assets and networks more effectively, leading to improved operational efficiency and customer service. A notable trend in this market is the expanding application of GIS for water management, as utilities seek to optimize water distribution and reduce non-revenue water losses. However, the utility GIS market faces challenges from open-source GIS software, which can offer cost-effective alternatives to proprietary solutions. These open-source options may limit the functionality and support available to users, necessitating careful consideration when choosing a GIS solution. To capitalize on market opportunities and navigate these challenges, utility companies must assess their specific needs and evaluate the trade-offs between cost, functionality, and support when selecting a GIS provider. Effective strategic planning and operational execution will be crucial for success in this dynamic market.

What will be the Size of the GIS In Utility Industry 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 Global Utilities Industry Market for Geographic Information Systems (GIS) continues to evolve, driven by the increasing demand for advanced data management and analysis solutions. GIS services play a crucial role in utility infrastructure management, enabling asset management, data integration, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage management, and spatial analysis. These applications are not static but rather continuously unfolding, with new patterns emerging in areas such as energy efficiency, smart grid technologies, renewable energy integration, network optimization, and transmission lines. Spatial statistics, data privacy, geospatial databases, and remote sensing are integral components of this evolving landscape, ensuring the effective management of utility infrastructure. Moreover, the adoption of mobile GIS, infrastructure planning, customer service, asset lifecycle management, metering systems, regulatory compliance, GIS data management, route planning, environmental impact assessment, mapping software, GIS consulting, GIS training, smart metering, workforce management, location intelligence, aerial imagery, construction management, data visualization, operations and maintenance, GIS implementation, and IoT sensors is transforming the industry. The integration of these technologies and services facilitates efficient utility infrastructure management, enhancing network performance, improving customer service, and ensuring regulatory compliance. The ongoing evolution of the utilities industry market for GIS reflects the dynamic nature of the sector, with continuous innovation and adaptation to meet the changing needs of utility providers and consumers.

How is this GIS In Utility Industry Industry segmented?

The gis in utility industry 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. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeFranceGermanyRussiaMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW).

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the utility industry, Geographic Information Systems (GIS) play a pivotal role in optimizing operations and managing infrastructure. Utilities, including electricity, gas, water, and telecommunications providers, utilize GIS software for asset management, infrastructure planning, network performance monitoring, and informed decision-making. The GIS software segment in the utility industry encompasses various solutions, starting with fundamental GIS software that manages and analyzes geographical data. Additionally, utility companies leverage specialized software for field data collection, energy efficiency, smart grid technologies, distribution grid design, renewable energy integration, network optimization, transmission lines, spatial statistics, data privacy, geospatial databases, GIS services, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage management, spatial analysis,