The Report Covers India's Big Data Services Market Trends and is Segmented by Type (Solution, Services), Organization Size (Small & Medium Enterprise, Large Enterprise), and End-User Vertical (BFSI, Retail, Telecommunication & IT, Media & Entertainment, Healthcare). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

India Manufacturing Industries: Number Of Factories data was reported at 249,987.000 Unit in 2022. This records a decrease from the previous number of 250,454.000 Unit for 2021. India Manufacturing Industries: Number Of Factories data is updated yearly, averaging 132,814.000 Unit from Mar 1982 (Median) to 2022, with 41 observations. The data reached an all-time high of 250,454.000 Unit in 2021 and a record low of 93,166.000 Unit in 1983. India Manufacturing Industries: Number Of Factories data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under India Premium Database’s Mining and Manufacturing Sector – Table IN.BAC001: Manufacturing Industry: NIC 2008: All Industries.

India Textile: Annual Survey of Industry: Percentage of Total Manufacturing Industry: Number of Factories data was reported at 7.500 % in 2017. This records a decrease from the previous number of 7.600 % for 2016. India Textile: Annual Survey of Industry: Percentage of Total Manufacturing Industry: Number of Factories data is updated yearly, averaging 8.300 % from Mar 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 8.800 % in 2011 and a record low of 7.500 % in 2017. India Textile: Annual Survey of Industry: Percentage of Total Manufacturing Industry: Number of Factories data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Textile Sector – Table IN.RSJ001: Textile: Overview of Annual Survey Industry.

Abstract

Introduction

The Annual Survey of Industries (ASI) is one of the large-scale sample survey conducted by Field Operation Division of National Sample Survey Office for more than three decades with the objective of collecting comprehensive information related to registered factories on annual basis. ASI is the primary source of data for facilitating systematic study of the structure of industries, analysis of various factors influencing industries in the country and creating a database for formulation of industrial policy.

The main objectives of the Annual Survey of Industries are briefly as follows:

(a) Estimation of the contribution of manufacturing industries as a whole and of each unit to national income.

(b) Systematic study of the structure of industry as a whole and of each type of industry and each unit.

(c) Casual analysis of the various factors influencing industry in the country: and

(d) Provision of comprehensive, factual and systematic basis for the formulation of policy.

The Annual Survey of Industries (ASI) is the principal source of industrial statistics in India. It provides statistical information to assess changes in the growth, composition and structure of organised manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. The Survey is conducted annually under the statutory provisions of the Collection of Statistics Act 1953, and the Rules framed there-under in 1959, except in the State of Jammu & Kashmir where it is conducted under the State Collection of Statistics Act, 1961 and the rules framed there-under in 1964.

Geographic coverage

The ASI is the principal source of industrial statistics in India and extends to the entire country except Arunachal Pradesh, Mizoram & Sikkim and the Union Territory of Lakshadweep. It covers all factories registered under Sections 2m(i) and 2m(ii) of the Factories Act, 1948.

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to census scheme is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Universe

The survey cover factories registered under the Factory Act 1948.

Establishments under the control of the Defence Ministry,oil storage and distribution units, restaurants and cafes and technical training institutions not producing anything for sale or exchange were kept outside the coverage of the ASI.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Procedure

The sampling design followed in ASI 1998-99 is a Circular Systematic one. All the factories in the updated frame (universe) are divided into two sectors, viz., Census and Sample.

Census Sector: Census Sector is defined as follows:

a) All the complete enumeration States namely, Manipur, Meghalaya, Nagaland, Tripura and Andaman & Nicobar Islands. b) For the rest of the States/ UT's., (i) units having 200 or more workers, and (ii) all factories covered under Joint Returns.

Rest of the factories found in the frame constituted Sample sector on which sampling was done. Factories under Biri & Cigar sector were not considered uniformly under census sector. Factories under this sector were treated for inclusion in census sector as per definition above (i.e., more than 200 workers and/or joint returns). After identifying Census sector factories, rest of the factories were arranged in ascending order of States, NIC-98 (4 digit), number of workers and district and properly numbered. The Sampling was taken within each stratum (State X Sector X 4-digit NIC) with a minimum of 8 samples in each stratum in the form of 2 sub-samples. For the first time, all electricity undertakings other than captive units, Government Departmental undertakings such as Railway Workshops, P & T workshops etc. were kept out of coverage of ASI.

Sampling deviation

There was no deviation from sample design in ASI 1998-99.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Pre-data entry scrutiny was carried out on the schedules for inter and intra block consistency checks. Such editing was mostly manual, although some editing was automatic. But, for major inconsistencies, the schedules were referred back to NSSO (FOD) for clarifications/modifications.

The final unit level data of ASI 98-99 is available now in electronic media. This document describes additional information regarding ASI 98-99 data from the point of data processing. Users of ASI 98-99 data are requested to read this document carefully before they attempt to process the unit level data for their own purpose. They are also requested to refer to the schedule and the instruction manual for filling up the schedule before interpreting contents of various data fields. A. Contents The CD (or any other media) should contain the following files: ASI99.TXT This file contains unit level detail data of ASI 98-99 as per structure given in ANNEXURE- Total no. of records: 104740 XASI98.TXT (Metadata created from this .TXT file) This file contains unit level detail data of ASI 97-98 for those factories which were found not responding during the survey of ASI 98-99. The record layout is already available with the Computer Centre, New Delhi. Record Length: 135 Total no. of records: 6974 README.DOC This file.

B. Tabulation procedure The tabulation procedure by CSO(ISW) includes both the ASI 98-99 data and the extracted data from ASI 97-98 for all tabulation purpose. To make results comparable, users are requested to follow the same procedure. For calculation of various parameters, users are requested to refer instruction manual/report for the respective years. Please note that a separate inflation factor (Multiplier) is available for each factory against records belonging to Block-A ,pos:38-46 (Please refer ANNEXURE-I) for ASI 98-99 data. Since the data extracted from ASI 97-98 belong to Census Sector no such inflation (Multiplier) factor is required. Industry code as per Return(5-digit level of NIC-98) Industry code as reported by the factories in Block-A, Item 1 has been further codified because of the following two policies practiced at CSO(ISW). Tabulation policy: As per the latest tabulation policy, it has been decided to publish detail information regarding factories belonging to 01 to 37 of industry codes( 2-digit, NIC-98). Factories belonging to other industry groups would be clubbed together and to be published under 'Others'. Accordingly all industry codes other than 01 to 37 were replaced with a 5-digited code 'YYYYY'. Merging and suppression of identity: To suppress the identity of factories, less frequent industry codes were modified accordingly. Example: if a reported industry code is found as 2930Z, this is to be treated as 'other merged industry code under industry group 2930 (4-digit NIC'98)'. Similarly if the reported industry code is found as 293ZZ, the same as to be treated as 'other merged industry code under industry group 293 (3-digit NIC'98)' and so on.

FIXED ASSETS (Block-C) Columnwise relationship (please refer schedule) may not hold true for data in this block. This is because of the lack of information available from the factory owners. E. EMPLOYMENT AND LABOUR COST (Block-E) It has been found that a larger number of factory owners were unable to provide detailed break-up of information regarding provident fund (Block-E, Col.7). Instead they provide total provident fund as a whole for all employees (Block-E, Srl. No. 7, Col.7). Users are requested to use Srl.9, Col.7 for information on provident fund. The total of srl.6 to 8 for Col.7 may not tally with srl.9, col.7. F. ASICC codes in Block H, I & J Because of the proximity of various item's description, it is possible that same ASICC code may appear against multiple records in these blocks. They should not be treated as duplicates. They are clubbed together at the time of tabulation to provide information at ASICC level. G. Record Identification Key Record identification key for each factory is Despatch Serial No. (DSL, pos: 4-8) X Block code (Blk, pos: 3). Please refer ANNEXURE-I for item level identification key for each factory.

Sampling error estimates

Relative Standard Error (RSE) is calculated in terms of worker, wages to worker and GVA using the formula (Pl ease refer to Estimation Procedure document in external resources). Programs developed in Visual Faxpro are used to compute the RSE of estimates.

Data appraisal

To check for consistency and reliability of data the same are compared with the NIC-2digit level growth rate at all India Index of Production (IIP) and the growth rates obtained from the National Accounts Statistics at current and constant prices for the registered manufacturing sector.

In 2024, ****** was estimated to have the power load for data center industry with ** percent of the total **** MW inventory in India. It was followed by *****************************.

India IT Industry Revenue: USD: Domestic data was reported at 41.000 USD bn in 2019. This stayed constant from the previous number of 41.000 USD bn for 2018. India IT Industry Revenue: USD: Domestic data is updated yearly, averaging 20.380 USD bn from Mar 1997 (Median) to 2019, with 23 observations. The data reached an all-time high of 48.000 USD bn in 2015 and a record low of 2.661 USD bn in 1997. India IT Industry Revenue: USD: Domestic data remains active status in CEIC and is reported by National Association of Software and Service Companies. The data is categorized under Global Database’s India – Table IN.TF007: Information Technology Statistics: National Association of Software and Service Company: IT-BPM: Domestic Revenue.

Abstract

Introduction

The Annual Survey of Industries (ASI) is the principal source of industrial statistics in India. It provides statistical information to assess changes in the growth, composition and structure of organised manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. Industrial sector occupies an important position in the State economy and has a pivotal role to play in the rapid and balanced economic development. The Survey is conducted annually under the statutory provisions of the Collection of Statistics Act 1953, and the Rules framed there-under in 1959, except in the State of Jammu & Kashmir where it is conducted under the State Collection of Statistics Act, 1961 and the rules framed there-under in 1964.

Geographic coverage

Coverage of the Annual Survey of Industries extends to the entire Factory Sector, comprising industrial units (called factories) registered under section 2(m)(i) and 2(m)(ii) of the Factories Act.1948, wherein a "Factory", which is the primary statistical unit of enumeration for the ASI is defined as:- "Any premises" including the precincts thereof:- (i) wherein ten or more workers are working or were working on any day of the preceding twelve months, and in any part of which a manufacturing process is being carried on with the aid of power or is ordinarily so carried on, or (ii) wherein twenty or more workers are working or were working on any day of the preceding twelve months, and in any part of which a manufacturing process is being carried on without the aid of power. In addition to section 2(m)(i) & 2(m)(ii) of the Factories Act, 1948, electricity units registered with the Central Electricity Authority and Bidi & Cigar units, registered under the Bidi & Cigar Workers (Conditions of Employment) Act,1966 are also covered in ASI.

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to same scheme (census or sample) is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Universe

The survey cover factories registered under the Factory Act 1948. Establishments under the control of the Defence Ministry,oil storage and distribution units, restaurants and cafes and technical training institutions not producing anything for sale or exchange were kept outside the coverage of the ASI. The geographical coverage of the Annual Survey of Industries, 2002-03 has been extended to the entire country except the states of Arunachal Pradesh, Mizoram and Sikkim and Union Territory of Lakshadweep.

Kind of data

Census and Sample survey data [cen/ssd]

Sampling procedure

Sampling Procedure

The sampling design followed in ASI 2002-03 is a circular systematic one. All the factories in the updated frame (universe) are divided into two sectors, viz., Census and Sample.

Census Sector: Census Sector is defined as follows:

a) All industrial units belonging to the six less industrially developed states/ UT's viz. Manipur, Meghalaya, Nagaland, Tripura, Sikkim and Andaman & Nicobar Islands.
b) For the rest of the twenty-six states/ UT's., (i) units having 100 or more workers, and (ii) all factories covered under Joint Returns. c) After excluding the Census Sector units as defined above, all units belonging to the strata (State by 4-digit of NIC-04) having less than or equal to 4 units are also considered as Census Sector units.

Remaining units, excluding those of Census Sector, called the sample sector, are arranged in order of their number of workers and samples are then drawn circular systematically considering sampling fraction of 20% within each stratum (State X Sector X 4-digit NIC) for all the states. An even number of units with a minimum of 4 are selected and evenly distributed in two sub-samples. The sectors considered here are Biri, Manufacturing and Electricity.

Sampling deviation

There was no deviation from sample design in ASI 2002-03

Mode of data collection

Statutory return submitted by factories as well as Face to face

Research instrument

Annual Survey of Industries Questionnaire (in External Resources) is divided into different blocks:

BLOCK A :IDENTIFICATION PARTICULARS BLOCK B : PARTICULARS OF THE FACTORY (TO BE FILLED BY OWNER OF THE FACTORY) BLOCK C : FIXED ASSETS BLOCK D : WORKING CAPITAL & LOANS BLOCK E : EMPLOYMENT AND LABOUR COST BLOCK F : OTHER EXPENSES BLOCK G : OTHER INCOMES BLOCK H : INPUT ITEMS (indigenous items consumed) BLOCK I : INPUT ITEMS – directly imported items only (consumed) BLOCK J : PRODUCTS AND BY-PRODUCTS (manufactured by the unit)

Cleaning operations

Pre-data entry scrutiny was carried out on the schedules for inter and intra block consistency checks. Such editing was mostly manual, although some editing was automatic. But, for major inconsistencies, the schedules were referred back to NSSO (FOD) for clarifications/modifications.

Code list, State code list, Tabulation program and ASICC code are also may be refered in the External Resources which are used for editing and data processing as well..

Tabulation procedure The tabulation procedure by CSO(ISW) includes both the ASI 2002-03 data and the extracted data from ASI 01-02 for all tabulation purpose. For extracted returns, status of unit (Block A, Item 12) would be in the range 17 to 20. To make results comparable, users are requested to follow the same procedure. For calculation of various parameters, users are requested to refer instruction manual/report. Please note that a separate inflation factor (Multiplier) is available for each unit against records belonging to Block-A ,pos:62-70 (Please refer STRUC03.XLS) for ASI 2002-03 data. The multiplier is calculated for each sub-stratum (i.e. State X NIC'98(4 Digit) X sub-stratum) after adjusting for non-response cases.

Status of unit code 17-20 may always be considered for all processing.

Merging of unit level data As per existing policy to merge unit level data at ultimate digit level of NIC'98 (i.e., 5 digit) for the purpose of dissemination, the data have been merged for industries having less than three units within State, District and NIC'98(5 Digit) with the adjoining industries within district and then to adjoining districts within a state. There may be some NIC'98(5 Digit) ending with '9' which do not figure in the book of NIC '98. These may be treated as 'Others' under the corresponding 4-digit group. To suppress the identity of factories data fields corresponding to PSL number, Industry code as per Frame (4-digit level of NIC-98) and RO/SRO code have been filled with '9' in each record.

It may please be noted that, tables generated from the merged data may not tally with the published results for few industries, since the merging for published data has been done at aggregate-level to minimise the loss of information.

Sampling error estimates

Relative Standard Error (RSE) is calculated in terms of worker, wages to worker and GVA using the formula. Programs developed in Visual Foxpro are used to compute the RSE of estimates.

Data appraisal

To check for consistency and reliability of data the same are compared with the NIC-2digit level growth rate at all India Index of Production (IIP) and the growth rates obtained from the National Accounts Statistics at current and constant prices for the registered manufacturing sector.

Abstract

The Annual Survey of Industries (ASI) is the principal source of industrial statistics in India. It provides statistical information to assess changes in the growth, composition and structure of organised manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. Industrial sector occupies an important position in the State economy and has a pivotal role to play in the rapid and balanced economic development. The Survey is conducted annually under the statutory provisions of the Collection of Statistics Act 1953, and the Rules framed there-under in 1959, except in the State of Jammu & Kashmir where it is conducted under the State Collection of Statistics Act, 1961 and the rules framed there-under in 1964.

Geographic coverage

Coverage of the Annual Survey of Industries extends to the entire Factory Sector, comprising industrial units (called factories) registered under section 2(m)(i) and 2(m)(ii) of the Factories Act.1948, wherein a "Factory", which is the primary statistical unit of enumeration for the ASI is defined as:- "Any premises" including the precincts thereof:- (i) wherein ten or more workers are working or were working on any day of the preceding twelve months, and in any part of which a manufacturing process is being carried on with the aid of power or is ordinarily so carried on, or (ii) wherein twenty or more workers are working or were working on any day of the preceding twelve months, and in any part of which a manufacturing process is being carried on without the aid of power. In addition to section 2(m)(i) & 2(m)(ii) of the Factories Act, 1948, electricity units registered with the Central Electricity Authority and Bidi & Cigar units, registered under the Bidi & Cigar Workers (Conditions of Employment) Act,1966 are also covered in ASI.

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to same scheme (census or sample) is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Universe

The survey cover factories registered under the Factory Act 1948. Establishments under the control of the Defence Ministry,oil storage and distribution units, restaurants and cafes and technical training institutions not producing anything for sale or exchange were kept outside the coverage of the ASI.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design followed in ASI 1997-98 is a circular systematic one. All the factories in the updated frame (universe) are divided into two sectors, viz., Census and Sample.

Census Sector: Census Sector is defined as follows:

a) All industrial units belonging to the 12 less industrially developed states/ UT's viz. Manipur, Meghalaya, Nagaland, Tripura, Sikkim and Andaman & Nicobar Islands etc.
b) For the rest of the states/ UT's., (i) units having 200 or more workers and also some "Significant Units"were identified from the databases of ASI 1993-94 to ASI 1995-96, which although having less than 200 workers, contributed significantly to the Value of Output in these ASI years. and (ii) all public sector undertakings (PSU) and electricity sector were included in the census sector.

Remaining units, excluding those of Census Sector, called the sample sector, are arranged in order of their number of workers and samples are then drawn circular systematically Sampling technique from each stratum (State X 3-digit NIC) with a minimum of 4 samples per stratum.

The census and sample sectors were such formulated that the Census Sector units comprising at most 9% units contributed at least 90% of Net Value Added (NVA) at All-India level. Alternaitively, the sample sector comprising 91% industrial units contributed less than 10% of NVA. The sample size n from the above sample sector was determined at All-India level first. Then the total size for a particular state was allocated in the proportion of total output in that stratum. It may be noted that the sample size in stratum was always taken as an even no. (by adding on unit in case, it was odd) and if, sample size alongwith the no. of census units in any stratum be less than 4, then that stratum was merged with the units of a nearest stratum.

Sampling deviation

There was no deviation from sample design in ASI 1997-98

Mode of data collection

Face-to-face [f2f]

Research instrument

Annual Survey of Industries Questionnaire (in External Resources) is divided into different blocks:

BLOCK A & B :IDENTIFICATION PARTICULARS (Filled by CSO and Industrial Units) BLOCK C : ASSETS AND LIABILITIES BLOCK D : EMPLOYMENT AND LABOUR COST BLOCK E : RECEIPTS BLOCK F : EXPENSES BLOCK G : INPUT ITEMS (indigenous items consumed) BLOCK H : INPUT ITEMS – directly imported items only (consumed) BLOCK I : PRODUCTS AND BY-PRODUCTS (manufactured by the unit) BLOCK J : DISTRIBUTIVE EXPENSES BLOCK K : POLLUTION CONTROL

Cleaning operations

Pre-data entry scrutiny was carried out on the schedules for inter and intra block consistency checks. Such editing was mostly manual, although some editing was automatic. But, for major inconsistencies, the schedules were referred back to NSSO (FOD) for clarifications/modifications.

Code list, State code list, Tabulation program and ASICC code are attached in the External Resources. which are used for editing and data processing as well..

Sampling error estimates

Relative Standard Error (RSE) is calculated in terms of worker, wages to worker and GVA using the formula. Programs developed in Visual Foxpro are used to compute the RSE of estimates.

Data appraisal

To check for consistency and reliability of data the same are compared with the NIC-2digit level growth rate at all India Index of Production (IIP) and the growth rates obtained from the National Accounts Statistics at current and constant prices for the registered manufacturing sector.

The India Data Center Storage Market report segments the industry into Storage Technology (Network Attached Storage (NAS), Storage Area Network (SAN), Direct Attached Storage (DAS), Other Technologies), Storage Type (Traditional Storage, All-Flash Storage, Hybrid Storage), and End-User (IT & Telecommunication, BFSI, Government, Media & Entertainment, Other End-User). Get five years of historical data and five-year market forecasts.

Manufacturing Industries: Uttar Pradesh: Factories data was reported at 17,481.000 Unit in 2022. This records an increase from the previous number of 16,503.000 Unit for 2021. Manufacturing Industries: Uttar Pradesh: Factories data is updated yearly, averaging 12,385.500 Unit from Mar 1999 (Median) to 2022, with 24 observations. The data reached an all-time high of 17,481.000 Unit in 2022 and a record low of 8,980.000 Unit in 2003. Manufacturing Industries: Uttar Pradesh: Factories data remains active status in CEIC and is reported by Ministry of Statistics and Programme Implementation. The data is categorized under India Premium Database’s Mining and Manufacturing Sector – Table IN.BAF032: Manufacturing Industry: NIC 2008: By State: Uttar Pradesh.

The provided dataset encompasses information about over 3000 Indian companies across various industries, offering a comprehensive snapshot of India's vibrant business landscape. Here's an insightful description of the dataset:

Company Name: The name of the company, representing its unique identity and brand within the marketplace.

Industry Sector: Categorization of companies based on the sector or industry in which they operate. This classification covers a diverse array of sectors such as technology, finance, healthcare, manufacturing, consumer goods, and many others, reflecting the multifaceted nature of India's economy.

Company Size: An indication of the size or scale of the company, which may include parameters such as revenue, number of employees, market capitalization, or other relevant metrics. This information provides insights into the company's market presence and potential impact.

Location: The geographic location of the company's headquarters or primary operational base within India. This includes cities across the length and breadth of the country, from metropolitan hubs like Mumbai, Delhi, and Bangalore to emerging business centers in tier 2 and tier 3 cities.

Year of Establishment: The year in which the company was founded or established, providing historical context and highlighting its longevity and experience in the market.

Key Products/Services: Description of the primary products or services offered by the company, showcasing its areas of specialization and core competencies.

Market Positioning: Insights into the company's market positioning, competitive landscape, and strategic initiatives, which may include market share, brand reputation, and differentiation strategies.

Key Observations:

Sectoral Diversity: The dataset reflects the rich diversity of industries present in India's economy, ranging from traditional sectors like agriculture and manufacturing to modern, technology-driven industries such as IT and e-commerce.

Geographic Spread: Companies in the dataset are spread across various states and regions of India, showcasing the country's economic decentralization and the emergence of new business hubs beyond traditional metropolitan areas.

Entrepreneurial Spirit: The dataset underscores India's thriving entrepreneurial ecosystem, characterized by a vibrant startup culture, innovation-driven enterprises, and a growing emphasis on technology and digital transformation.

Contribution to Economy: These 3000+ Indian companies collectively contribute significantly to India's economic growth, job creation, and global competitiveness, driving innovation, investment, and productivity across sectors.

Insights and Applications:

Market Analysis: Analysts and researchers can leverage the dataset to conduct in-depth market analysis, identify industry trends, and gain insights into the performance and growth trajectories of Indian companies across different sectors and regions.

Investment Opportunities: Investors seeking opportunities in India can use the dataset to identify promising companies for potential investment, based on industry dynamics, growth potential, and market positioning.

Policy Formulation: Policymakers and government agencies can utilize the dataset to formulate strategies, policies, and initiatives aimed at fostering entrepreneurship, promoting industrial growth, and enhancing the competitiveness of Indian businesses on the global stage.

Business Development: Entrepreneurs and business leaders can draw inspiration from the diverse array of Indian companies in the dataset, learning from their success stories, strategies, and best practices to drive their own business growth and innovation.

The India data center server market size reached USD 2.46 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 4.76 Billion by 2033, exhibiting a growth rate (CAGR) of 7.03% during 2025-2033. The market for data center servers in India is expanding due to increasing AI-driven infrastructure, rising cloud adoption, and sustainability initiatives. Investments in AI-optimized and green data centers are driving demand for high-performance, energy-efficient servers, supporting digital transformation, and enhancing India’s position as a key data center hub.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the region/country level for 2025-2033. Our report has categorized the market based on product and application.

Snapshot img

Big Data In Manufacturing Market Size 2025-2029

The big data in manufacturing market size is forecast to increase by USD 21.44 billion at a CAGR of 26.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing adoption of Industry 4.0 and the emergence of artificial intelligence (AI) and machine learning (ML) technologies. The integration of these advanced technologies is enabling manufacturers to collect, process, and analyze vast amounts of data in real-time, leading to improved operational efficiency, enhanced product quality, and increased competitiveness. Cost optimization is achieved through root cause analysis and preventive maintenance, and AI algorithms and deep learning are employed for capacity planning and predictive modeling.
To capitalize on the opportunities presented by the market and navigate these challenges effectively, manufacturers must invest in building strong data analytics capabilities and collaborating with technology partners and industry experts. By leveraging these resources, they can transform raw data into actionable insights, optimize their operations, and stay ahead of the competition. The sheer volume, velocity, and variety of data being generated require sophisticated tools and expertise to extract meaningful insights. Additionally, ensuring data security and privacy, particularly in the context of increasing digitalization, is a critical concern.

What will be the Size of the Big Data In Manufacturing Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic manufacturing market, Business Intelligence (BI) plays a pivotal role in driving operational efficiency and competitiveness. Blockchain technology and industrial automation are key trends, enhancing transparency and security in supply chain operations. Real-time monitoring systems, Data Integration Tools, and Data Analytics Dashboards enable manufacturers to gain insights from vast amounts of data. Lifecycle analysis, Smart Manufacturing, and Cloud-based Data Analytics facilitate predictive maintenance and optimize production.
PLC programming, Edge AI, KPI tracking, and Automated Reporting facilitate data-driven decision making. Manufacturing Simulation Software and Circular Economy principles foster innovation and sustainability. The market is transforming towards Digital Transformation, incorporating Predictive Maintenance Software and Digital Thread for enhanced visibility and agility. SCADA systems, Carbon Footprint, and Digital Thread promote sustainable manufacturing practices. AI-powered Quality Control, Performance Measurement, and Sensor Networks ensure product excellence.

How is this Big Data In Manufacturing Industry segmented?

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

Type

  Services
  Solutions


Deployment

  On-premises
  Cloud-based
  Hybrid


Application

  Operational analytics
  Production management
  Customer analytics
  Supply chain management
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The services segment is estimated to witness significant growth during the forecast period. In the realm of manufacturing, the rise of data from sensors, machines, and operations presents a significant opportunity for analytics and insights. Big data services play a pivotal role in this landscape, empowering manufacturers to optimize resource allocation, minimize operational inefficiencies, and discover cost-saving opportunities. Real-time analytics enable predictive maintenance, reducing unplanned downtime and repair costs. Data visualization tools offer human-machine interfaces (HMIs) for seamless interaction, while machine learning and predictive modeling uncover hidden patterns and trends. Data security is paramount, with robust access control, encryption, and disaster recovery solutions ensuring data integrity. Supply chain management and demand forecasting are streamlined through data integration and real-time analytics.

Quality control is enhanced with digital twins and anomaly detection, minimizing defects and rework. Capacity planning and production monitoring are optimized through time series analysis and neural networks. IoT sensors and data acquisition systems feed data warehouses and data lakes, fueling statistical analysis and regression modeling. Energy efficiency is improved through data-driven insights, while inventory management

Capacity Utilization in India increased to 75.30 percent in the first quarter of 2025 from 74.70 percent in the fourth quarter of 2024. This dataset provides - India Capacity Utilization- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The AI market size in India was around *** billion U.S. dollars in 2024. Among all the segments, machine learning had the largest share at *** billion dollars. Artificial intelligence has been responsible for drastic changes in the technology sector where it can greatly improve productivity through process simplification and automation. It is also an integral part and one of the fundamental bases of Industry 4.0. IT industry in India The IT industry in India is a huge industry which consists of information technology services, consulting, and outsourcing. India’s IT services industry was born in Mumbai in 1967 when Tata Consultancy Services was established. India made up to more than ** percent of the global IT spending in financial year 2021. Within the global IT industry, India is renowned for its IT outsourcing services, and with governmental support and foreign investments, the industry is also developing technologies relative to AI and IoT. AI technologies The main branches of an AI ecosystem are machine learning, robotics, artificial neural networks, and Natural Language Processing (NLP). In machine learning, software programs run through existing data, and apply the learned knowledge to new data or to predict data. In the field of robotics, it develops and trains robots for various applications. A prominent example is autonomous vehicles, though the level of autonomy varies, it was estimated that between 2024 and 2025, fully autonomous cars could be seen in the market.

In 2022, over 18 thousand data science job positions were available in the BFSI sector in India. An increase in the availability of data science jobs was seen over the years from 2019. E-commerce and internet followed suite with roughly 13 thousand jobs during the same time period.

The gig economy has witnessed remarkable growth in India, offering workers flexibility but often lacking in traditional social security benefits. This research aims to explore the multifaceted factors influencing the social security landscape for gig workers in India. The study draws upon a wide range of data sources, including government reports, labor surveys, academic research, and surveys from non-governmental organizations.

The manufacturing sector in India employed over **** million people in the financial year 2023. This was a growth of over * percent as compared to the previous financial year. Industries driving the sector's growth included basic metals, coke and refined petroleum, food products, and chemicals among others.

Being one of the largest automotive sectors, India had over 326 million registered vehicles as of financial year 2020. It was the largest producer of two-wheelers across the globe in 2023. The market within the country was also dominated by this segment. In financial year 2024, over 17.97 million units of two-wheelers were sold domestically across the south Asian country. A decline in the sales volume of two-wheelers has been witnessed between 2020 and 2022. Hero MotoCorpHero MotoCorp had the maximum share in the two-wheeler segment in India. The company was the worldwide leader in two-wheeler manufacturing. The company has taken up the initiative of manufacturing electric scooters and bikes. To reduce the high battery costs that create a significant cost difference between the petrol and the battery variants, the Indian government has introduced the National Programme on Advanced Chemistry Cell (ACC) in 2022 to inventivize batery manufacturing. Two-wheeler market outlookThe Indian government has set a target to electrify a major proportion of the two-wheelers within the nation. However, the manufacturers have encouraged the government to adopt more ‘realistic’ expectations, as the former’s scheme would mean the electrification of over two million vehicles. With the two-wheeler industry estimated to grow at over nine percent in the next few years, more investments in the clean energy sector could pave a way for the domestic market.

Success.ai’s Import Export Data for Import, Export & Trade Professionals in Asia delivers a comprehensive dataset tailored for businesses aiming to connect with key players in Asia’s dynamic trade industry. Covering professionals involved in import/export operations, international logistics, and supply chain management, this dataset provides verified contact details, firmographic insights, and actionable professional data.

With access to over 700 million verified global profiles and 70 million business datasets, Success.ai ensures your outreach, market research, and trade strategies are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is essential for navigating the complexities of global trade in Asia.

Why Choose Success.ai’s Import Export Data?

1. Verified Contact Data for Effective Engagement

   • Access verified work emails, phone numbers, and LinkedIn profiles of trade professionals, logistics experts, and supply chain managers.
   • AI-driven validation ensures 99% accuracy, reducing data gaps and improving communication efficiency.

2. Comprehensive Coverage of Asian Trade Markets

   • Includes profiles of professionals from key Asian markets such as China, India, Japan, South Korea, and Southeast Asia.
   • Gain insights into regional trade trends, import/export regulations, and supply chain dynamics.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership roles, trade activities, and market expansions.
   • Stay aligned with evolving market conditions and emerging trade opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful data usage for all business initiatives.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with import/export professionals, logistics managers, and trade consultants across Asia.
• 70M Business Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic footprints.
• Contact Details: Gain verified work emails, phone numbers, and business locations for precise targeting.
• Industry Trends: Understand key import/export opportunities, supply chain challenges, and market dynamics in Asia.

Key Features of the Dataset:

1. Professional Profiles in Import/Export and Logistics

   • Identify and engage with trade professionals managing cross-border operations, customs compliance, and supply chain efficiency.
   • Target individuals responsible for vendor selection, international partnerships, and trade negotiations.

2. Firmographic and Geographic Insights

   • Access data on company structures, trade volumes, and operational hubs in key Asian markets.
   • Pinpoint high-value prospects in established and emerging trade routes for strategic engagement.

3. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (manufacturing, wholesale, retail), geographic location, or revenue size.
   • Tailor campaigns to address specific trade needs such as market entry, cost optimization, or regulatory compliance.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with trade professionals.

Strategic Use Cases:

1. Sales and Business Development

   • Present trade services, logistics solutions, or supply chain optimization tools to import/export managers and trade consultants.
   • Build relationships with procurement teams and logistics managers seeking reliable partners and innovative solutions.

2. Market Research and Competitive Analysis

   • Analyze trends in Asia’s import/export landscape, including key trade routes, regulatory changes, and logistics challenges.
   • Benchmark against competitors to identify growth opportunities, underserved markets, and emerging needs.

3. Partnership Development and Trade Collaboration

   • Engage with businesses seeking partnerships for supply chain management, customs compliance, or international expansion.
   • Foster alliances that enhance efficiency, reduce costs, and drive growth in the import/export sector.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers recruiting for roles in international trade, logistics, or operations.
   • Provide workforce optimization platforms or training solutions tailored to the trade and logistics industry.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality import/export data at competitive prices, ensuring maximum ROI for your marketing, sales, and trade initiatives.

2. Seamless Integration

   • Integrate verified trade data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, simplifying workflows and ...