The Supply Chain Big Data Analytics Market Report is Segmented by Component (Solution, Service), End User Industry (Retail, Transportation and Logistics, Manufacturing, Healthcare, Other End-User Industries), Deployment Model (On-Premise, Cloud), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

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

Big Data Analytics in BFSI Market Outlook

As per our latest research, the global Big Data Analytics in BFSI market size reached USD 22.7 billion in 2024, driven by the increasing digital transformation initiatives and the accelerating adoption of advanced analytics across financial institutions. The market is expected to grow at a robust CAGR of 14.8% during the forecast period, reaching an estimated USD 62.5 billion by 2033. The rapid proliferation of digital banking, heightened focus on fraud detection, and the need for personalized customer experiences are among the primary growth drivers for the Big Data Analytics in BFSI market.

The exponential growth of data generated by financial transactions, customer interactions, and regulatory requirements has created an urgent need for advanced analytics solutions in the BFSI sector. Financial institutions are leveraging Big Data Analytics to gain actionable insights, optimize operations, and enhance decision-making processes. The integration of artificial intelligence and machine learning with Big Data Analytics platforms is enabling BFSI organizations to automate risk assessment, predict customer behavior, and streamline compliance procedures. Furthermore, the surge in digital payment platforms and online banking services has resulted in an unprecedented volume of structured and unstructured data, further necessitating robust analytics solutions to ensure data-driven strategies and operational efficiency.

Another significant growth factor is the increasing threat of cyberattacks and financial fraud. As digital channels become more prevalent, BFSI organizations face sophisticated threats that require advanced analytics for real-time detection and mitigation. Big Data Analytics empowers financial institutions to monitor vast datasets, identify unusual patterns, and respond proactively to potential security breaches. Additionally, regulatory bodies are imposing stringent data management and compliance standards, compelling BFSI firms to adopt analytics solutions that ensure transparency, auditability, and adherence to global regulations. This regulatory push, combined with the competitive need to offer innovative, customer-centric services, is fueling sustained investment in Big Data Analytics across the BFSI landscape.

The growing emphasis on customer-centricity is also propelling the adoption of Big Data Analytics in the BFSI sector. Financial institutions are increasingly utilizing analytics to understand customer preferences, segment markets, and personalize product offerings. This not only enhances customer satisfaction and loyalty but also drives cross-selling and upselling opportunities. The ability to analyze diverse data sources, including social media, transaction histories, and customer feedback, allows BFSI organizations to predict customer needs and deliver targeted solutions. As a result, Big Data Analytics is becoming an indispensable tool for BFSI enterprises aiming to differentiate themselves in an intensely competitive market.

From a regional perspective, North America remains the largest market for Big Data Analytics in BFSI, accounting for over 38% of global revenue in 2024. This dominance is attributed to the presence of major financial institutions, early adoption of advanced technologies, and a mature regulatory environment. However, the Asia Pacific region is witnessing the fastest growth, with a CAGR exceeding 17% during the forecast period, driven by rapid digitization, expanding banking infrastructure, and increasing investments in analytics solutions by emerging economies such as China and India.

Component Analysis

The Big Data Analytics in BFSI market is segmented by component into Software and Services. The software segment comprises analytics platforms, data management tools, visualization software, and advanced AI-powered solutions. In 2024, the software segment accounted for the largest share

This graph shows the types of sources used by companies using Big Data in France in 2015, according to the sector. According to the source, ** percent of companies in the transport sector used geolocation data. In the area of ​​accommodation and food services, three-quarters of the companies surveyed reported using social media data. The Big Data concept refers to large volumes of data related to usage a good or a service, for example a social network or a connected object such as a GPS. Being able to handle large volumes of data is a big business challenge, as it allows them to better understand how service users behave, making them better able to meet user expectations.

Refined DataCo Smart Supply Chain Geospatial Dataset

Optimized for Geospatial and Big Data Analysis

This dataset is a refined and enhanced version of the original DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS dataset, specifically designed for advanced geospatial and big data analysis. It incorporates geocoded information, language translations, and cleaned data to enable applications in logistics optimization, supply chain visualization, and performance analytics.

Key Features

1. Geocoded Source and Destination Data

• Accurate latitude and longitude coordinates for both source and destination locations.
• Facilitates geospatial mapping, route analysis, and distance calculations.

2. Supplementary GeoJSON Files

• src_points.geojson: Source point geometries.
• dest_points.geojson: Destination point geometries.
• routes.geojson: Line geometries representing source-destination routes.
• These files are compatible with GIS software and geospatial libraries such as GeoPandas, Folium, and QGIS.

3. Language Translation

• Key location fields (countries, states, and cities) are translated into English for consistency and global accessibility.

4. Cleaned and Consolidated Data

• Addressed missing values, removed duplicates, and corrected erroneous entries.
• Ready-to-use dataset for analysis without additional preprocessing.

5. Routes and Points Geometry

• Enables the creation of spatial visualizations, hotspot identification, and route efficiency analyses.

Applications

1. Logistics Optimization

• Analyze transportation routes and delivery performance to improve efficiency and reduce costs.

2. Supply Chain Visualization

• Create detailed maps to visualize the global flow of goods.

3. Geospatial Modeling

• Perform proximity analysis, clustering, and geospatial regression to uncover patterns in supply chain operations.

4. Business Intelligence

• Use the dataset for KPI tracking, decision-making, and operational insights.

Dataset Content

Files Included

1. DataCoSupplyChainDatasetRefined.csv

   • The main dataset containing cleaned fields, geospatial coordinates, and English translations.

2. src_points.geojson

   • GeoJSON file containing the source points for easy visualization and analysis.

3. dest_points.geojson

   • GeoJSON file containing the destination points.

4. routes.geojson

   • GeoJSON file with LineStrings representing routes between source and destination points.

Attribution

This dataset is based on the original dataset published by Fabian Constante, Fernando Silva, and António Pereira:
Constante, Fabian; Silva, Fernando; Pereira, António (2019), “DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS”, Mendeley Data, V5, doi: 10.17632/8gx2fvg2k6.5.

Refinements include geospatial processing, translation, and additional cleaning by the uploader to enhance usability and analytical potential.

Tips for Using the Dataset

• For geospatial analysis, leverage tools like GeoPandas, QGIS, or Folium to visualize routes and points.
• Use the GeoJSON files for interactive mapping and spatial queries.
• Combine this dataset with external datasets (e.g., road networks) for enriched analytics.

This dataset is designed to empower data scientists, researchers, and business professionals to explore the intersection of geospatial intelligence and supply chain optimization.

The Data Analytics in Retail Industry is segmented by Application (Merchandising and Supply Chain Analytics, Social Media Analytics, Customer Analytics, Operational Intelligence, Other Applications), by Business Type (Small and Medium Enterprises, Large-scale Organizations), and Geography. The market size and forecasts are provided in terms of value (USD billion) for all the above segments.

We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive—the largest publicly available archive of FOSS source code with accompanying development history—all versions of files whose names are commonly used to convey licensing terms to software users and developers. The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing. Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared. The dataset is released as open data as an archive file containing all deduplicated license blobs, plus several portable CSV files for metadata, referencing blobs via cryptographic checksums.

For more details see the included README file and companion paper:

Stefano Zacchiroli. A Large-scale Dataset of (Open Source) License Text Variants. In proceedings of the 2022 Mining Software Repositories Conference (MSR 2022). 23-24 May 2022 Pittsburgh, Pennsylvania, United States. ACM 2022.

If you use this dataset for research purposes, please acknowledge its use by citing the above paper.

Controlled source audio-frequency magnetotellurics (CSAMT) data were collected in the Big Chino Valley and Paulden areas, Yavapai County, Arizona, to better understand the hydrogeology of the area. CSAMT data provide vertical cross-section (profile) data about the resistivity of the subsurface, which may be related to lithologic boundaries and (or) grain-size distribution in the subsurface. CSAMT involves transmitting a current at various frequencies in one location, and measuring resistivity differences between electrodes spaced along a receiver line several kilometers from the transmitter. . Big_Chino_Valley_CSAMT_InversionData.zip: Text files of inverted resistivity values, starting model values, and corresponding x, y, z coordinates These files allow the user to recreate the inversions provded in the accompanying kmz file. The zip file contains one text file per line.

Big Data Analytics in Manufacturing Industry Market Outlook

According to our latest research, the Big Data Analytics in Manufacturing Industry market size reached USD 9.3 billion in 2024 globally. The market is experiencing robust expansion, registering a CAGR of 17.2% from 2025 to 2033. By the end of 2033, the market is projected to attain a size of USD 36.4 billion. This impressive growth trajectory is primarily driven by the increasing adoption of Industry 4.0 practices, the proliferation of IoT-enabled devices, and the growing need for real-time data-driven decision-making across the manufacturing sector. As per our latest research, the integration of advanced analytics solutions is reshaping manufacturing operations, enabling enhanced productivity, operational efficiency, and predictive maintenance capabilities worldwide.

The rapid digital transformation within the manufacturing sector is a key growth factor propelling the adoption of big data analytics solutions. Manufacturers are increasingly leveraging data analytics to optimize production processes, reduce downtime, and enhance product quality. The proliferation of connected devices and sensors across shop floors generates massive volumes of data, necessitating sophisticated analytics platforms for meaningful insights. These platforms facilitate real-time monitoring, predictive maintenance, and process optimization, which collectively drive operational excellence. Furthermore, the integration of artificial intelligence and machine learning algorithms with big data analytics enables manufacturers to forecast demand, manage inventory efficiently, and minimize waste, thereby bolstering profitability and competitiveness in an intensely dynamic market.

Another significant driver of growth in the Big Data Analytics in Manufacturing Industry market is the mounting pressure on manufacturers to meet stringent regulatory standards and quality benchmarks. With global supply chains becoming increasingly complex, manufacturers are adopting big data analytics to ensure compliance, traceability, and transparency throughout the production lifecycle. Advanced analytics tools help organizations monitor quality parameters, identify deviations, and implement corrective actions proactively. This not only enhances product reliability but also minimizes the risk of costly recalls and reputational damage. Additionally, big data analytics supports manufacturers in achieving sustainability goals by optimizing energy consumption, reducing emissions, and promoting resource-efficient production methods, which are critical in todayÂ’s environmentally conscious landscape.

The competitive landscape in the manufacturing sector is intensifying, compelling organizations to differentiate themselves through innovation and customer-centricity. Big data analytics empowers manufacturers to gain a deeper understanding of market trends, customer preferences, and emerging opportunities. By harnessing data from diverse sources such as social media, customer feedback, and market reports, manufacturers can tailor their offerings, improve after-sales services, and foster long-term customer relationships. The ability to rapidly adapt to changing market dynamics and consumer demands is a decisive advantage, and big data analytics serves as a cornerstone for agile and responsive manufacturing operations. This strategic focus on data-driven decision-making is expected to fuel sustained market growth over the forecast period.

Manufacturing Analytics is becoming an integral component of the modern manufacturing landscape, offering unprecedented insights into production processes and operational efficiencies. By leveraging advanced analytics techniques, manufacturers can gain a deeper understanding of their operations, from supply chain logistics to production line performance. This data-driven approach allows for the identification of bottlenecks, optimization of resource allocation, and enhancement of product quality. As the manufacturing industry continues to evolve, the role of Manufacturing Analytics in driving innovation and competitiveness is becoming increasingly significant. The integration of real-time data analysis with traditional manufacturing practices is paving the way for smarter, more agile manufacturing environments that can quickly adapt to market changes and consumer demands.

Regionally, the

Introduction: With the increasing fluctuations in the current domestic and international economic situation and the rapid iteration of macroeconomic regulation and control demands, the inadequacy of the existing economic data statistical system in terms of agility has been exposed. It has become a primary task to closely track and accurately predict the domestic and international economic situation using effective tools and measures to compensate for the inadequate economic early warning system and promote stable and orderly industrial production.Methods: Against this background, this paper takes industrial added value as the forecasting object, uses electricity consumption to predict industrial added value, selects factors influencing industrial added value based on grounded theory, and constructs a big data forecasting model using a combination of “expert interviews + big data technology” for economic forecasting.Results: The forecasting accuracy on four provincial companies has reached over 90%.Discussion: The final forecast results can be submitted to government departments to provide suggestions for guiding macroeconomic development.

According to Cognitive Market Research, the global Data Quality Software 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 of

Data Quality Software

The Emergence of Big Data and IoT drives the Market

The rise of big data analytics and Internet of Things (IoT) applications has significantly increased the volume and complexity of data that businesses need to manage. As more connected devices generate real-time data, the amount of information businesses handle grows exponentially. This surge in data requires organizations to ensure its accuracy, consistency, and relevance to prevent decision-making errors. For instance, in industries like healthcare, where real-time data from medical devices and patient monitoring systems is used for diagnostics and treatment decisions, inaccurate data can lead to critical errors. To address these challenges, organizations are increasingly investing in data quality software to manage large volumes of data from various sources. Companies like GE Healthcare use data quality software to ensure the integrity of data from connected medical devices, allowing for more accurate patient care and operational efficiency. The demand for these tools continues to rise as businesses realize the importance of maintaining clean, consistent, and reliable data for effective big data analytics and IoT applications. With the growing adoption of digital transformation strategies and the integration of advanced technologies, organizations are generating vast amounts of structured and unstructured data across various sectors. For instance, in the retail sector, companies are collecting data from customer interactions, online transactions, and social media channels. If not properly managed, this data can lead to inaccuracies, inconsistencies, and unreliable insights that can adversely affect decision-making. The proliferation of data highlights the need for robust data quality solutions to profile, cleanse, and validate data, ensuring its integrity and usability. Companies like Walmart and Amazon rely heavily on data quality software to manage vast datasets for personalized marketing, inventory management, and customer satisfaction. Without proper data management, these businesses risk making decisions based on faulty data, potentially leading to lost revenue or customer dissatisfaction. The increasing volumes of data and the need to ensure high-quality, reliable data across organizations are significant drivers behind the rising demand for data quality software, as it enables companies to stay competitive and make informed decisions.

Key Restraints to

Data Quality Software

Lack of Skilled Personnel and High Implementation Costs Hinders the market growth

The effective use of data quality software requires expertise in areas like data profiling, cleansing, standardization, and validation, as well as a deep understanding of the specific business needs and regulatory requirements. Unfortunately, many organizations struggle to find personnel with the right skill set, which limits their ability to implement and maximize the potential of these tools. For instance, in industries like finance or healthcare, where data quality is crucial for compliance and decision-making, the lack of skilled personnel can lead to inefficiencies in managing data and missed opportunities for improvement. In turn, organizations may fail to extract the full value from their data quality investments, resulting in poor data outcomes and suboptimal decision-ma...

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.

Discover the booming market for open-source storage software! Explore key trends, growth projections (CAGR 15%), leading players (Ceph, GlusterFS, MinIO), and regional insights in this comprehensive analysis. Learn how open-source solutions are transforming cloud storage and data management.

The dataset was compiled to examine the use of ChatGPT 3.5 in educational settings, particularly for creating and personalizing concept maps. The data has been organized into three folders: Maps, Texts, and Questionnaires. The Maps folder contains the graphical representation of the concept maps and the PlanUML code for drawing them in Italian and English. The Texts folder contains the source text used as input for the map's creation The Questionnaires folder includes the students' responses to the three administered questionnaires.

These ESRI shape files are of National Park Service tract and boundary data that was created by the Land Resources Division. Tracts are numbered and created by the regional cartographic staff at the Land Resources Program Centers and are associated to the Land Status Maps. This data should be used to display properties that NPS owns and properties that NPS may have some type of interest such as scenic easements or right of ways.

The real-time lakehouse platform market is experiencing explosive growth, projected to reach a market size of $189 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 26.8% from 2019 to 2033. This robust expansion is fueled by several key drivers. The increasing need for real-time data analytics across diverse industries, including finance, healthcare, and e-commerce, is a significant catalyst. Businesses are increasingly reliant on immediate insights derived from streaming data to make critical decisions and gain a competitive edge. Furthermore, the convergence of data lake and data warehouse technologies within the lakehouse architecture offers a compelling solution for managing and analyzing both structured and unstructured data, efficiently addressing the challenges of data silos and complexity. The market is also propelled by advancements in cloud computing, enabling scalability, cost-effectiveness, and accessibility of real-time lakehouse solutions. Key players like Aliyun, Huawei, Amazon, Oracle, Deepexi, Cloudera, and Esensoft are driving innovation and competition within this rapidly evolving landscape. The market's trajectory is shaped by evolving technological trends. The rise of serverless computing and AI-powered analytics significantly enhance the capabilities of real-time lakehouse platforms. Furthermore, increasing adoption of open-source technologies and improved data governance frameworks are contributing to the market's growth. Despite this positive outlook, the market faces certain challenges. The complexity of implementing and managing these platforms, along with the need for specialized skills and expertise, can hinder adoption. Data security and privacy concerns remain crucial factors impacting market growth. Addressing these challenges through robust security protocols and simplified implementation processes will be critical for sustained market expansion in the coming years. The forecast period of 2025-2033 presents significant opportunities for growth, driven by continued technological innovation and increasing enterprise demand for efficient and insightful real-time data analytics.