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

What is Big data?

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

Big data analytics

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

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

The Big Data Analytics in Banking Market is Segmented by Type of Solutions (Data Discovery and Visualization (DDV) and Advanced Analytics (AA)), and Geography (North America, Europe, Asia-Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD Million) for all the Above Segments.

This statistic shows the size of the global big data analytics services market related to healthcare in 2016 and a forecast for 2025, by application. It is predicted that by 2025 the market for health-related financial analytics services using big data will increase to over 13 billion U.S. dollars.

The need for advanced analytical approaches to provide HPDA solutions is driving the market growth of High Performance Data Analytics (HPDA). According to the analyst from Verified Market Research, The High Performance Data Analytics (HPDA) Market is estimated to reach a valuation of USD 597.06 Billion over the forecast period 2031, by subjugating around USD 113.23 Billion in 2023.

The adoption of an open-source framework for big data analytics is driving market growth. This surge in demand enables the market to grow at a CAGR of 23.1% from 2024 to 2031.

High Performance Data Analytics (HPDA) Market: Definition/ Overview

HPDA refers to big data analytics that uses High-Performance Computing (HPC) techniques. Big data analytics has always relied on high-performance computing (HPC), but as data grows exponentially, new forms of high-performance computing will be required to access previously unimaginable volumes of data. The combination of big data analytics and high-performance computing is called “high-performance data analytics.” High-performance data analytics is the process of quickly finding insights from large data sets by running powerful analytical tools in parallel on high-performance computing systems.

Furthermore, high-performance data analytics infrastructure is a rapidly expanding market for government and commercial organizations that need to combine high-performance computing with data-intensive analysis. For complex modeling and simulations, big data analytics techniques like Hadoop and Spark have long required high-performance computing, which they lack.

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Big Data as a Service Market Size 2024-2028

The big data as a service market size is forecast to increase by USD 41.20 billion at a CAGR of 28.45% between 2023 and 2028.

The market is experiencing significant growth due to the increasing volume of data and the rising demand for advanced data insights. Machine learning algorithms and artificial intelligence are driving product quality and innovation in this sector. Hybrid cloud solutions are gaining popularity, offering the benefits of both private and public cloud platforms for optimal data storage and scalability. Industry standards for data privacy and security are increasingly important, as large amounts of data pose unique risks. The BDaaS market is expected to continue its expansion, providing valuable data insights to businesses across various industries.

What will be the Big Data as a Service Market Size During the Forecast Period?

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Big Data as a Service (BDaaS) has emerged as a game-changer in the business world, enabling organizations to harness the power of big data without the need for extensive infrastructure and expertise. This service model offers various components such as data management, analytics, and visualization tools, enabling businesses to derive valuable insights from their data. BDaaS encompasses several key components that drive market growth. These include Business Intelligence (BI), Data Science, Data Quality, and Data Security. BI provides organizations with the ability to analyze data and gain insights to make informed decisions.



Data Science, on the other hand, focuses on extracting meaningful patterns and trends from large datasets using advanced algorithms. Data Quality is a critical component of BDaaS, ensuring that the data being analyzed is accurate, complete, and consistent. Data Security is another essential aspect, safeguarding sensitive data from cybersecurity threats and data breaches. Moreover, BDaaS offers various data pipelines, enabling seamless data integration and data lifecycle management. Network Analysis, Real-time Analytics, and Predictive Analytics are other essential components, providing businesses with actionable insights in real-time and enabling them to anticipate future trends. Data Mining, Machine Learning Algorithms, and Data Visualization Tools are other essential components of BDaaS.

How is this market segmented and which is the largest segment?

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

Type

  Data analytics-as-a-Service
  Hadoop-as-a-service
  Data-as-a-service


Deployment

  Public cloud
  Hybrid cloud
  Private cloud


Geography

  North America

    Canada
    US


  APAC

    China


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Type Insights

The data analytics-as-a-service segment is estimated to witness significant growth during the forecast period.

Big Data as a Service (BDaaS) is a significant market segment, highlighted by the availability of Hadoop-as-a-Service solutions. These offerings enable businesses to access essential datasets on-demand without the burden of expensive infrastructure. DAaaS solutions facilitate real-time data analysis, empowering organizations to make informed decisions. The DAaaS landscape is expanding rapidly as companies acknowledge its value in enhancing internal data. Integrating DAaaS with big data systems amplifies analytics capabilities, creating a vibrant market landscape. Organizations can leverage diverse datasets to gain a competitive edge, driving the growth of the global BDaaS market. In the context of digital transformation, cloud computing, IoT, and 5G technologies, BDaaS solutions offer optimal resource utilization.

However, regulatory scrutiny poses challenges, necessitating stringent data security measures. Retail and other industries stand to benefit significantly from BDaaS, particularly with distributed computing solutions. DAaaS adoption is a strategic investment for businesses seeking to capitalize on the power of external data for valuable insights.

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The Data analytics-as-a-Service segment was valued at USD 2.59 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 35% to the growth of the global market during the forecast period.

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

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Big Data as a Service Market analysis, North America is experiencing signif

The Big Data Consulting Market Report is Segmented by Service Type (Strategic Consulting, Implementation Services, Analytics and Insights, Managed Services, Training and Support), Deployment Model (On-Premise, Cloud-Based, Hybrid), Organization Size (Small and Medium Enterprises (SMEs), Large Enterprises), Application (Customer Analytics, Operational Analytics, Risk and Fraud Management, Supply Chain Management, Marketing and Sales Analytics, Predictive Maintenance, Financial Analytics, Other Applications), and Geography (North America, Europe, Asia Pacific, Latin America, Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

In 2022, around 77 percent of companies that used big data analysis and related services in South Korea did so with public data. Following this was the analysis of customer information, at around 39 percent.

Scientific and related management challenges in the water domain require synthesis of data from multiple domains. Many data analysis tasks are difficult because datasets are large and complex; standard formats for data types are not always agreed upon nor mapped to an efficient structure for analysis; water scientists may lack training in methods needed to efficiently tackle large and complex datasets; and available tools can make it difficult to share, collaborate around, and reproduce scientific work. Overcoming these barriers to accessing, organizing, and preparing datasets for analyses will be an enabler for transforming scientific inquiries. Building on the HydroShare repository’s established cyberinfrastructure, we have advanced two packages for the Python language that make data loading, organization, and curation for analysis easier, reducing time spent in choosing appropriate data structures and writing code to ingest data. These packages enable automated retrieval of data from HydroShare and the USGS’s National Water Information System (NWIS), loading of data into performant structures keyed to specific scientific data types and that integrate with existing visualization, analysis, and data science capabilities available in Python, and then writing analysis results back to HydroShare for sharing and eventual publication. These capabilities reduce the technical burden for scientists associated with creating a computational environment for executing analyses by installing and maintaining the packages within CUAHSI’s HydroShare-linked JupyterHub server. HydroShare users can leverage these tools to build, share, and publish more reproducible scientific workflows. The HydroShare Python Client and USGS NWIS Data Retrieval packages can be installed within a Python environment on any computer running Microsoft Windows, Apple MacOS, or Linux from the Python Package Index using the PIP utility. They can also be used online via the CUAHSI JupyterHub server (https://jupyterhub.cuahsi.org/) or other Python notebook environments like Google Collaboratory (https://colab.research.google.com/). Source code, documentation, and examples for the software are freely available in GitHub at https://github.com/hydroshare/hsclient/ and https://github.com/USGS-python/dataretrieval.

This presentation was delivered as part of the Hawai'i Data Science Institute's regular seminar series: https://datascience.hawaii.edu/event/data-science-and-analytics-for-water/

The Big Data Analytics Software market is projected to reach $900.3 million by 2033, exhibiting a CAGR of 8.9% from 2025 to 2033. The expanding volume of data generated across industries, coupled with the growing need for efficient data analysis and decision-making, is driving market growth. Cloud-based deployment models and the proliferation of artificial intelligence (AI) and machine learning (ML) technologies are also contributing to market growth. However, data security and privacy concerns, as well as the skills gap in big data analytics, pose challenges to market expansion. North America dominates the market, followed by Europe and Asia Pacific. Large enterprises constitute the primary customer base, leveraging big data analytics software to improve operational efficiency, customer service, and product development. Key players in the market include Sisense, Looker, Zoho Analytics, Yellowfin, Domo, Qlik Sense, GoodData, Birst, IBM, MATLAB, and Google Analytics. These companies offer a range of solutions tailored to different industry verticals and data analysis needs, driving innovation and competition in the market.

This statistic illustrates the share of analytics models used by Big data and Business Intelligence (BI) companies in Italy in 2017. That year, all the interviewed companies reported using descriptive analytics, while only eleven percent used automated analytics.

The market size of the Big Data Analytics In The Energy Sector Market is categorized based on Type (On-premise, Cloud-based) and Application (Grid Operations, Smart Metering, Asset and Workforce Management) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

This report provides insights into the market size and forecasts the value of the market, expressed in USD million, across these defined segments.

Market Overview and Key Drivers: The Big Data Analytics in Banking market is rapidly expanding, with a projected CAGR of XX% from 2025 to 2033. This growth is fueled by an increasing need for banks to enhance customer experiences, reduce risks, and optimize operations. The market is driven by factors such as:

Rising data volumes and complexity Need for personalized customer interactions Growing focus on risk management and compliance Demand for predictive and prescriptive analytics

Market Segments and Key Players: The Big Data Analytics in Banking market is segmented by type (on-premise, cloud), application (feedback management, customer analytics, social media analytics, fraud detection and management), and region. Major players in the market include IBM, Oracle, SAP SE, Microsoft, HP, Amazon AWS, Google, Hitachi Data Systems, Tableau, and New Relic. The market is expected to be dominated by the cloud segment and customer analytics applications due to increasing cloud adoption and the need for customer-centric approaches. North America is anticipated to hold the largest market share, followed by Europe and Asia Pacific.

The Report Covers Global Big Data Services Market Size & Industry Share and It is Segmented by Deployment Type (On-Premise and Cloud), End-User (Telecom and IT, Energy and Power, BFSI, Healthcare, Retail, and Other End-Users), and Geography (North America, Europe, Asia Pacific, Latin America, and Middle East and Africa). The Market Size and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

The big data and analytics (BDA) software market has seen an incremental increase in annual revenue worldwide from 2011 to 2019, with a slight exception in 2015. In 2019, the worldwide revenue from business analytics software amounted to 67 billion U.S. dollars.

Big data and analytics software market
The BDA software market can be broken down into three main categories: business intelligence analytic tools and platforms, analytic data management and integration platforms, and analytic and performance management applications. Simply put, the BDA software market provides business solutions in various industries through the use of analytical software tools in order to support the full life cycle of data integration, intelligence, analysis, visualization, and other related decision support systems or decision automation functions. The vendors who lead this big data and analytics software market include Microsoft, Oracle, and SAP.

Migration to the cloud
The BDA software market is continually experiencing migration to the cloud. As of 2019, the cloud services portion grew tremendously and now takes up about a quarter of the total revenue of the BDA software market. It would not be a surprise if the increase in cloud services may be contributing to the total size of the public cloud software as a service (SaaS) market that has seen an increase in recent years.

The Industrial Analytics Market size was valued at USD 25.11 Billion in the year 2024, and it is expected to reach USD 97.38 Billion in 2031, at a CAGR of 18.46% from 2024 to 2031.

Key Market Drivers
Rise of Industry 4.0: The rise of Industry 4.0 is leading to the creation of vast amounts of data from sensors, machines, and other industrial equipment. This data is then analyzed by industrial analytics solutions to optimize processes, improve efficiency, and gain valuable insights into operations.
Proliferation of IoT and IIoT: The proliferation of IoT and IIoT devices is resulting in a massive amount of data generation. Industrial analytics solutions are being employed to analyze this data.
Big Data Adoption: Big data is increasingly being recognized by businesses as a valuable asset for informed decision-making. Industrial analytics plays a critical role in processing and analyzing large datasets from various industrial sources, thereby enabling data-driven decision-making for improved performance.
Cloud Technology Advancement: The advancement of cloud technology is offering scalability, flexibility, and cost-effectiveness for businesses. This growth in cloud computing is facilitating the widespread use of industrial analytics, making it accessible to a wider range of organizations.

The size and share of the market is categorized based on Type (Capturing Data, Storing Data, Sharing Data, Analyzing Data, Others) and Application (Crop Production, Pesticide Company, Other) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The global Big Data Analytics in Energy Market size was valued at USD 26.53 billion in 2025 and is projected to grow from USD 33.08 billion in 2026 to USD 80.81 billion by 2033, exhibiting a CAGR of 9.8% during the forecast period. The market growth is attributed to the increasing need for efficient energy management, rising adoption of smart grids, and advancements in data analytics technologies. The market is segmented based on analytics type, deployment model, application sector, end user, and region. By analytics type, the market is divided into descriptive analytics, predictive analytics, prescriptive analytics, and diagnostic analytics. By deployment model, the market is classified into on-premises, cloud-based, and hybrid. By application sector, the market is segmented into utility management, renewable energy management, energy trading and risk management, and energy consumption optimization. By end user, the market is categorized into residential, commercial, and industrial. By region, the market is segmented into North America, South America, Europe, the Middle East & Africa, and Asia Pacific. Key drivers for this market are: 1. Predictive maintenance solutions 2. Renewable energy integration 3. Enhanced asset management 4. Real-time data analytics 5. Regulatory compliance support. Potential restraints include: 1. Growing energy data volume 2. Enhanced operational efficiency 3. Regulatory compliance pressures 4. Demand for predictive analytics 5. Rising focus on renewable energy.