The global big data analytics market size was valued at $307.52 billion in 2023 & is projected to grow from $348.21 billion in 2024 to $961.89 billion by 2032

Big Data and Society Abstract & Indexing - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with ** percent of the companies reporting the technology will create jobs and * percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with ** percent of organizations expecting the technology to displace jobs and ** percent expecting AI to create jobs.

The global big data and business analytics (BDA) market was valued at ***** billion U.S. dollars in 2018 and is forecast to grow to ***** 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 ** 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 **** 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 **** 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 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.

Hadoop Big Data Analytics Market size was valued at USD 61.6 Billion in 2024 and is projected to reach USD 968.89 Billion by 2031, growing at a CAGR of 45.36% during the forecast period 2024-2031.

Global Hadoop Big Data Analytics Market Drivers

Explosive Growth of Data: One of the main factors propelling the Hadoop big data analytics market is the exponential growth of data collected across multiple sectors, such as social media, IoT devices, and enterprise applications. Large datasets may be stored, processed, and analysed with Hadoop, which is a scalable and affordable option for enterprises looking to extract value from this enormous amount of data.

Cost-Effectiveness: Businesses looking to analyse massive volumes of data may find traditional data warehousing solutions unaffordable due to their high prices. An affordable substitute is provided by the open-source Hadoop framework, which uses distributed computing and commodity hardware to drastically lower infrastructure costs.

Flexibility and Scalability: Hadoop's distributed computing architecture facilitates smooth scalability, enabling businesses to grow their data infrastructure in response to changing needs. Its adaptability to manage a range of data kinds, such as unstructured, semi-structured, and structured data, further makes it a desirable option for businesses interacting with a variety of data sources.

Advanced Analytics Capabilities: Machine learning, real-time processing, and predictive analytics are just a few of the advanced analytics jobs that organisations can carry out thanks to the abundance of tools and frameworks included in Hadoop's ecosystem, including Apache Spark, Hive, and HBase. With the use of these skills, businesses may extract useful insights from their data, resulting in better decision-making and a competitive advantage.

Growing Need for Real-Time Insights: Being able to glean real-time insights from data is critical in the fast-paced business world of today. When used in conjunction with Apache Kafka and Spark Streaming, Hadoop enables real-time data processing and analytics, allowing businesses to react quickly to shifting consumer preferences and market conditions.

Big Data and Society Acceptance Rate - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

Big Data Enabled Market Outlook

The global big data enabled market size was valued at approximately USD 274 billion in 2023 and is projected to reach USD 830 billion by 2032, growing at a robust CAGR of around 13%. This impressive growth trajectory is driven by the increasing adoption of big data analytics across various industry verticals, fueled by the incessant demand for data-driven decision-making and the proliferation of connected devices generating a massive amount of data. The rise in digital transformation initiatives, coupled with the continuous advancements in data processing technologies, is significantly enhancing the value derivation from big data, making it an indispensable tool for modern businesses.

One of the primary growth factors in the big data enabled market is the exponential increase in data generation. With the continuous growth of internet users, social media interactions, and the advent of IoT devices, the volume of data generated daily has reached unprecedented levels. Organizations are increasingly recognizing the potential of this data to unlock valuable insights, optimize operations, and create new revenue streams. As a result, there is a growing investment in big data technologies to harness this data effectively. Furthermore, advancements in machine learning and artificial intelligence are further enhancing the capabilities of big data analytics, allowing businesses to predict trends, understand consumer behavior, and make informed decisions with greater accuracy.

Another significant factor contributing to the market's growth is the increasing adoption of cloud-based big data solutions. The cloud offers scalability, flexibility, and cost-effectiveness, enabling organizations of all sizes to leverage big data technologies without the need for substantial upfront investments in infrastructure. This has democratized access to big data analytics, particularly benefiting small and medium enterprises (SMEs) that can now compete with larger players by leveraging cloud-based solutions to gain insights from data. The shift towards cloud-based deployments is further accelerated by the growing trend of remote work and digital collaboration, which necessitates the need for accessible and scalable data solutions.

The demand for enhanced customer experiences and personalized services is also driving the adoption of big data in industries such as retail, BFSI, and healthcare. Businesses are leveraging big data analytics to gain a deeper understanding of customer preferences, behaviors, and purchasing patterns, enabling them to tailor their offerings and improve customer satisfaction. In the healthcare sector, big data is revolutionizing patient care by providing insights into patient data, facilitating early diagnosis, and enabling personalized treatment plans. Similarly, in the BFSI sector, big data analytics is being used for fraud detection, risk management, and customer segmentation, leading to more efficient operations and improved customer service.

Big Data Technology is at the forefront of this transformation, providing the tools and frameworks necessary to process and analyze vast datasets efficiently. As organizations strive to become more data-driven, the integration of Big Data Technology into their operations is proving to be a game-changer. These technologies enable businesses to not only handle large volumes of data but also to derive meaningful insights that drive strategic decisions. The evolution of Big Data Technology continues to push the boundaries of what is possible, offering new opportunities for innovation and growth across various sectors.

Regionally, North America holds a significant share of the big data enabled market, driven by the presence of key technology players, high adoption rates across industries, and a strong focus on technological innovation. The region is expected to maintain its dominance over the forecast period, supported by continued investments in big data infrastructure and research. Meanwhile, the Asia Pacific region is anticipated to witness the highest growth, fueled by rapid digitalization, increasing internet penetration, and rising adoption of big data technologies in countries such as China and India. The growing focus on smart cities and government initiatives promoting data-driven decision-making are further propelling the market in this region.

Component Analysis

In the big data enabled market, the component segment is categorized into software, hardware, and ser

According to Cognitive Market Research, the global SME Big Data market size is USD xx million in 2024. It will expand at a compound annual growth rate (CAGR) of 4.60% from 2024 to 2031. North America held the major market share for more than 40% of the global revenue with a market size of USD xx million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.8% from 2024 to 2031. Europe accounted for a market share of over 30% of the global revenue with a market size of USD xx million. Asia Pacific held a market share of around 23% of the global revenue with a market size of USD xx million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.6% from 2024 to 2031. Latin America had a market share for more than 5% of the global revenue with a market size of USD xx million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.0% from 2024 to 2031. Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD xx million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.3% from 2024 to 2031. The Software held the highest SME Big Data market revenue share in 2024. Market Dynamics of SME Big Data Market Key Drivers for SME Big Data Market Growing Recognition of Data-Driven Decision Making The growing recognition of data-driven decision making is a key driver in the SME Big Data market as businesses increasingly understand the value of leveraging data for strategic decisions. This shift enables SMEs to optimize operations, enhance customer experiences, and gain competitive advantages. Access to affordable big data technologies and analytics tools has democratized data usage, making it feasible for smaller enterprises to adopt these solutions. SMEs can now analyze market trends, customer behaviors, and operational inefficiencies, leading to more informed and agile business strategies. This recognition propels demand for big data solutions, as SMEs seek to harness data insights to improve outcomes, innovate, and stay competitive in a rapidly evolving business landscape. Growing Number of Affordable Big Data Solutions The growing number of affordable big data solutions is driving the SME Big Data market by lowering the entry barrier for smaller enterprises to adopt advanced analytics. Cost-effective technologies, particularly cloud-based services, allow SMEs to access powerful data analytics tools without substantial upfront investments in infrastructure. This affordability enables SMEs to harness big data to gain insights into customer behavior, streamline operations, and enhance decision-making processes. As a result, more SMEs are integrating big data into their business models, leading to improved efficiency, innovation, and competitiveness. The availability of scalable and flexible solutions tailored to SME needs further accelerates adoption, making big data analytics an accessible and valuable resource for small and medium-sized businesses aiming for growth and success. Restraint Factor for the SME Big Data Market High Initial Investment Cost to Limit the Sales High initial costs are a significant restraint on the SME Big Data market, as they can deter smaller businesses from adopting big data technologies. Implementing big data solutions often requires substantial investment in hardware, software, and skilled personnel, which can be prohibitively expensive for SMEs with limited budgets. These costs include purchasing or subscribing to analytics platforms, upgrading IT infrastructure, and hiring data scientists or analysts. The financial burden associated with these initial expenses can make SMEs hesitant to commit to big data projects, despite the potential long-term benefits. Consequently, high initial costs limit the accessibility of big data analytics for SMEs, slowing the market's overall growth and the widespread adoption of these transformative technologies among smaller enterprises. Impact of Covid-19 on the SME Big Data Market The COVID-19 pandemic significantly impacted the SME Big Data market, accelerating digital transformation as businesses sought to adapt to rapidly changing conditions. With disruptions in traditional operations and a shift towards remote work, SMEs increasingly turned to big data analytics to maintain efficiency, manage supply chains, and understand evolving customer behaviors. The pandemic underscored the importance of real-time data insights for agile decision-making, dr...

Big Data Analytics in Banking Market size was valued at USD 5.67 Billion in 2024 and is projected to reach USD 12.89 Billion by 2031, growing at a CAGR of 10.8% during the forecast period 2024-2031.

Global Big Data Analytics In Banking Market Drivers

Big data analytics assists banks in understanding consumer habits, preferences, and needs by analyzing enormous amounts of data from a variety of sources, including transaction records, social media, mobile engagements, and web visits. This allows banks to modify their products and services, providing personalized banking experiences that greatly increase consumer happiness and loyalty, hence driving market development.

Banks operate in a highly regulated environment, where risk management and compliance are critical. Big data analytics provides instruments for effective risk monitoring, analysis, and management. It aids in the detection of fraudulent activity by spotting anomalous trends, analyzing credit risks, and guaranteeing regulatory compliance through continuous monitoring of the transactions that banks handle daily, thus accelerating market growth.

Furthermore, big data analytics help banks become more efficient and cost-effective. Banks can uncover inefficiencies and areas for improvement by examining data from their processes and client interactions. This results in enhanced resource management, lower costs due to regular work automation, and better decision-making processes, all of which help to drive market expansion.

Business intelligence and analytics software publishers' revenue is expected to swell at a compound annual rate of 1.7% over the five years through 2025-26 to reach £964.5 million. Strong growth has been fuelled by rising business software investment, IT and telecommunications adoption, advances in computing technology and the digitalisation of business processes. This has driven the advent of big data, providing new data sets which can interface with business analytics software. Many software products, including customer relationship management and enterprise resource planning systems, have become basic tools for managing large companies. The largest publishers have pursued acquisition activity to take control of cloud companies and data analytics businesses. These industry giants are generally selective with acquisitions, embracing the switch to software as a service and adopting the low-cost cloud model. Leading BI suites, LIKE Tableau, SAP Analytics Cloud, Qlik Sense and IBM’s Cognos Analytics, have all transformed to provide real-time KPI dashboards and robust remote management capabilities, supporting decentralised operations. Intensified merger and acquisition activity, particularly by SAP, has allowed major software publishers to rapidly enhance product ecosystems with niche digital adoption and enterprise architecture tools, further cementing their dominance and spurring innovation. As remote work became the new norm and businesses faced the necessity of managing expansive data sets efficiently, they turned to analytics software. Despite fiscal stresses, companies continued investing in software subscriptions, recognising the indispensable use of applications in a remote work environment. As such, subscriptions and sales of cloud-based software witnessed noticeable growth. Revenue is forecast to climb by 1.7% in 2025-26, with profit also expected to edge up as demand remains strong. Over the five years through 2030-31, revenue is expected to climb at a compound annual rate of 3% to reach £1.1 billion. Heightened adoption of industry-specific software among small and medium-size enterprises (SMEs) is projected to fuel growth. Ongoing e-commerce expansion, which has seen the online share of retail sales climb steadily, will keep demand for BI and analytics tools rising as retailers and supply chains seek deeper insights into customer behaviour and operational efficiencies. Cloud adoption will remain central, with hybrid and distributed models expected to persist, yet competition from cloud infrastructure giants like Amazon Web Services is likely to intensify. Investment in artificial intelligence and machine learning is anticipated to accelerate, with publishers needing to embed AI-driven analytics and automation to stay competitive, bolstered by the UK’s substantial public and private AI investment. However, talent shortages and heightened corporation tax could dampen growth, particularly for smaller publishers struggling to absorb higher costs or secure skilled staff. The industry's resilience will hinge on strategic upskilling, smart automation and continued innovation, ensuring UK BI and analytics software remains at the forefront of enterprise digital transformation.

The Indian analytics market, a significant contributor to the global landscape, is experiencing robust growth, driven by the increasing adoption of data-driven decision-making across diverse sectors. The market, valued at approximately $2.17 billion in 2025 (based on provided global data and considering India's significant contribution to the IT sector), is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 7.66% through 2033. This expansion is fueled by several key factors, including the burgeoning digital economy, rising government initiatives promoting digitalization, and the expanding adoption of cloud computing and big data technologies. The BFSI (Banking, Financial Services, and Insurance) sector, along with Retail and Telecommunications/IT, are major drivers, exhibiting strong demand for advanced analytics solutions to enhance customer experience, optimize operations, and mitigate risks. The market is segmented by solution type (software, services), organization size (SME, large enterprises), and end-user vertical, offering diverse opportunities for both established players and emerging companies. While data privacy concerns and the need for skilled professionals present some challenges, the overall outlook remains positive, with significant potential for continued expansion. The competitive landscape is characterized by a mix of global giants like SAS Institute, IBM, and TIBCO, and strong domestic players like Wipro, Infosys, and Mu Sigma. These companies are actively investing in research and development, strategic partnerships, and talent acquisition to maintain their market position. The increasing demand for specialized services, such as managed analytics services, is creating opportunities for businesses specializing in providing tailored solutions. The government's emphasis on digital infrastructure and data security is shaping the regulatory environment, fostering both growth and responsible data handling practices. Looking ahead, the Indian analytics market is expected to witness further consolidation, increased investment in AI and machine learning technologies, and a growing adoption of predictive and prescriptive analytics across sectors, creating a dynamic and evolving market with immense potential for innovation and growth. This comprehensive report provides a detailed analysis of the burgeoning Analytics Market in India, projecting significant growth from ₹XXX Million in 2025 to ₹XXX Million by 2033. Covering the historical period (2019-2024), base year (2025), and forecast period (2025-2033), this study offers invaluable insights for businesses navigating this dynamic landscape. We delve into market segmentation by type (solution, managed services), organization size (SME, large enterprise), and end-user vertical (BFSI, retail, IT & telecom, media & entertainment, healthcare, others). Key players like SAS Institute Inc, TIBCO Software Inc, IBM Corporation, Wipro Ltd, Sigma Data Systems, Capgemini SE, Mu Sigma Business Solutions Pvt Ltd, Fractal Analytics Limited, WNS (Holdings) Ltd, and Infosys Ltd are analyzed for their market share and strategic moves. Recent developments include: May 2023: with the introduction of its DIgital Content Ratings Solutions (DCR), Nielsen, a solution in audience measurement, data and analytics has once again shown its dedication to impartial, digital audience content measuremnt in India. Nielsen's Identity System, which will leverage the same big data as the market's Digital Ad Ratings, is intended to fuel DCR in India., November 2022: Wipro Ltd. partnered with a US-based cloud computing service provider, VMware. The partnership was to see Wipro offer VMware's cloud computing and remote work platform, allowing enterprises to offer security standards and other services to a distributed workforce., September 2022: Sigma Computing announced its partnership with Snowflake, the Data Cloud company, to launch a seamless integration experience for joint customers on the Snowflake Healthcare & Life Sciences Data Cloud. The Snowflake Healthcare & Life Sciences Data Cloud offered healthcare companies a single, integrated, and cross-cloud data platform that eliminated technical and institutional data silos to centralize securely, integrate, and exchange critical and sensitive data at scale.. Key drivers for this market are: Reduction in Cost of Implementation will act as a Driver, Increasing Number of Connected Devices. Potential restraints include: Structural Barriers and Decentralized Systems act as a Restraint, Lack of Skilled Professionals. Notable trends are: The BFSI Segment is Expected to Drive the Market's Growth.

This statistic displays the adoption rates of Big Data analytics in the United Kingdom (UK) in 2015 and 2020. In 2015, the adoption rate amounted to 56 percent across all examined industry. 67 percent of the industries will adopt Big Data analytics in 2020.

Global Data Analytics market size is expected to reach $257.96 billion by 2029 at 28.4%, segmented as by big data analytics, predictive analytics, prescriptive analytics, descriptive analytics

Scraped Data on AI, ML, DS & Big Data Jobs is a comprehensive dataset that includes valuable information about job opportunities in the fields of Artificial Intelligence (AI), Machine Learning (ML), Data Science (DS), and Big Data. The dataset covers various aspects, including company names, job titles, locations, job types (full-time, part-time, remote), experience levels, salary ranges, job requirements, and available facilities.

This dataset offers a wealth of insights for job seekers, researchers, and organizations interested in the rapidly evolving fields of AI, ML, DS, and Big Data. By analyzing the data, users can gain a better understanding of the job market trends, geographical distribution of opportunities, popular job titles, required skills and qualifications, salary expectations, and the types of facilities provided by companies in these domains.

Whether you are exploring career prospects, conducting market research, or building predictive models, this dataset serves as a valuable resource to extract meaningful insights and make informed decisions in the exciting world of AI, ML, DS, and Big Data jobs.

This dataset is about books. It has 3 rows and is filtered where the book is Big data demystified : how to use big data, data science and AI to make better business decisions and gain competitive advantage. It features 7 columns including author, publication date, language, and book publisher.

This dataset has been created for the purpose of studying the impact of various components of data technology on the effectiveness of decision-making processes within the retail industry. The analysis consists of a structural model containing five independent components: Business Analytics and Intelligence, Big Data, Predictive Analytics, Data Quality, and Data Analysis, each of which is assessed against the dependent variable: Decision Making Effectiveness in the Retail Industry.

Data collection is performed using a structured questionnaire which was administered to 455 students or people who have good knowledge about the research model that is used in this study. from Greater Jakarta during the period of May 2 to July 15, 2025. This study applies the technique of Structural Equation Modelling (SEM) to study the relationships between variables, making it possible to evaluate the contribution of each data technology factor on decision making effectiveness.

The dataset is comprised of both the observed measurements as well as the latent constructs derived from the validated indicators, thus providing a sound statistical basis for testing the hypotheses and validating the model. Furthermore, the implementation of SEM provides in-depth analysis concerning the direct and indirect relationships of data technologies concerning the decisions in the retail sector.

Important steps in the methodology are Model Specification, Path Analysis, Evaluation of Goodness of Fit, and Hypothesis Testing. The work provides an example of how advanced analytics are being applied in the retail sector, showcasing the pervasive role of data and analytics in facilitating evidence-based decision-making in an increasingly competitive landscape.

Big data is expected to be a huge creator of jobs in the future. This source estimates that by 2015, there will be an additional *** million IT jobs internationally in this field and that *** million of these jobs will be based in the United States.

This dataset is about book subjects. It has 5 rows and is filtered where the books is Big data made easy : a working guide to the complete Hadoop toolset. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Big Data and Society CiteScore 2024-2025 - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus