Global Big Data Market Size, Share & Industry Trends Analysis Report By Component, By Business Function, By Deployment Type, By Organization Size, By Vertical By Regional Outlook and Forecast, 2021 - 2027
発行: KBV Research
ページ情報: 英文 432 Pages
List of Figures
The Global Big Data Market size is expected to reach $300.7 billion by 2027, rising at a market growth of 10.9% CAGR during the forecast period.
Big data is described as large amounts of unstructured or semi-structured data that have been accumulated over time. Big data is frequently unstructured and scattered. The term "Big Data" also refers to the sheer amount of data accumulated, as well as its speed, velocity, diversity, and complexity. Traditional database management systems struggle to process and/or handle these massive data collections, which are measured in petabytes/exabytes of data. Big data is defined in this study as an enormous dataset that does not fit into standard business database designs. RFID readers, social networks, sensor networks, internet text and documents, phone registers, internet search indexing, scientific research studies, medical records, military surveillance, and eCommerce, among many other sources, generate big data.
Big data combines information from a variety of sources and applications. Extract, transform, and load (ETL) are common data integration procedures that aren't up to the challenge. To evaluate large data sets on a terabyte or even petabyte-scale, new methodologies and technologies are required. During the integration process, one must bring in the data, process it, and ensure that it is prepared and available in a way that the business analysts can use.
Space for storage is required for big data. The storage solution can be cloud-based, on-premises, or a combination of both. Users can store their data in any format they want, and then apply their processing needs and process engines to those data sets as needed. The decision by people regarding where to store their data is based on where their data is currently stored. Since it satisfies businesses' present computation requirements and allows them to spin up resources as needed, the cloud is quickly gaining appeal.
Users can make new findings with larger data sets. To that aim, new investments in skills, organization, or infrastructure must be made in the context of a strong business-driven environment in order to ensure continuous project investments and funding. Understanding how to filter site logs to analyze e-commerce activity, extracting sentiment from social media and customer support contacts, and statistical correlation approaches and their significance for the customer, product, manufacturing, and engineering data are just a few examples.
COVID-19 has had three primary repercussions on the global economy: it has had a direct and negative influence on production and demand, disrupted supply chains and marketplaces, and impacted enterprises and financial markets financially. The COVID-19 pandemic, on the other hand, has had a beneficial impact on the Big Data market, since the usage of Big Data has expanded in order to better comprehend the impact of COVID-19 on the economy. As the pandemic continues and new waves keep coming, the impact of the outbreak on the big data market could be negative. This is due to governments in the majority of countries imposing a state of emergency and prohibiting travel to prevent the spread of the virus. However, following the recovery from the COVID-19 pandemic, the big data and business analytics market is expected to grow in the following years.
Market Growth Factors
Exponential Growth in Data Availability
Owing to the increase in the popularity of social media, the Internet of Things (IoT), and multimedia, which have produced an excessive flow of data in either structured or unstructured format, the volume of data gathered by enterprises is constantly expanding. The growth of data creation has been exponential in the past few years, especially after the wide proliferation of smartphones and social media. Machine-generated as well as human-generated data is growing at a rate ten times faster than traditional commercial data. Machine data is growing at a much quicker rate than human data. Big data is predominantly consumer-driven and oriented; most of the data in the world is created by 'always-on' customers. Typically, people spend a vast proportion of each day using a range of gadgets and (social) applications to consume and generate data.
Development of Novel Trends like Media Analytics
Organizations are seeking to harness information assets to improve customer connections, business outcomes, and operational efficiency, and big data analytics is in high demand. However, keeping up with the shifting demands and expectations of a growing number of big data analytics consumers has grown more difficult. Emerging developments in big data analytics, such as social media analytics and text analytics, on the other hand, are expected to open up a slew of new prospects for the sector. Moreover, because social media is more effective than traditional advertising, many organizations have made it their primary source for various advertising campaigns, product promotions, and events. The ever-increasing competition among businesses is forcing them to use big data analytics to improve their growth.
Market Restraining Factors
Privacy Concerns and Strict Data Security Regulations Implemented by the Government
On big data platforms, users save sensitive data and information about company activities. However, handling and preserving the documents can expose to a variety of risks and vulnerabilities. As the platform grows in popularity, so do security concerns about data breaches, unforeseen events, application vulnerabilities, and information loss. Some sectors, such as academia and research, federal government departments, and financial services, may experience revenue reductions as a result of information security and privacy concerns. This can significantly harm a company's reputation and, as a result, undermine the confidence of those in charge. As a result, criminal sanctions and even legal culpability may be imposed. Cybercriminals can subvert important company information and engage in unlawful transactions by storing sensitive information and data in databases and on the cloud. As a result, security and privacy concerns are projected to hamper market growth over the forecast period.
Based on Component, the market is segmented into Solution (Data Discovery, Big Data Analytics, Data Visualization, and Data Management) and Services. The services segment observed a substantial revenue share in the big data market in 2020. Big data consulting services include big data strategy, real-time big data processing, machine learning, data platform management, and analytics solutions, and there are various organizations active in the big data market that provide these services.
Business Function Outlook
Based on Business Function, the market is segmented into Marketing & Sales, Finance, Human Resources, Operations, and Others. The marketing and sales segment acquired the largest revenue share in the big data market in 2020. Big data in marketing and sales reveals which content is most efficient at each stage of the sales process, as well as how to improve investments in Customer Relationship Management (CRM) systems and strategies for increasing conversion rates, prospect interaction, conversion rates, revenue, and customer lifetime value. Big data provides insights on how to minimize Customer Acquisition Cost (CAC), Customer Lifetime Value (CLTV), and monitor many other customer-driven indicators necessary to running a cloud-based business for cloud-based enterprise software enterprises.
Deployment Type Outlook
Based on Deployment Type, the market is segmented into On-premise and Cloud (Public, Private, and Hybrid). The on-premise segment recorded the largest revenue share in the big data market in 2020. The on-premises deployment strategy of big data solutions is preferred by businesses in heavily regulated sector verticals such as BFSI, healthcare and life sciences, and manufacturing. Large businesses with adequate IT resources are also likely to choose the on-premises deployment strategy. On-premises deployment is the most dependable deployment strategy, providing a high level of control and protection to an organization. To use cloud-based products, businesses must pay for a license or a copy.
Organization Size Outlook
Based on Organization Size, the market is segmented into Large Enterprises and Small & Medium Enterprises (SMEs). The SMEs segment recorded a substantial revenue share in the big data market in 2020. SMEs tend to generate much fewer data as compared to large enterprises and multinational corporations and thus their demand for big data services and solutions is relatively low. SMEs also tend to prefer the cloud deployment of big data storage and analysis as it helps them keep their costs low.
Based on Vertical, the market is segmented into BFSI, Retail & Consumer Goods, Telecom & IT, Government & Defense, Healthcare & Life Sciences, Manufacturing, Media & Entertainment, Transportation & Logistics, and Others. The retail and consumer goods segment witnessed a substantial revenue share in the big data market in 2020. In the retail sector, big data analytics allows businesses to make custom recommendations based on their purchase history, resulting in more personalized shopping experiences and better customer care. In the retail industry, big data is crucial for developing an appropriate sales strategy. To understand customer behavior, predict demand, and improve price, big data is being used at every level of the retail process.
Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. North America emerged as the leading region with the highest revenue share in the big data market in 2020. Most enterprises and verticals in North America regard data discovery and big data analytics to be quite productive. The high adoption rate of advanced solutions and services across various nations of the region is contributing to the growth of the regional market during the forecast period.
The major strategies followed by the market participants are Partnerships. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Big Data Market. Companies such as Oracle Corporation, SAP SE and IBM Corporation are some of the key innovators in the Market.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, Google LLC, Oracle Corporation, Microsoft Corporation, SAS Institute, Inc., SAP SE, Alteryx, Inc., Teradata Corporation, Salesforce.com, Inc., and TIBCO Software, Inc.
Recent Strategies Deployed in Big Data Market
Partnerships, Collaborations and Agreements:
Feb-2022: Teradata extended its partnership with Microsoft, an American multinational technology corporation. Through this partnership expansion, the companies aimed to incorporate Teradata's Vantage data analysis and Power BI capabilities with Microsoft Azure.
Dec-2021: Google extended its partnership with Genesys, a customer experience company. Under this partnership, the companies would integrate various Google Cloud technologies and services in order to allow enterprises to leverage real-time streaming events as well as and historical data from all over their businesses on their preferred infrastructure for controls, governance, and customization over several public and private cloud environments.
Oct-2021: SAS teamed up with Red Hat, a leading provider of enterprise open source solutions. Under this collaboration, the companies would introduce analytic capabilities to the hybrid cloud by integrating Red Hat OpenShift with SAS Viya
Aug-2021: IBM partnered with Cloudera, an American software company. Under this partnership, the companies would strengthen their joint development as well as go-to-market programs in order to bring the cutting-edge analytical potentials of IBM Cloud Pak for Data, a unified platform for AI and data, to Cloudera Data Platform.
Jun-2021: Salesforce extended its partnership with AWS, an American cloud-based software company. Following this extended partnership, the companies would introduce a range of integration capabilities in order to facilitate data sharing and application building that cross the two platforms.
May-2021: Google partnered with Vodafone, a leading telecom provider. Under this partnership, the companies would develop a big data platform in order to help Vodafone in delivering more personalized services to companies as well as consumers across the world.
Mar-2021-Mar Oracle partnered with Red Bull Racing, the four-time Formula 1 World Champion team. Following this acquisition, Oracle would offer its data analytics and machine learning expertise to Red Bull in order to optimize the utilization of data.
Jan-2021: Alteryx entered into a partnership with Snowflake, a Data Cloud company. Following this partnership, the companies would integrate analytics automation and data science expertise of Alteryx into Snowflake's platform in order to offer automated data pipelining along with faster data processing and analytics outcomes to its customers.
Oct-2020: IBM entered into a partnership with Vodafone Idea, a leading telecom operator. Through this partnership, IBM would help VIL in embracing open source at scale over the enterprise by deploying the Big Data Platform on the open-source Hadoop framework.
Oct-2020: SAS formed a partnership with TMA Solutions, a privately owned software outsourcing company. This partnership aimed to provide analytics solutions in AI and Data Analytics to companies in Vietnam via its cloud-native analytics platform, which would offer business insights for decision-making.
Jun-2020: Microsoft came into a partnership with SAS, a big data analytics software developer. This partnership would allow customers to operate their SAS workloads on the cloud to support them in expanding their business solutions.
Jun-2020: Microsoft signed a multi-year agreement with Commvault, a global enterprise software company. Under this agreement, the companies would integrate go-to-market, engineering, and sales of Commvault's Metallic Software-as-a-Service data protection offerings into Microsoft Azure in order to provide ultimate scale and trusted security to its customers.
Jun-2020: Salesforce extended its partnership with Snowflake, a cloud computing-based data warehousing company. Following this partnership, the companies would introduce a data marketplace, new data integration tools, and other capabilities in order to assist organizations in migrating Salesforce data to the Snowflake platform for collaboration and analysis.
May-2020: SAP formed a partnership with Informatica, an enterprise cloud data management provider. This partnership aimed to streamline businesses' journeys to the cloud by adopting SAP's data & analytics cloud suite and Informatica Intelligent Cloud Services.
Product Launches and Product Expansions:
Mar-2022: Alteryx introduced Alteryx Analytics Cloud, its first unified end-to-end analytics automation platform. The new product would help enterprises to make well-informed decisions with data as well as allow customers to access data with a browser only.
Sep-2021: TIBCO unveiled TIBCO Cloud Composer, TIBCO Cloud Discover, and TIBCO LABS Gallery, additions in its TIBCO Cloud portfolio. The new product would enable partners and customers to bring innovations in connecting and developing new applications or defining data, digital strategies, and data management.
Jul-2021: Oracle launched its fully managed Hadoop service. With this launch, the company aimed to facilitate the process of lift and shift for its customers along with the capability to build net-new analytics solutions on Hadoop's ecosystem of capabilities. In addition, the new version leverages Oracle's Distribution of Apache Hadoop and comprises an integrated OCI.
May-2021: Google rolled out Datastream, Dataplex, and Analytics Hub. The new services aimed to assist organizations to break free from data silos to estimate business outcomes as well as make informed decisions.
May-2021: SAS introduced enhancements in its Viya platform. With this product expansion, the new version would be available on Google Cloud, Microsoft Azure, and Amazon Web Services.
Mar-2021: Oracle introduced a set of innovative enhancements to Oracle Autonomous Data Warehouse. With this launch, the company aimed to strengthen its position across the industry by transforming cloud data warehousing from a complex ecosystem of tools, tasks, and products with the requirement of technical expertise, into an intuitive and convenient process.
Dec-2020: Microsoft rolled out Azure Purview, a new data governance solution. The new product would automate the discovery of cataloging and data along with eliminating the compliance risk. Moreover, Azure Purview would also help customers to map their entire data to offer an end-to-end view of their data estate.
Sep-2020: Teradata introduced enhancements to its Vantage platform. The new version comprises increased collaboration between business analysts, data engineers, data scientists, business leads, and others. Moreover, the new version would allow enterprises to save significant costs and time with enhanced data governance and security.
May-2020: Alteryx introduced enhancements in its Analytic Process Automation. The new version comprises several ready-to-use building blocks for automating processes along with a collection of more than 80 natively incorporated data sources.
Apr-2020: SAS unveiled a COVID-19 Data Analytics Resource Hub. This hub would help companies and individuals across various industries to tackle the challenges brought by the pandemic through a range of free resources that offer data, analytic capabilities, and learning resources aimed at three key areas: Recover, Respond and Reimagine.
Feb-2020: Oracle launched Oracle Cloud Data Science Platform. The new product would help companies in training, building, managing, and deploying machine learning models with the aim to strengthen the outcomes of data science projects.
Acquisitions and Mergers:
Jan-2022: Alteryx acquired Trifacta, a cloud-based data-wrangling technology specialist. With this acquisition, the company would enhance its cloud portfolio while accelerating its reach to more enterprise customers.
Oct-2021: Alteryx completed its acquisition of Lore IO, a no-code AI-enabled data modeling platform based in Silicon Valley. This acquisition would allow Alteryx to leverage cloud-native, elastic compute within Alteryx Designer Cloud as well as Alteryx Machine Learning, with the help of Lore IO's technical expertise. Moreover, it would allow customers to analyze significant datasets for actionable self-service insights.
Sep-2021: SAP took over SwoopTalent's intellectual property. Following this acquisition, the company would embed data and machine learning technology of SwoopTalent across SAP SuccessFactors solutions.
Jul-2021: IBM entered into an agreement to acquire Bluetab Solutions Group, an enterprise software, and technical services company. Through this acquisition, Bluetab would become a strategic part of IBM's data services consulting practice to improve its hybrid cloud and AI strategy.
Jan-2021: SAS took over Boemska, a provider of low-code development tools and analytics workload management software. This acquisition aimed to enhance SAS Viya, a cloud-native, advanced analytics platform with a range of capabilities that support SAS' objective of supporting the whole analytics life cycle and facilitating customer migration to the cloud.
Jan-2021: TIBCO acquired Information Builders, a privately held software company. This acquisition aimed to integrate Information Builders' analytics and data management capabilities with the cutting-edge TIBCO Connected Intelligence platform in order to allow TIBCO to focus on resource mapping and business alignment.
Jun-2020: Microsoft acquired ADRM Software, a leader in providing large-scale industry data models. Following this acquisition, the companies would incorporate their capabilities in order to develop an intelligent data lake with the capability of rapidly harmonizing data from multiple business lines.
Feb-2020: Google took over Looker, a unified platform. Following this acquisition, the company aimed to provide a comprehensive analytics solution that visualizes and incorporates insights at each layer of their business.
Market Segments covered in the Report:
By Business Function
By Deployment Mode
By Organization Size
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