Overview and Scope
Big data software refers to a category of applications and tools designed to process, store, analyze, and extract insights from large and complex datasets. These software solutions are specifically designed to handle huge amounts of organized, semi-structured, and unstructured data, which are frequently created at rapid speeds and in a variety of data types.
Sizing and Forecast
The big data software market size has grown rapidly in recent years. It will grow from $211.83 billion in 2023 to $245.47 billion in 2024 at a compound annual growth rate (CAGR) of 15.9%. The growth in the historic period can be attributed to rise of social media, and multimedia, the Internet of Things (IoT), increased usage of smart apps, the need for data management and analytics software.
The big data software market size is expected to see rapid growth in the next few years. It will grow to $446.02 billion in 2028 at a compound annual growth rate (CAGR) of 16.1%. The growth in the forecast period can be attributed to growing databases across sectors, demand from big businesses for adequate IT resources, rise in the data volume generated, increasing adoption of data discovery and visualization tools. Major trends in the forecast period include strategic partnerships, product innovations, technological advancements, and integration of AI and ML.
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The big data software market covered in this report is segmented –
1) By Component: Software, Services
2) By Deployment Mode: Cloud Based, On-Premise
3) By Industry: Banking, Discrete Manufacturing, Professional Services, Process Manufacturing, Government, Other Industries
North America was the largest region in the big data software market in 2023. Asia-Pacific is expected to be the fastest-growing region in the market going forward. The regions covered in the big data software market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
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Major Driver Impacting Market Growth
The expanding use of digital solutions is expected to drive the growth of the big data software market going forward. Digital solutions refer to the integration of digital technologies into various aspects of a business, organization, or personal activity to enhance efficiency, effectiveness, and the user experience. The demand for digital solutions is on the rise due to several reasons, such as increasing internet penetration, access to smartphones, and increasing cost effectiveness. Digital solutions use big data software to analyze large volumes of data swiftly and accurately, enabling organizations to make informed decisions, optimize operations, and personalize customer experiences. For instance, in December 2022, according to data from Augusta Free Press, a US-based news service company, global digital transformation spending hit about $1.85 trillion in 2022, which is 16% more than the previous year, indicating an increase in digital solutions. Therefore, the expanding use of digital solutions is driving the growth of the big data software market.
Key Industry Players
Major companies operating in the big data software market are Alphabet Inc., Microsoft Corporation, Hitachi Vantara Corporation, Amazon Web Services Inc., Siemens AG, Hewlett-Packard Company, International Business Machines Corporation, Cisco Systems Inc., Oracle Corporation, Broadcom Inc., ServiceNow Inc., Dassault Systemes SE, OpenText Corporation, SAP SE, Micro Focus International Limited, Splunk Inc., Zoho Corporation, Snowflake Inc., Cloudera Inc., Confluent Inc., Sumo Logic, Qubole Inc., TIBCO Software Inc., SAS Institute Inc.
The big data software market report table of contents includes:
1. Executive Summary
2. Big Data Software Characteristics
3. Big Data Software Trends And Strategies
4. Big Data Software – Macro Economic Scenario
5. Global Big Data Software Size and Growth
…..
32. Global Big Data Software Competitive Benchmarking
33. Global Big Data Software Competitive Dashboard
34. Key Mergers And Acquisitions In The Big Data Software
35. Big Data Software Future Outlook and Potential Analysis
36. Appendix
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