Automated Data Platform Market By Component (Platform, Services) By Services (Advisory, Integration, Support & Maintenance) By Deployment (On-premises, Cloud; By Enterprise: Large Enterprise, Small and Medium Enterprise Size) By End-Use (BFSI, Healthcare, Retail, Manufacturing, IT and Telecom, Government, Others) - By Region, And Segment Forecasts, 2023 2031
The global automated data platform market size was estimated at USD 1.3 billion in 2022 and it is expected to hit around USD 7.2 billion by 2031, poised to grow at a CAGR of 19 % from 2023 to 2031. The application of cutting-edge technologies such as Machine Learning (ML) and Artificial Intelligence (AI), as well as increased demand for real-time information and increased digitization and automation across industries, are expected to contribute to the growth of the automated data platforms industry. With the trend of cloud platforms in new organizations and the retention of enterprise data primarily in hybrid and public clouds, autonomous data platforms are becoming increasingly applicable in cloud-based businesses.
A self-contained data platform provides exceptional flexibility, allowing businesses to adjust capacity based on convenience and needs. With the rapid growth of social media and associated devices, a large amount of unstructured data is being generated, which is expected to increase the demand for autonomous database platforms from small and medium-sized businesses. Data is encrypted, workloads are tracked, and any entity attempting to access the data is tracked by autonomous data platforms.
Organizations generate an increasing amount and variety of data. With the help of autonomous data platforms, organizations can handle and evaluate this data more effectively and efficiently. The growing popularity of autonomous data platforms reflects the need for more effective and efficient data management and exploitation strategies, as well as the increasing importance of data management and analytics in modern organizations. The growing volume of complex and unstructured data, as well as the increased use of advanced analytics and cognitive computing technologies, all contribute to market growth.
Automated Data Platform Market REPORT SCOPE & SEGMENTATION
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Report Attribute |
Details |
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Estimated Market Value (2022) |
1.3 Bn |
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Projected Market Value (2031) |
7.2 Bn |
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Base Year |
2022 |
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Forecast Years |
2023 - 2031 |
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Scope of the Report |
Historical and Forecast Trends, Industry Drivers and Constraints, Historical and Forecast Market Analysis by Segment- By Component, By Services, By Deployment, By Enterprise Size, By End-Use & By Region |
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Segments Covered |
By Component, By Services, By Deployment, By Enterprise Size, By End-Use & By Region |
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Forecast Units |
Value (USD Billion), and Volume (Units) |
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Quantitative Units |
Revenue in USD million/billion and CAGR from 2023 to 2031 |
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Regions Covered |
North America, Europe, Asia Pacific, Latin America, and Middle East & Africa, and Rest of World |
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Countries Covered |
U.S., Canada, Mexico, U.K., Germany, France, Italy, Spain, China, India, Japan, South Korea, Brazil, Argentina, GCC Countries, and South Africa, among others |
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Report Coverage |
Market growth drivers, restraints, opportunities, Porter’s five forces analysis, PEST analysis, value chain analysis, regulatory landscape, market attractiveness analysis by segments and region, company market share analysis, and COVID-19 impact analysis. |
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Delivery Format |
Delivered as an attached PDF and Excel through email, according to the purchase option. |
Key Market Drivers
The increasing volume and complexity of data generated by organizations is driving the need for automated data platforms. Traditional data processing and analysis methods may not be able to keep up with the sheer volume of data generated by modern organizations. Automated data platforms can help organizations handle this data volume and complexity by automating data processing, reducing the risk of errors, and speeding up the analysis process.
The ongoing digital transformation across various industries is driving the need for automated data platforms that can help organizations make sense of their data and drive business growth. Organizations are leveraging digital technologies to gain a competitive advantage, and automated data platforms can help organizations leverage their data to identify new opportunities, optimize their operations, and drive business growth.
Cost savings is another key driver of the automated data platform market. By automating data processing and analysis tasks, organizations can reduce the need for manual labor, which can help them save costs. Automated data platforms can also help organizations free up valuable resources that can be directed towards other strategic initiatives.
Key Market Challenges
Data Security and Privacy:
Data security and privacy remain a top concern for many organizations, particularly in light of increasing regulations such as GDPR and CCPA. Automated data platforms may pose a risk to data security and privacy if not properly secured. Data breaches and cyber-attacks can have serious consequences, including financial losses, loss of customer trust, and legal ramifications.
Integration with Legacy Systems:
Many organizations have legacy systems that are not easily compatible with automated data platforms. Integrating these systems can be a complex and costly process, which may hinder adoption of automated data platforms. Organizations may need to invest in new infrastructure and training to integrate automated data platforms with their existing systems.
Data Quality Issues:
Automated data platforms rely on high-quality data to provide accurate insights and recommendations. However, poor data quality can lead to inaccurate insights and poor decision-making. Organizations may need to invest in data quality initiatives to ensure the data used by automated data platforms is accurate and reliable.
Resistance to Change:
Some organizations may be resistant to change and may prefer to stick with their existing manual data processing and analysis methods. Convincing these organizations to adopt automated data platforms can be a challenge, particularly if they are satisfied with their current methods.
Key Market Opportunities
Cloud-based Solutions:
Cloud-based automated data platforms offer several benefits, including scalability, flexibility, and cost-effectiveness. Cloud-based solutions can help organizations leverage the power of automated data platforms without the need for significant investment in infrastructure.
Artificial Intelligence and Machine Learning:
The integration of artificial intelligence (AI) and machine learning (ML) into automated data platforms can help organizations gain deeper insights into their data and make more informed decisions. AI and ML can help identify patterns, trends, and anomalies in data that may be difficult for humans to detect.
Real-time Analytics:
Real-time analytics is becoming increasingly important in today's fast-paced business environment. Automated data platforms can provide real-time insights into data, enabling organizations to make faster and more accurate decisions.
Integration with IoT Devices:
The Internet of Things (IoT) is generating vast amounts of data that can be leveraged by automated data platforms. Integrating automated data platforms with IoT devices can help organizations gain deeper insights into their operations and identify new opportunities for growth.
Cross-functional Insights:
Automated data platforms can help break down organizational silos by providing cross-functional insights into data. This can help organizations make better decisions by leveraging insights from different departments and functions.
Regional Insights:
North America had the highest revenue share in 2022, accounting for 37.3%. Because it is home to the most established countries, including the United States and Canada, the region is thought to be the most evolved in terms of embracing new technologies and cloud-based solutions. The widespread use of mobile phones and the internet in North America is driving significant market growth.
Another factor driving market growth in the area is the increasing use of cell phones and social networking sites to communicate with customers and business partners. The regional distribution of solutions that provide clients with versatile analytics on any cloud while maintaining ongoing control and security is creating strong growth prospects for North America's autonomous data platform business. The Asia Pacific region is expected to grow at the fastest rate, with a CAGR of 24.3%.
Because AI and machine learning are increasingly assisting decision-making, the business is expected to expand rapidly. Furthermore, the ability of businesses to combine client data from multiple sources onto a single platform, saving hours of computational work, is fueling demand for autonomous data platforms.
Key Market Players:
Key Benefits of the Report
Automated Data Platform Market Report Segmentation
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ATTRIBUTE |
DETAILS |
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By Component |
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By Services |
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By Deployment |
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By Enterprise Size |
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By End-Use |
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By Geography |
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Customization Scope |
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Pricing |
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The Report Answers Questions Such As:
Research Methodology
Our research methodology has always been the key differentiating reason which sets us apart in comparison from the competing organizations in the industry. Our organization believes in consistency along with quality and establishing a new level with every new report we generate; our methods are acclaimed and the data/information inside the report is coveted. Our research methodology involves a combination of primary and secondary research methods. Data procurement is one of the most extensive stages in our research process. Our organization helps in assisting the clients to find the opportunities by examining the market across the globe coupled with providing economic statistics for each and every region. The reports generated and published are based on primary & secondary research. In secondary research, we gather data for global Market through white papers, case studies, blogs, reference customers, news, articles, press releases, white papers, and research studies. We also have our paid data applications which includes hoovers, Bloomberg business week, Avention, and others.
Data Collection
Data collection is the process of gathering, measuring, and analyzing accurate and relevant data from a variety of sources to analyze market and forecast trends. Raw market data is obtained on a broad front. Data is continuously extracted and filtered to ensure only validated and authenticated sources are considered. Data is mined from a varied host of sources including secondary and primary sources.
Primary Research
After the secondary research process, we initiate the primary research phase in which we interact with companies operating within the market space. We interact with related industries to understand the factors that can drive or hamper a market. Exhaustive primary interviews are conducted. Various sources from both the supply and demand sides are interviewed to obtain qualitative and quantitative information for a report which includes suppliers, product providers, domain experts, CEOs, vice presidents, marketing & sales directors, Type & innovation directors, and related key executives from various key companies to ensure a holistic and unbiased picture of the market.
Secondary Research
A secondary research process is conducted to identify and collect information useful for the extensive, technical, market-oriented, and comprehensive study of the market. Secondary sources include published market studies, competitive information, white papers, analyst reports, government agencies, industry and trade associations, media sources, chambers of commerce, newsletters, trade publications, magazines, Bloomberg BusinessWeek, Factiva, D&B, annual reports, company house documents, investor presentations, articles, journals, blogs, and SEC filings of companies, newspapers, and so on. We have assigned weights to these parameters and quantified their market impacts using the weighted average analysis to derive the expected market growth rate.
Top-Down Approach & Bottom-Up Approach
In the top – down approach, the Global Batteries for Solar Energy Storage Market was further divided into various segments on the basis of the percentage share of each segment. This approach helped in arriving at the market size of each segment globally. The segments market size was further broken down in the regional market size of each segment and sub-segments. The sub-segments were further broken down to country level market. The market size arrived using this approach was then crosschecked with the market size arrived by using bottom-up approach.
In the bottom-up approach, we arrived at the country market size by identifying the revenues and market shares of the key market players. The country market sizes then were added up to arrive at regional market size of the decorated apparel, which eventually added up to arrive at global market size.
This is one of the most reliable methods as the information is directly obtained from the key players in the market and is based on the primary interviews from the key opinion leaders associated with the firms considered in the research. Furthermore, the data obtained from the company sources and the primary respondents was validated through secondary sources including government publications and Bloomberg.
Market Analysis & size Estimation
Post the data mining stage, we gather our findings and analyze them, filtering out relevant insights. These are evaluated across research teams and industry experts. All this data is collected and evaluated by our analysts. The key players in the industry or markets are identified through extensive primary and secondary research. All percentage share splits, and breakdowns have been determined using secondary sources and verified through primary sources. The market size, in terms of value and volume, is determined through primary and secondary research processes, and forecasting models including the time series model, econometric model, judgmental forecasting model, the Delphi method, among Flywheel Energy Storage. Gathered information for market analysis, competitive landscape, growth trends, product development, and pricing trends is fed into the model and analyzed simultaneously.
Quality Checking & Final Review
The analysis done by the research team is further reviewed to check for the accuracy of the data provided to ensure the clients’ requirements. This approach provides essential checks and balances which facilitate the production of quality data. This Type of revision was done in two phases for the authenticity of the data and negligible errors in the report. After quality checking, the report is reviewed to look after the presentation, Type and to recheck if all the requirements of the clients were addressed.
