The Autonomous Data Platform Market is expected to register a CAGR of 21.7% over the forecast period 2020 to 2025. The growing adoption of cognitive computing technology and advanced analytics as well as the rising volume of complex and unstructured data drive market growth. Across the world, people are swapping information that is going to develop in the coming years. Domo Inc. estimated that 1.7MB of data will be created every second for every person on earth by 2020 end. Additionally, rising demand for autonomous data platforms from SMEs and accelerating adoption of cloud technology are the determinants for the growth of this market.
- Big Data has turned out to be one of the widespread technologies being utilized by companies today. An autonomous data platform controls and optimizes the big data infrastructure. According to the most contemporary Shopping Index of Salesforce, digital commerce grew at a rate of 13% year-over-year in Q4 2018, and projected retail e-commerce sales exceeding USD 4 trillion through 2020. The US Census Bureau reported that 87% of the US customers began their hunt in digital channels in 2019, up from 71% the previous year. This calls for enhanced use of big data services for the cloud.
- Owing to the advantages the technology grants, cloud computing is witnessing an accelerated development in its adoption. According to Forbes, the market for cloud computing will increase to USD 160 billion by 2020, achieving a growth rate of 19%. Cloud-based deployment anticipated to have meaningful growth during the forecast period. Enhanced collaboration, Scalability, and cost-effectiveness offered by the cloud platform are expected to encourage the demand for cloud-based autonomous data platforms.
- The propagation of the Internet will feed this increase in the number of devices and autonomous data tool. The Internet happens to the principal reason for this growth in data. According to the Cisco VNI report, there will be about 4.8 billion internet subscribers in 2022, 60% of the global population. According to Cisco VNI Global IP Traffic Forecast, the other significant factor for the increase in consumption of data will be the rise in global average Wi-Fi speeds that are exacted to more than double in Asia-Pacific in 2022 as compared to 2017.
- However, complicated analytical process, lack of skilled and trained professional, and problem associated with the maintaining sync between quality and safety acts as a restricting factor for this market growth. Moreover, growing popularity of cognitive computing technology and the increasing need for advanced analytics will provide adequate opportunities for the growth of the market.
- The principal factors driving the growth of COVID-19 impact on the Big Data Analytics industry and hence Autonomous Data Platform Market are increasing demand for digital transformation, increased investments in analytics, growing demand for remote services and location data, and increasing need for real-time information track and monitor the COVID-19 spread.
- Especially during the COVID-19 pandemic - including efforts to contain its spread and help businesses stay afloat - the need to extract, visualize, and execute this intelligence in near-real-time is increasingly becoming a mission-critical objective, thus giving a boost to the Autonomous Data Platform Market.
Key Market Trends
Retail Vertical is Expected to Register a Significant Growth
- With the growing use of the Internet, the retail vertical has become more customer-centric. Advancements in technologies have also made this vertical witness the accelerated changes in consumers’ behavior. Consequently, the autonomous data platform has become an essential part of the retail vertical, assisting retailers to attain improved customer loyalty in the highly competitive market. The platform helps retailers track customers’ shopping journeys in real-time, thus enabling retailers to understand and address their customers’ needs and requirements.
- Big data powers AI, and so it follows that AI would continue to find its way into the retail & consumer goods industry. Many big data companies globally claim to assist marketers, retailers, and eCommerce companies in managing their data so that it would allow them to personalize customer engagement, forecast inventory, and segment customers in the region.
- MapR Technologies is offering an Autonomous Data Platform, which helps retailers store, integrate, and analyze the wide variety of online and offline customer data e-commerce transactions, point of sale (POS) systems, clickstream data, email, social media, and call center records - all in one central repository. Walmart is experiencing a digital transformation. It is in the process of developing the world’s most extensive private cloud system, which is supposed to have the capacity to manage 2.5 petabytes of data every hour.
- According to IBM, 62% of retailers report that the use of Big Data is giving them a competitive advantage. It is expected that the industry will witness significant growth in the adoption of Big Data technology over the forecast period, thereby positively impacting the Autonomous Data Platform market’s growth.
- The retail sector needs a strong autonomous data platform to collect different data types, including structured and unstructured, from various sources in real-time. The significant challenges faced by this vertical include the demand for omnichannel experience and the tracking of consumers in real-time. As autonomous data platforms and services help efficiently address these challenges, their adoption by retailers is expected to increase in the coming years.
North America to Hold the Largest Market Share
- The extensive penetration of the Internet and mobile devices in North America has created possibilities for enterprises to reach out to channel partners, clients, and other stakeholders in the region. The widespread use of mobile devices and social media platforms to connect with business partners and clients for giving customized content as per the business necessities of clients has prompted businesses to embrace autonomous data platforms and services.
- American multinational corporation, Intel is finding meaningful value in big data. The firm uses big data to develop chips quicker, recognize manufacturing glitches, and inform about security threats. By adopting Big Data, the firm has been able to facilitate predictive analysis and save around USD 30 million on its Quality Assurance spend while still increasing quality. The White House has also invested around USD 200 million in big data projects. The country also has a huge number of professionals in the studied market, which offers a vast potential to grow over the forecast period.
- The US retailer’s growth is expected to foster their investment in the supply chain management and are rigorously trying to enhance the customer experience. Big data applications and Autonomous data platform can help them in achieving both. Customers spent USD 601.75 billion online with U.S. merchants in 2019, up 14.9% from USD 523.64 billion the prior year, according to the U.S. Department of Commerce quarterly ecommerce figures released, and that was a higher growth rate than 2018, when online sales reported by the Commerce Department rose 13.6% year over year. As a result, the use of big data and hence Autonomous data platform is also expected to rise significantly among the US retailers.
- Companies focus on offering the most reliable end-user experience and providing the best services by using machine-learning technology-based software and services. They leverage the autonomous data platform to analyze customer-related data and to find parameters such as customers' buying behavior, seasonal demand, and product demand. With the advent of independent data platforms, marketers can centralize customers’ data from different sources at one platform, thereby saving hours of integration work.
The Autonomous Data Platform Market is concentrated with major legacy players dominating the market like IBM, Microsoft, and Teradata Corporation. Since companies are concerned regarding the privacy and management of their employee/customer data, they trust established vendors more rather than new entrants. The proliferation of data has pushed data management platform vendors, such as Oracle, MapR, and AWS, to develop and design autonomous data platforms that help IT teams simplify and manage processes. The autonomous data platform providers are competing with each other to expand their market coverage and increase their presence in newer markets.
- June 2020 - Anaconda, Inc.? provider of the leading Python data science platform and IBM Watson announced a new collaboration to help simplify enterprise adoption of AI open-source technologies. By working together, the two companies plan to help fuel innovation and address the AI and data science skills gap that many enterprises face. Anaconda Team Edition repository will be integrated with IBM Watson Studio on IBM Cloud Pak for Data, enabling organizations to better govern and speed the deployment of AI open-source technologies across any cloud.
- Feb 2020 - Oracle announced the availability of the Oracle Cloud Data Science Platform. At the core is Oracle Cloud Infrastructure Data Science, helping enterprises to collaboratively build, train, manage and deploy machine learning models to increase the success of data science projects, helping improve the effectiveness of data science teams with capabilities like shared projects, model catalogs, team security policies, reproducibility and auditability.
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1.1 Study Assumptions and Market Definition
1.2 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET DYNAMICS
4.1 Market Overview
4.2 Market Drivers
4.2.1 Growing Adoption of Cognitive Computing Technology and Advanced Analytics
4.2.2 Expanding Volume of Unstructured Data Due to the Phenomenal Growth of Interconnected Devices and Social Media
4.3 Market Restraints
4.3.1 Complex Analytical Process Requiring Skilled Professionals Services
4.4 Industry Attractiveness - Porter's Five Force Analysis
4.4.1 Threat of New Entrants
4.4.2 Bargaining Power of Buyers/Consumers
4.4.3 Bargaining Power of Suppliers
4.4.4 Threat of Substitute Products
4.4.5 Intensity of Competitive Rivalry
4.5 Industry Value Chain Analysis
4.6 Analysis on the impact of COVID-19 on the Autonomous Data Platform Market
5 MARKET SEGMENTATION
5.1 By Organization Size
5.1.1 Large Enterprises
5.1.2 Small and Medium-Sized Enterprise
5.2 By Deployment Type
5.2.1 Public Cloud
5.2.2 Private Cloud
5.2.3 Hybrid Cloud
5.3 By End-user Vertical
5.3.2 Healthcare and Life Sciences
5.3.3 Retail and Consumer Goods
5.3.4 Media and Telecommunication
5.3.5 Other End-User Verticals (Government, Manufacturing)
5.4.1 North America
5.4.3 Asia Pacific
5.4.4 Latin America
5.4.5 Middle East & Africa
6 COMPETITIVE LANDSCAPE
6.1 Company Profiles
6.1.1 Oracle Corporation
6.1.2 International Business Machines Corporation
6.1.3 Amazon Web Services
6.1.4 Teradata Corporation
6.1.5 Qubole Inc
6.1.6 MapR Technologies, Inc.
6.1.7 Alteryx Inc.
6.1.8 Ataccama Corporation
6.1.9 Cloudera, Inc.
6.1.10 Gemini Data Inc.
6.1.11 Datrium, Inc.
6.1.12 Denodo Technologies
6.1.13 Paxata, Inc.
6.1.14 Zaloni Inc.
7 INVESTMENT ANALYSIS
8 MARKET OPPORTUNITIES AND FUTURE TRENDS
Secondary Research Information is collected from a number of publicly available as well as paid databases. Public sources involve publications by different associations and governments, annual reports and statements of companies, white papers and research publications by recognized industry experts and renowned academia etc. Paid data sources include third party authentic industry databases.
Once data collection is done through secondary research, primary interviews are conducted with different stakeholders across the value chain like manufacturers, distributors, ingredient/input suppliers, end customers and other key opinion leaders of the industry. Primary research is used both to validate the data points obtained from secondary research and to fill in the data gaps after secondary research.
The market engineering phase involves analyzing the data collected, market breakdown and forecasting. Macroeconomic indicators and bottom-up and top-down approaches are used to arrive at a complete set of data points that give way to valuable qualitative and quantitative insights. Each data point is verified by the process of data triangulation to validate the numbers and arrive at close estimates.
The market engineered data is verified and validated by a number of experts, both in-house and external.
REPORT WRITING/ PRESENTATION
After the data is curated by the mentioned highly sophisticated process, the analysts begin to write the report. Garnering insights from data and forecasts, insights are drawn to visualize the entire ecosystem in a single report.