Global Autonomous Data Platform Market Size By Component (Services, Platform, Integration), By Vertical (Retail, BFSI, Manufacturing), By Geographic Scope And Forecast

Autonomous Data Platform Market Size And Forecast

Autonomous Data Platform Market size was valued at USD 1.95 Billion in 2024 and is projected to reach USD 9.63 Billion by 2032, growing at a CAGR of 22.10 % from 2026 to 2032.

The increasing adoption of cloud-based solutions owing to technological advancement and the rising adoption of cognitive computing technology & advanced analytics are expected to drive the Autonomous Data Platform Market over the predicted years. The Global Autonomous Data Platform Market report provides a holistic evaluation of the market. The report offers a comprehensive analysis of key segments, trends, drivers, restraints, competitive landscape, and factors that are playing a substantial role in the market.

Global Autonomous Data Platform Market Definition

The autonomous data tool analyses a specific customer's big data infrastructure to address crucial company problems and ensure optimal database usage. It is a data and analytics platform that manages and optimizes itself by leveraging various cognitive computing platforms, such as Artificial Intelligence (AI), and Machine Learning (ML).

By making use of the combination of heuristics and machine learning, it serves insights, actionable alerts, and recommendations to the users, which results in ensuring high performance, workload continuity, and cost savings. It enhances operational efficiency and makes the process easier. Based on the component, the market is classified into Support & Maintenance, Services, Platform, Integration, and Advisory. Based on the vertical, the market is bifurcated into Telecommunication & Media, Retail, Manufacturing, Healthcare & Life Sciences, and Others.

Global Autonomous Data Platform Market Overview

The increasing adoption of cloud-based solutions owing to the advancement in technologies and the rising adoption of cognitive computing technology & advanced analytics are expected to drive the Autonomous Data Platform Market over the predicted years. Also, the growing volume of unstructured data with respect to the increasing utilization of social media & interconnected devices expects a boost to the market in the coming years.

Moreover, the introduction of expandable, unstructured, & complex data and the increasing demand for omnichannel experience from retailers are anticipated to fuel the market during the forecasted period. There are certain restraints and challenges faced which can hinder market growth. Factors such as the dearth of skilled professionals and complicated analytical processes are likely to act as market restraints.

Global Autonomous Data Platform Market: Segmentation Analysis

The Global Autonomous Data Platform Market is Segmented on the basis of Component, Vertical, And Geography.

Autonomous Data Platform Market, By Component

• Support and Maintenance
• Services
• Platform
• Integration
• Advisory

Based on Component, the market is bifurcated into Support & Maintenance, Services, Platform, Integration, and Advisory. A wide range of applications of the Autonomous Data Platform in various industry segments is expected to bolster the market demand in the coming years.

Autonomous Data Platform Market, By Vertical

• Telecommunication & Media
• Retail
• BFSI
• Manufacturing
• Healthcare and Life Sciences
• Others

Based on Vertical, the market is bifurcated into Telecommunication & Media, Retail, BFSI, Manufacturing, Healthcare & Life Sciences, and Others. BFSI segment is predicted to hold the most significant CAGR in the forecasted period due to the rapid adoption of the Autonomous Data Platform in this segment.

Autonomous Data Platform Market, By Geography

• North America
• Europe
• Asia Pacific
• Rest of the world
• On the basis of Regional Analysis, the Global Autonomous Data Platform Market is classified into North America, Europe, Asia Pacific, and the Rest of the world. The largest share in the market will be dominated by Europe owing to the rise of inconsistent data generated across organizations in different European countries.

Key Players

The "Global Autonomous Data Platform Market" study report will provide valuable insight with an emphasis on the global market including some of the major players such as Oracle Corporation, Teradata Corporation, IBM Corporation, Amazon Web Services, Inc., MapR, Cloudera, Inc., Qubole, Inc, Ataccama Corporation, Gemini Data, Inc, DvSum.

Our market analysis also entails a section solely dedicated to such major players wherein our analysts provide an insight into the financial statements of all the major players, along with its product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share, and market ranking analysis of the above-mentioned players globally.

Key Developments

• Partnerships, Collaborations, and Agreements
• Qubole, Inc. teamed up with Ascend.io, a data engineering firm, in 2020 to combine the world's most powerful data pipeline autonomous technology with the most comprehensive data lake platform. It enables data teams to construct self-service data pipelines 7x faster and with 95% less code, lowering infrastructure costs by 50% or more while improving data processing efficiency.
• June 2020 - Anaconda, Inc., the leading Python data science platform provider, and IBM Watson have announced a new partnership to enable enterprises to adopt AI open-source technology more easily. By collaborating, the two companies hope to boost innovation and overcome the AI and data science skills gap that many businesses are experiencing. The Anaconda Team Edition repository will be integrated with IBM Watson Studio on IBM Cloud Pak for Data, allowing businesses to better manage and accelerate the adoption of AI open-source technologies across any cloud.
• Product Launches and Product Expansions
• Qubole introduced a self-service platform in June 2019 for data scientists and engineers to construct AI, machine learning, and analytics processes on their preferred public cloud.
• MapR announced new MapR Data Platform innovations in April 2019, including new, deep integrations with Kubernetes key components for primary workloads on Spark and Drill. The platform was able to better manage extremely elastic workloads as a result of this innovation.
• Oracle launched a cloud-based data science platform in 2020, with Oracle Cloud Infrastructure Data Science at its heart. It allows users to train, manage, and create machine learning algorithms on the Oracle Cloud.