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レポート概要
| 本調査資料は、世界のデータサイエンスプラットフォーム市場について調査し、イントロダクション、調査手法、エグゼクティブサマリー、市場インサイト、サービス別（プロフェッショナル、マネージド）分析、用途別（マーケティング、販売、物流、その他）分析、展開別（オンプレミス、クラウドベース）分析、産業別（IT・通信、医療、金融、製造、小売、その他）分析、地域別分析、競争状況、定性分析、投資分析、グローバルデータの見通しなどを徹底分析したものです。 ・イントロダクション ・調査手法 ・エグゼクティブサマリー ・市場インサイト ・世界のデータサイエンスプラットフォーム市場規模：サービス別（プロフェッショナル、マネージド） ・世界のデータサイエンスプラットフォーム市場規模：用途別（マーケティング、販売、物流、その他） ・世界のデータサイエンスプラットフォーム市場規模：展開別（オンプレミス、クラウドベース） ・世界のデータサイエンスプラットフォーム市場規模：産業別（IT・通信、医療、金融、製造、小売、その他） ・世界のデータサイエンスプラットフォーム市場規模：地域別 ・競争状況 ・定性分析 ・投資分析 ・グローバルデータの見通し |
The global data science platform market (henceforth, referred to as the market studied) was valued at USD 31.05 billion in 2020, and it is expected to reach USD 230.80 billion by 2026, registering a CAGR of 39.7 % during the forecast period, 2021-2026. The data science platform comprises the software hub around which all the types of data science work takes place, including integrating and exploring data from various sources, coding, and building models. It also leverages the data, deploys models into production, and serves up results through model-powered applications or reports. It allows data scientists within a single environment to discover actionable insights from data, plan a strategy, and in communicating the collected ideas throughout an enterprise.
– IT managers who support a large team of data scientists in an enterprise setting are tasked with data governance and providing the infrastructure and tools that data scientists need. The proliferation of data science tools and applications available provides opportunities along with challenges.
– Data science encompasses many job titles across different industries and organizations, starting from analytics officer, to actuary, to research scientist. But regardless of all title posts, all the roles are united in unlocking strategic insights from data, for which business demand is stronger than ever before. IBM estimated that the need for data scientists would soar 28% by 2020. The increased usage of large amounts of structured and unstructured data in the various end-user industry is boosting the adoption of big data. For instance, according to Seagate Technology PLC, the global volume of data is expected to increase to 47 zettabytes and 163 zettabytes in 2020 and 2025, respectively, from 12 zettabytes in 2015.
– Specifically, a data science platform supports all four stages of the data science production line: data preparation, model development, DevOps, and business delivery. It relies on transparent data access, consistent metadata, strong enterprise governance, automated machine learning, and model building, operationalized model management, and tools that measure and improve its impact on business.
– Moreover, the active use of data science and machine learning is boosting the telecommunication industry. The telecom companies operate with full data flow as they majorly function with vast communication networks and infrastructures. Analyzing and processing this data with the help of data science platforms is one of the most practical solutions.
– Further, the cloud is catering to market adoption with the data science platform integration, where players are significantly developing the cloud integration platform. Cloud computing offers the ability to access limitless processing power. Cloud vendors, such as Amazon Web Services, offer their servers with up to 96 virtual CPU cores and around 768 GB of RAM. These servers can be set up in the autoscaling group, where more than hundreds of them can be launched or stopped without much delay. Beyond just compute, cloud computing companies are offering full-fledged platforms for Data Analytics. For instance, Google Cloud offers a platform called BigQuery, a serverless and scalable data warehouse giving Data Scientists the ability to store and analyze petabytes of data, all in a single platform.
– Furthermore, the COVID-19 crisis continues to rise with the coming period. During this phase, cloud computing has strongly emerged as an efficient model that help facilitate some of the most significant transformations that businesses are undergoing. Globally, as much as 50 % of organizations (both SMBs and enterprises combined) plan to increase their cloud usage in the light of the coronavirus crisis, according to a cloud survey conducted by Flexera. In India, the numbers are much more promising, with key sectors like education, healthcare, and entertainment and gaming consistently moving to the cloud to ensure business continuity and resilience. This caters to the high usage of the data science platform.
Key Market Trends
Healthcare Sector to Dominate the Market over the Forecast Period
– Health care is a segment where individual pieces of data provide life-or-death importance, and many organizations fail to aggregate data adequately to gain insights into broader care processes. Drawing conclusions and making decisions based on data and efficiently using medical knowledge to improve safety and quality is impossible without a comprehensive data science strategy.
– The data science platform provides various medical research communities that can broadly share, integrate, and analyze historical, patient-level data from academic and industry phase III clinical trials. Such a rich data set is a part of data science and undoubtedly will help the pharmaceutical research and development segment.
– Further, players are offering new platforms that are cloud-agnostic and can be deployed as a single-tenant Platform on AWS, GCP, Azure, or Private Cloud. In June 2020, Aigenpulse introduced a new data intelligence platform designed to expedite drug discovery and development. Aigenpulse platform harnesses the latest artificial intelligence (AI) and machine learning tools to deliver advanced analytics to underpin scientific decision making.
– Also, scientists can process hundreds of datasets simultaneously and at scale, making them free for higher-value tasks. The platform easily integrates with ELNs and LIMSs, in-house data lakes for sample/experiment meta-data, and public data sources, such as TRON, TCGA, and GTeX.
– Further, despite vast amounts of health data at hand, diagnostic failure rates are still relatively high. Employing this health data to the data science platform will increase the accuracy and efficiency of diagnostics. These advantages are achieved by using powerful machine learning algorithms to extract and analyze the biological samples from over 1,000 patients.
North America to Hold Maximum Market Share
– North America is expected to hold the largest share of the data science platform market, due to large enterprises, technical experts, and growing demand for data science platform in this region. With about 1.2 billion clinical documents being produced in the United States annually, healthcare practitioners and doctors have a significant amount of data to base their research upon. Moreover, vast volumes of health-related information are made accessible through the widespread adoption of wearable tech in the region, thus offering new opportunities for the region’s better, more informed healthcare system.
– Further, the presence of many capital-intensive industries across this region proves to be beneficial for the growth of the data science platform. With the growing awareness of the benefits of these platforms, enterprises continue to integrate it into their existing operating system to gain a competitive advantage in the region’s marketplace.
– With businesses turning to big data to guide their decisions, the need for a data analytics talent pool has grown along with the presence of some of the established industry players such as Google Inc., Microsoft Corporation, IBM Corporation, Cloudera Inc., leveraging powerful machine learning and data science technologies that can turn data into actionable insights.
Competitive Landscape
The Data Science Platform Market is competitive and consists of numerous players. The major players with a leading share in the market converge on expanding their customer base across foreign countries. The market players are leveraging strategic collaborative initiatives to grow their market share and improve and increase their profitability. Some of the key developments in the market are:
– In June 2020 – IBM Corporation announced the general availability of its IBM Cloud Pak for Data V3.0 platform. The platform has grown exponentially from being a collection of IBM data services to a robust end-to-end data and AI solution. It provides a cohesive ecosystem to accelerate data estate modernization and drives AI adoption.
– In February 2020 – Oracle announced the launch of the new Oracle Cloud Infrastructure Data Science Service, a native service on Oracle Cloud Infrastructure (OCI) that is designed to let teams of data scientists collaborate on the development, deployment, and maintenance of machine learning models. As Oracle grows the footprint of its “second generation” cloud, the new service aims to leapfrog the services other public cloud vendors offer for data scientists and the problems that come with typical data scientist workflows.
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1 INTRODUCTION
1.1 Study Assumptions and Market Definition
1.2 Scope of the Study
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET INSIGHTS
4.1 Market Overview
4.2 Market Drivers
4.3 Market Challenges
4.4 Industry Attractiveness – Porter’s Five Force Analysis
4.4.1 Bargaining Power of Suppliers
4.4.2 Bargaining Power of Buyers/Consumers
4.4.3 Threat of New Entrants
4.4.4 Threat of Substitute Products
4.4.5 Intensity of Competitive Rivalry
4.5 Assessment of Impact of COVID-19
5 MARKET SEGMENTATION
5.1 By Service
5.1.1 Professional
5.1.2 Managed
5.2 By Application
5.2.1 Marketing
5.2.2 Sales
5.2.3 Logistics
5.2.4 Other Application
5.3 By Deployment
5.3.1 On-premise
5.3.2 Cloud-Based
5.4 By End-user Industry
5.4.1 IT & Telecommunication
5.4.2 Healthcare
5.4.3 BFSI
5.4.4 Manufacturing
5.4.5 Retail
5.4.6 Other End-user Industries (Government and Defense, Energy and Utilities)
5.5 Geography
5.5.1 North America
5.5.2 Europe
5.5.3 Asia Pacific
5.5.4 Latin America
5.5.5 Middle East and Africa
6 DATA SCIENTIST WORKFORCE COVERAGE
6.1 Data Scientists Workforce in North America
6.1.1 Job Listing in the United States in million – 2015-2020
6.1.2 Job Listing in the Canada in million – 2015-2020
6.2 European Data Market Landscape
6.2.1 European Data Workers in million – 2016-2020
6.2.2 Data Scientist Mean Salaries for Following Countries
6.2.2.1 United Kingdom
6.2.2.2 Spain
6.2.2.3 Germany
6.2.2.4 Italy
6.2.2.5 Switzerland
6.2.2.6 Netherlands
6.3 Data Scientists Workforce in Asia-Pacific
6.3.1 Job Listing in Malaysia in million – 2016-2020
6.3.2 Job Listing in Singapore in million – 2015-2018
6.3.3 Job Listing in the Philippines in million – 2016-2022
7 COMPETITIVE LANDSCAPE
7.1 Company Profiles
7.1.1 Google, Inc.
7.1.2 Microsoft Corporation
7.1.3 IBM Corporation
7.1.4 Cloudera, Inc.
7.1.5 Dataiku SAS
7.1.6 RapidMiner, Inc
7.1.7 Wolfram Research
7.1.8 SAS Institute, Inc.
7.1.9 H2O.ai
7.1.10 TIBCO Software Inc.
7.1.11 Domino Data Lab, Inc.
7.1.12 Anaconda Inc
7.1.13 Alteryx Inc.
7.1.14 Teradata Corporation
7.1.15 WNS Global Services Pvt. Ltd.
7.1.16 KNIME.com AG
7.1.17 BRIDGEi2i Analytics Solutions Pvt Ltd
8 QUALITATIVE TRENDS (SCREENING, SORTING, AND SENSING, DATA PREPARATION AND BASIC DATA ANALYSIS)
8.1 Content Analysis
8.2 Narrative Analysis
8.3 Discourse Analysis
8.4 Grounded Theory
9 INVESTMENT ANALYSIS
10 FUTURE OF GLOBAL DATA SCIENCE PLATFORM MARKET