세계 데이터 사이언스 플랫폼 시장 규모는 2023년 118억 달러에 달했습니다. 향후 IMARC Group은 2024-2032년 28.5%의 연평균 성장률(CAGR)을 보이며 2032년까지 1,199억 달러에 달할 것으로 예측하고 있습니다. 의료 산업에서 데이터 사이언스 플랫폼의 활용 증가, 다양한 기업 조직의 클라우드 기반 프로그램에 대한 수요 증가, 데이터 사이언스 플랫폼의 첨단 기술 통합 증가 등이 시장을 주도하는 주요 요인으로 꼽힙니다.
데이터 과학 플랫폼은 데이터 과학 프로세스의 다양한 측면에 필요한 도구, 기술 및 리소스를 제공하는 종합적인 소프트웨어 및 하드웨어 인프라입니다. 데이터 과학은 데이터 수집, 정리, 분석 및 해석을 포함한 다학제적 부문으로 가치 있는 인사이트를 도출하고 데이터 기반 의사결정을 내립니다. 이러한 플랫폼에는 데이터 추출, 변환, 로딩(ETL)을 위한 도구와 데이터베이스, 데이터 웨어하우스, API 및 기타 데이터 소스에 대한 커넥터가 포함됩니다. 또한 예측 및 설명 모델을 구축하기 위한 다양한 기계 학습 알고리즘과 모델링 도구도 제공됩니다.
현재 의료 부문에서는 방대한 양의 정형 및 비정형 데이터를 효율적으로 분석, 감독, 통합할 수 있는 데이터 사이언스 플랫폼의 도입이 증가하고 있으며, 이는 시장 성장의 주요 원동력이 되고 있습니다. 또한, 다양한 세계 기업에서 클라우드 기반 솔루션에 대한 선호도가 높아지고 있는 것도 시장 성장에 기여하고 있습니다. 또한, 비용 효율적이고 효율적이며 향상된 의사결정 도구에 대한 수요가 전 세계적으로 증가하고 있습니다. 이러한 수요 증가는 기업의 분석 및 생산성 향상을 위한 데이터 사이언스 플랫폼의 사용 확대와 함께 시장 성장을 촉진하고 있습니다. 또한 인공지능(AI), 사물인터넷(IoT), 머신러닝(ML)을 데이터 사이언스 플랫폼에 통합하는 것은 업계 이해관계자들에게 유리한 성장 기회를 제공하고 있습니다. 또한, 비즈니스를 위한 예측 모델 구축, 관리 및 최적화를 위한 통합된 접근 방식을 제공하는 데이터 사이언스 플랫폼에 대한 수요가 증가함에 따라 시장에 긍정적인 영향을 미치고 있습니다. 또한, 빅데이터 기술의 발전에 힘입어 데이터 사이언스 플랫폼에 대한 수요가 증가하면서 시장 확대에 기여하고 있습니다. 또한, BFSI 부문의 데이터 사이언스 플랫폼에 대한 수요가 증가함에 따라 은행 서비스 이용이 확대되면서 시장 성장에 더욱 힘을 실어주고 있습니다.
의료 산업에서 데이터 사이언스 플랫폼의 활용 확대
의료 분야에서는 구조화된 데이터(환자 기록)뿐만 아니라 의료 영상, 임상 기록과 같은 비정형 데이터 등 방대한 양의 데이터가 생성됩니다. 데이터 사이언스 플랫폼은 의료 서비스 제공자가 이 풍부한 정보를 효과적으로 분석, 관리, 흡수할 수 있게 해줍니다. 예를 들어, 데이터 분석을 통해 환자 집단의 추세와 패턴, 잠재적인 건강 위험을 파악할 수 있습니다. 또한, 이러한 플랫폼은 의료 전문가에게 예측 분석을 활용할 수 있는 역량을 제공합니다. 질병 발생을 예측하고, 더 많은 주의가 필요한 고위험군 환자를 식별하고, 환자의 결과를 예측할 수 있습니다. 이러한 예측 능력은 환자 치료와 자원 배분을 향상시킬 수 있습니다. 또한, 제약 및 생명공학 분야에서는 데이터 사이언스 플랫폼이 신약 개발 및 약물 개발에 도움을 주고 있습니다. 연구자들은 유전자 데이터, 임상시험 결과, 약물 상호 작용을 분석하여 새로운 치료법을 시장에 출시하는 과정을 가속화할 수 있습니다.
다양한 기업에서 증가하는 클라우드 기반 프로그램 수요
클라우드 기반 플랫폼은 대규모 데이터 세트와 계산 수요를 처리할 수 있는 확장성을 갖추고 있습니다. 기업은 필요에 따라 리소스를 늘리거나 줄일 수 있어 데이터 과학 프로젝트를 유연하게 관리할 수 있습니다. 또한, 이러한 솔루션은 하드웨어 및 인프라에 대한 초기 투자 비용이 적게 드는 경우가 많습니다. 이러한 비용 효율성은 모든 규모의 조직, 특히 스타트업과 중소기업에게 매력적입니다. 또한, 클라우드 기반 플랫폼은 원격 액세스를 통해 지리적으로 분산된 팀 간의 협업을 촉진할 수 있습니다. 이러한 접근성은 오늘날의 세계화된 비즈니스 환경에서 매우 중요합니다. 또한, 클라우드 제공업체가 소프트웨어 업데이트와 인프라 유지보수를 수행하기 때문에 사내 IT팀의 부담을 덜어주며, 기업은 항상 최신 기능과 보안 패치를 이용할 수 있습니다.
데이터 사이언스 플랫폼의 첨단 기술 통합이 증가하고 있다
AI와 ML 알고리즘은 데이터 사이언스 플랫폼의 필수 요소로 자리 잡고 있습니다. 이들은 자동화, 예측 모델링, 자연어 처리, 이상 징후 탐지를 가능하게 합니다. 이러한 고급 기능은 복잡한 데이터 세트에서 가치 있는 인사이트를 도출하는 데 필수적입니다. 또한, 다양한 산업 분야에서 IoT 디바이스가 확산됨에 따라 데이터 사이언스 플랫폼은 이러한 디바이스에서 생성되는 대량의 데이터 유입을 처리할 수 있도록 적응하고 있습니다. 데이터 사이언스 플랫폼은 센서, 기기, 기계에서 나오는 데이터를 분석하여 실시간 인사이트를 제공하고 의사결정을 개선할 수 있습니다. 또한, 첨단 기술을 통해 데이터 사이언스 플랫폼은 더욱 정교한 데이터 시각화 기술을 제공할 수 있게 되었습니다. 이를 통해 이해관계자들에게 효과적으로 인사이트를 전달할 수 있는 능력을 강화할 수 있습니다.
The global data science platform market size reached US$ 11.8 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 119.9 Billion by 2032, exhibiting a growth rate (CAGR) of 28.5% during 2024-2032. The rising utilization of data science platforms in the healthcare industry, the growing demand for cloud-based programs in various business organizations, and the rising integration of advanced technologies in data science platforms represent some of the key factors driving the market.
A data science platform is a comprehensive software and hardware infrastructure that provides the tools, technologies, and resources necessary for various aspects of the data science process. Data science is a multidisciplinary field that involves collecting, cleaning, analyzing, and interpreting data to extract valuable insights and make data-driven decisions. These platforms include tools for data extraction, transformation, and loading (ETL), as well as connectors to databases, data warehouses, APIs, and other data sources. They also offer a wide range of machine learning algorithms and modeling tools for building predictive and descriptive models.
Currently, the increased adoption of data science platforms within the healthcare sector, owing to their ability to efficiently analyze, oversee, and integrate vast volumes of structured and unstructured data is primarily driving the market growth. Furthermore, the increasing preference for cloud-based solutions across diverse global business entities is fostering a favorable market landscape. Additionally, there is a growing demand for cost-effective, efficient, and enhanced decision-making tools on a global scale. This surge in demand, coupled with the expanding utilization of data science platforms, which enhance enterprise analysis and productivity, is propelling market growth. Moreover, the integration of artificial intelligence (AI), the internet of things (IoT), and machine learning (ML) into data science platforms is presenting lucrative growth opportunities for industry stakeholders. Furthermore, the increasing appetite for data science platforms, which offer a cohesive and integrated approach to constructing, managing, and optimizing predictive models for businesses, is exerting a positive influence on the market. Additionally, the escalating demand for data science platforms, driven by the evolution of big data technologies, is contributing to market expansion. Furthermore, the heightened need for data science platforms within the BFSI sector due to the growing utilization of banking services is further strengthening the market growth.
Rising utilization of data science platforms in the healthcare industry
Healthcare generates an enormous amount of data, both structured (patient records) and unstructured such as medical images and clinical notes. Data science platforms enable healthcare providers to effectively analyze, manage, and assimilate this wealth of information. For instance, they can use data analytics to identify trends, patterns, and potential health risks among patient populations. Besides, these platforms empower healthcare professionals to leverage predictive analytics. They can forecast disease outbreaks, identify high-risk patients who may require more attention, and even predict patient outcomes. This predictive capability enhances patient care and resource allocation. Moreover, in the pharmaceutical and biotechnology sectors, data science platforms are instrumental in drug discovery and development. Researchers can analyze genetic data, clinical trial results, and drug interactions to accelerate the process of bringing new treatments to market.
Growing demand for cloud-based programs in various business organizations
Cloud-based platforms offer scalability to handle large datasets and computational demands. Businesses can scale their resources up or down as needed, providing flexibility in managing their data science projects. Besides, these solutions often require lower upfront investment in hardware and infrastructure. This cost-effectiveness appeals to organizations of all sizes, especially startups and small businesses. Moreover, cloud-based platforms enable remote access, facilitating collaboration among geographically dispersed teams. This accessibility is crucial in today's globalized business environment. Additionally, cloud providers handle software updates and infrastructure maintenance, reducing the burden on in-house IT teams and ensuring that organizations always have access to the latest features and security patches.
Rising integration of advanced technologies in data science platforms
AI and ML algorithms are becoming integral parts of data science platforms. They enable automation, predictive modeling, natural language processing, and anomaly detection. These advanced capabilities are essential for extracting valuable insights from complex datasets. Moreover, with the proliferation of IoT devices in various industries, data science platforms are adapting to handle the massive influx of data generated by these devices. They can analyze data from sensors, devices, and machines to provide real-time insights and improve decision-making. Besides, advanced technologies enable data science platforms to offer more sophisticated data visualization techniques. This enhances the ability to convey insights to stakeholders effectively.
IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the global, regional and country levels from 2024-2032. Our report has categorized the market based on component, application and vertical.
Software
Services
Software represents the most popular component
The report has provided a detailed breakup and analysis of the market based on the component. This includes software and services. According to the report, software represented the largest segment.
Data science software offers a wide range of tools and capabilities for data collection, cleaning, analysis, modeling, and visualization. It provides data scientists with the flexibility to perform a multitude of tasks within a single platform. Moreover, it is readily available and accessible to organizations of all sizes. Many software solutions are user-friendly, making them accessible to both data science experts and those with less technical expertise. Besides, software solutions can be scaled up or down to accommodate different data volumes and complexities. This scalability is crucial in handling the ever-increasing amount of data generated by organizations.
Marketing and Sales
Logistics
Finance and Accounting
Customer Support
Others
Marketing and sales hold the largest market share
A detailed breakup and analysis of the market based on the application has also been provided in the report. This includes marketing and sales, logistics, finance and accounting, customer support, and others. According to the report, marketing and sales represented the largest segment.
Marketing and sales are inherently data-intensive fields. They heavily rely on data to make informed decisions about product development, pricing strategies, customer segmentation, and sales forecasting. Data science platforms provide the tools and capabilities to process and analyze vast datasets, enabling more accurate and data-driven decision-making. Besides, understanding customer behavior, preferences, and needs is critical for effective marketing and sales strategies. Data science platforms help organizations gather, analyze, and extract actionable insights from customer data. This allows businesses to tailor their marketing campaigns and sales efforts to target specific customer segments more effectively. Moreover, these platforms assist in optimizing marketing campaigns by analyzing campaign performance metrics and identifying which strategies are most effective. This allows marketers to allocate resources to the most successful campaigns and refine their approaches in real-time.
IT and Telecommunication
Healthcare
BFSI
Manufacturing
Retail and E-Commerce
Others
BFSI accounts for the majority of market share
A detailed breakup and analysis of the market based on the vertical has also been provided in the report. This includes IT and telecommunication, healthcare, BFSI, manufacturing, retail and e-commerce, and others. According to the report, BFSI represented the largest segment.
The BFSI industry deals with vast volumes of data, including customer transactions, financial records, market data, and risk assessments. Data science platforms are essential for processing and analyzing this extensive data to extract valuable insights, detect fraudulent activities, and make informed decisions. Besides, risk assessment is a critical aspect of the BFSI sector. Data science platforms equipped with machine learning and predictive analytics help banks and financial institutions assess and mitigate risks effectively. These platforms can identify potential credit defaults, market fluctuations, and fraudulent transactions, which is crucial for maintaining financial stability.
North America
United States
Canada
Asia-Pacific
China
Japan
India
South Korea
Australia
Indonesia
Others
Europe
Germany
France
United Kingdom
Italy
Spain
Russia
Others
Latin America
Brazil
Mexico
Others
Middle East and Africa
North America leads the market, accounting for the majority of the data science platform market share
The market research report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, North America was the largest market.
North America, particularly the United States, is home to many technology hubs such as Silicon Valley, which is known for innovation and technological advancements. This region fosters a fertile ground for the development and adoption of cutting-edge data science technologies and platforms. Moreover, the region hosts a vast number of large enterprises, including Fortune 500 companies, across various industries. These enterprises have substantial budgets and resources to invest in data science platforms to gain a competitive edge, improve operational efficiency, and drive innovation. Besides, North America leads in research and development activities related to data science and artificial intelligence (AI). Leading universities, research institutions, and tech companies in the region continually push the boundaries of data science capabilities, leading to the development of state-of-the-art platforms and tools.
The competitive landscape of the market is characterized by the presence of multiple players that include established brands, emerging startups, and specialty manufacturers. Presently, leading companies are investing in research and development to enhance their data science platforms. They are introducing new features, tools, and capabilities to stay ahead of evolving industry trends and customer demands. This includes the integration of artificial intelligence (AI), machine learning (ML), and automation to improve data analytics and predictive modeling. Besides, many key players are expanding their cloud-based data science platform offerings. Cloud platforms provide scalability, flexibility, and accessibility, which are highly valued by businesses. This expansion enables organizations to harness the power of data science without significant infrastructure investments. Moreover, they are acquiring innovative startups and smaller companies in the data science and analytics space. These acquisitions enable them to quickly gain access to cutting-edge technologies, talent, and customer bases.
Alteryx Inc.
Cloudera Inc.
Dataiku Inc.
Google LLC (Alphabet Inc.)
H2O.ai Inc.
International Business Machines Corporation
Microsoft Corporation
RapidMiner Inc.
SAP SE
SAS Institute Inc.
The MathWorks Inc.
TIBCO Software Inc.
(Please note that this is only a partial list of the key players, and the complete list is provided in the report.)
In November 2022, Alteryx Inc., launched innovations in analytics and data science automation, analytics in the cloud, machine learning (ML), and artificial intelligence (AI) during the company's Virtual Global Inspire conference. The new designer interface will be powered by the Alteryx Analytics Cloud platform, providing all cloud users access to the browser-based no-code analytics tool, with in-database pushdown processing for cloud data warehouses.
In September 2021, Microsoft updates Microsoft Machine Learning Studio which adds a new PyTorch extension library for agile deep learning experimentation.
In September 2021, MathWorks updated The MATLAB and Simulink product families. They included new and updated features and functions major improvements, code refactoring and block editing, and the ability to run Python commands and scripts from MATLAB.