The Data Science Platform market size is projected to grow from USD 95.3 billion in 2021 to 322.9 USD billion in 2026, at a Compound Annual Growth Rate (CAGR) of 27.7% during the forecast period. The Data Science Platform industry is driven by Astonishing growth of big data, however, Rising in adoption of cloud-based solutions, Rising application of the data science platform in various industries and Growing need to extract in-depth insights from voluminous data to gain competitive advantage.
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Based on the Component, platform segment to account for a larger market size during the forecast period
The Data Science Platform market has been segmented by two components: platform and services. The deployment of Data Science Platform has witnessed an increase in adoption, as serves a variety of purposes, such as ML modeling and predictive analytics. The growing adoption of Data Science Platform across all major verticals, such as BFSI, Retail and eCommerce, Telecom and IT, Media and Entertainment, Healthcare and Life Sciences, Government and Defense, Manufacturing, Transportation and Logistics, Energy and Utilities, Other Verticals ( travel and hospitality, and education and research).
Based on deployment mode, cloud segment to grow at a higher CAGR during the forecast period
Cloud computing refers to the storage, management, and processing of data via networks of remote servers, which are typically accessed via the Internet. According to Statista, cloud computing would generate more than USD 300 billion in revenue in 2020 as a component of IT services. At the same time, PwC shows the COVID-19 crisis has accelerated the cloud transition even further as per data during the first quarter of 2020, cloud spending increased by 37% to USD 29 billion. The increasing generation of data leads to various challenges for several organizations. These challenges include storage, privacy, and affordability.
Based on services, professional services segment to hold the largest market size during the forecast period
Customized, knowledge-based services that are offered to clients fall under the professional services category. These services include network deployment and customization, maintenance and support services, consulting services, designing, installation, training, network planning, network analysis, and outsourcing services. Online support, live chat for real-time answers, other real-time support options, and community portals are established so that clients can meet and exchange ideas with people in other organizations.
Based on business function, Finance and Accounting segment to grow at a higher CAGR during the forecast period
Financial services firms and banks, for example, use financial data science: Forward-thinking banks and FinTech’s improve customer service by evaluating transactional and behavioral data using various data science methods. Data science is already being used by some of the worlds largest banks to acquire insights from previous customer purchases, engagements, and accounts that are most relevant to them. Investing items, insurance coverage, bank accounts, and mortgages are now the most common notices they receive. Data science is also providing insights into how well a product sells or to whom it sells, allowing financial services organizations and banks to build consumer products, policies, and investment instruments that will sell well in the future.
Based on vertical, the healthcare and life science segment is expected to grow at a higher CAGR during the forecast period
Data Science Platform are gaining acceptance among all verticals to improve profitability and reduce overall costs. The major verticals adopting Data Science Platform are BFSI, Retail and eCommerce, Telecom and IT, Media and Entertainment, Healthcare and Life Sciences, Government and Defense, Manufacturing, Transportation and Logistics, Energy and Utilities, Other Verticals ( travel and hospitality, and education and research). Healthcare and life science, by vertical segment, is expected to grow at a higher CAGR during the forecast period
Based on organization size, SMEs segment to grow at the highest CAGR during the forecast period
The Despite contributing a major share to the world’s economy, SMEs are more open toward the adoption of new technologies to smoothen and enhance the business processes, but SMEs are under constant pressure to adopt cost-effective and reliable strategies that effectively support their business goals and help them grow. Small businesses can use Data Science Platform to better understand their customers, including what motivates them to buy, how they prefer to shop, why they switch, what they will buy next, and what variables cause them to promote a firm to others.
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Unique Features in the Data Science Platform Market
By automating processes like feature engineering, model selection, and data preparation, this functionality streamlines the machine learning process. This democratizes access to data science for enterprises by enabling individuals with less technical expertise to design and deploy models.
Understanding AI models’ decision-making process is essential as they are used more and more. Data-driven decisions are made with greater openness and confidence thanks to XAI features, which offer insights into how models make their predictions.
The term “MLOps” describes the procedures used to connect data science and production. Platforms with strong MLOps integration offer capabilities for model deployment, monitoring, and version control, ensuring models are properly operationalized for real-world application.
Teams with a variety of skill sets frequently work on data science initiatives. Collaboration technologies like version control and project management are integrated into platforms to facilitate communication and work flow among data scientists, analysts, and other stakeholders.
Certain platforms target particular sectors, such as healthcare, banking, or retail, by providing domain-specific algorithms, pre-built templates, and interfaces to pertinent data sources. This adapts the platform to the particular requirements and difficulties faced by those sectors.
Major Highlights of the Data Science Platform Market
The industry is being dominated by cloud-based data science platforms because of their scalability, accessibility, and capacity for real-time data processing. With this change, companies can avoid the hefty upfront expenditures associated with on-premise infrastructure.
Data science is becoming more widely available with the rise of user-friendly platforms that include tools like AutoML. This enables companies with lower levels of technical proficiency to take advantage of data science’s capabilities for activities like model creation and analysis
Advanced AI and machine learning capabilities are being integrated into data science systems more and more. This enables companies to make more data-driven decisions, automate processes, and obtain deeper insights from data.
Platforms designed for particular industries are becoming more and more prevalent in the market. In order to meet the specialized requirements of many industries, these platforms provide pre-built functionality, domain-specific algorithms, and interfaces to pertinent data sources.
These examples demonstrate how the market for data science platforms has evolved into a vital force behind innovation and corporate expansion in a number of industries. In this rapidly evolving market, we can anticipate seeing even more breakthroughs and distinctive features as the need for data-driven insights grows.
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Top Companies in the Data Science Platform Market
Major Big Data vendors include IBM(US), Google(US), Microsoft(US), SAS(US), AWS(US), MathWorks (US), Cloudera (US), Teradata (US), TIBCO (US), Alteryx (US), RapidMiner (US), Databricks (US), Snowflake (US), H2O.ai (US), Altair (US), Anaconda (US), SAP (US), Domino Data Lab (US), Dataiku (US), DataRobot (US), Apheris (Germany), Comet (US), Databand (US), dotData (US), Explorium (US), Noogata (US), Tecton (US), Spell (US), Arrikto (US), and Iterative (US). These market players have adopted various growth strategies, such as partnerships, collaborations, and new product launches, to expand have been the most adopted strategies by major players from 2018 to 2022, which helped companies innovate their offerings and broaden their customer base.
Teradata is a data intelligence company catering to more than 75 countries. It also caters to several industries, including communications, media and entertainment, financial services, life sciences, healthcare, retail, energy, travel and transportation, and manufacturing. Its broad customer base comprises O2 Czech Republic, BMW, Siemens Healthineers, Vodafone, American Red Cross, and US Bank. It has partnered with various companies, including Accenture, Alation, AlphaZetta, Alteryx, General Dynamics Information technology, Erwin, HCL Technologies, and Informatica, to deliver end-to-end analytics solutions. The company focuses on delivering pervasive data intelligence to its customers. It is also a provider of database management systems, which are suitable to build large scalable data warehousing applications. It helps customers integrate and simplify the analytics ecosystem, access and manage data, use analytics to extract answers, and derive business value from data. The company’s solutions and services comprise software, hardware, and related business consulting and support services to deliver analytics across companies’ entire analytical ecosystem. The company aims at providing a differentiated set of offerings to the Megadata target market with a wide range of integrated data and analytics solutions.
In data science platform market, Teradata Vantage platform, which is highly scalable, secure, concurrent, and resilient for pervasive data intelligence. Teradata Vantage offers descriptive, predictive, and prescriptive analytics with integrated data that helps implement complex analytics and algorithms through a simple, intuitive SQL language compatible with preferred business analytics tools. It also includes a Vantage Analyst that enables business analysts to perform ML and advanced analytics.
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