Data Science Platform Market with COVID-19 Impact Analysis by Component (Platform & Services), Business Function (Marketing, Sales, Logistics, & Customer Support), Deployment Mode, Organization Size, Industry Vertical & Region - Global Forecast to 2026

Data Science Platform Market with COVID-19 Impact Analysis by Component (Platform & Services), Business Function (Marketing, Sales, Logistics, & Customer Support), Deployment Mode, Organization Size, Industry Vertical & Region - Global Forecast to 2026

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.

Based on Component, the service segment is expected to grow at a higher CAGR during the forecast period

The service segment of the Data Science Platform market is further segmented into professional services (consulting, support and maintenance, and deployment and integration) and managed services. This section discusses each service subsegment's market size and growth rate based on type (for selected subsegments) and region.

Based on deployment mode, on-premises segment is segmented to account for a larger market size during the forecast period Most

Enterprises mostly in heavily regulated industry verticals, such as BFSI, healthcare and life sciences, and manufacturing, opt for the on-premises deployment model of the data science platform. Large enterprises with sufficient IT resources are expected to opt for the on-premises deployment model. On-premises is the most reliable deployment mode, which an enterprise can rely on for the high level of control and security. Enterprises need to purchase the license or a copy to deploy the cloud-based platform. If an organization uses on-premises storage, they might also need to have IT staff to maintain and manage servers.

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 world's 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 organization size, large enterprise segment to account for a larger market size during the forecast period Most Large enterprises considered in the report are organizations with an employee size of more than or equal to 1,000. The adoption of the data science platform among large enterprises is high due to the ever-increasing adoption of the cloud, and the trend is expected to continue during the forecast period. Large enterprises accumulate huge chunks of data that can be attributed to the widespread client base. In large enterprises, data plays a major role in evaluating the overall performance of organizations. Large enterprises are leveraging the data science platform coming from various sources, for instance, social media feeds or sensors and cameras, each record needs to be processed in a way that preserves its relation to other data and sequence in time.

APAC is expected to grow at a higher CAGR during the forecast period

Asia Pacific (APAC) has continually presented lucrative market opportunities for Data Science Platform Solutions and service providers with a notable increase in Data Science Platform across its developed and emerging countries., Japan, China, and India have displayed ample growth opportunities in the Data Science Platform market. Owing to a rapidly proliferating technology-backed economical structure, APAC is expected to emerge as the fastest-growing region in Data Science Platform software and services demand during the forecast period.

Given below is the breakup of the primary respondents:
By Company Type: Tier 1 – 34%, Tier 2 – 43%, and Tier 3 – 23%
By Designation: C-level – 50%, Directors – 30%, and Others – 20%
By Region: North America – 30%, Europe – 30%, APAC – 25%, MEA – 10%, Latin America– 5%.

Some prominent players profiled in the study 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).

Research coverage

The market study covers Data Science Platform across different segments. It aims at estimating the market size and the growth potential of this market across different segments, such as, by Component (Platform & Services), Business Function (Marketing, Sales, Logistics, & Customer Support), Deployment Mode, Organization Size, Industry Vertical, and Region. The regional analysis of the Data Science Platform covers North America, Europe, APAC, MEA, and Latin America

The study also includes an in-depth competitive analysis of the key market players, along with their company profiles, key observations related to product and business offerings, recent developments, and key market strategies.

Key benefits of buying the report

The report is expected to help the market leaders/new entrants in this market by providing them information on the closest approximations of the revenue numbers for the overall Data Science Platform and its segments. This report is also expected to help stakeholders understand the competitive landscape and gain insights to improve the position of their businesses and plan suitable go-to-market strategies. The report also aims at helping stakeholders understand the pulse of the market and provide them with information on key market drivers, restraints, challenges, and opportunities.