Data Science Platform Market Size to Surpass USD 446.79 BN by 2032
The data science platform market size was valued at USD 97.6 billion in 2022 and it is expected to surpass around USD 446.79 billion by 2032, growing at a CAGR of 16.43% over the forecast period from 2023 to 2032.
Key Takeaways:
Data Science Platform Market Report Scope
| Report Attribute | Details |
| Market Size in 2023 | USD 113.64 Billion |
| Market Size by 2032 | USD 446.79 Billion |
| Growth Rate From 2023 to 2032 | CAGR of 16.43% |
| Base Year | 2022 |
| Forecast Period | 2023 to 2032 |
| Segments Covered | Component (Platform & Services), Business Function (Marketing, Sales, Logistics, & Customer Support), Deployment Mode, Organization Size, Industry Vertical |
| Market Analysis (Terms Used) | Value (US$ Million/Billion) or (Volume/Units) |
| Report Coverage | Revenue forecast, company ranking, competitive landscape, growth factors, and trends |
| Key Companies Profiled | 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). |
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.
Market Dynamics
Driver: Astonishing growth of Big Data
The volume of data captured by organizations is continuously increasing due to the rise of social media, IoT, and multimedia, which have produced an overwhelming flow of data in either structured or unstructured format. For instance, almost 90% of the world’s data has been created in the past two years alone. Machine-based as well as human-generated data are witnessing an overall growth rate of 10 times faster than the conventional business data. For instance, machine data is experiencing an exponential 50 times faster growth rate. Data is largely consumer driven and consumer oriented. Most of the data in the world is generated by consumers, who are nowadays ‘always-on.’ Most people now spend 4–6 hours per day consuming and generating data through a variety of devices and (social) applications. With every click, swipe, or message, new data is created in a database somewhere around the world. Because everyone now has a smartphone in their pocket, the data creation sums to incomprehensible amounts.
The increasing volume of business data, rapid technological changes, and declining average selling prices of smart devices eventually contribute to the generation of massive amount of structured and unstructured data. More than 80% of all data collected by organizations is not in a standard relational database. Instead, it is trapped in unstructured documents, social media posts, machine logs, images, and other sources. The massive increase in data creates opportunities for organizations to gain new insights, for which the demand for new techniques and methods has also increased. This, in turn, plays a crucial role to drive the data science platform market.
Restraint: Lack of clarity on business problem
Companies should study the business challenges for which they want to implement the data science platform. Opting for the mechanical approach of identifying datasets and performing data analysis before getting a clear picture of what business issue to solve, proves to be less effective. This is especially unsupportive when the companies are applying the data science platform for effective decision-making. Even with a clear purpose in mind if companies’ expectations from the data science platform implementation is not aligned with the end-goals, the efforts are futile.
Opportunity: Higher inclination of enterprises toward data-intensive business strategies
Organizations are quickly adopting the data-intensive approach and are taking great measures to ensure that they are able to compete in this digital era, where their consumers are most informed, and competitors are leaving no stone unturned to attract their clients. They are utilizing different data science tools, technologies, and best practices from the industry to determine optimal solutions for their complex business problems, to have better insights into their customers’ behavior as well as their requirements and invent creative solutions to cater to their diverse business requirements. Data science allows organizations to make better informed decisions based on real scenarios and accurately predict possible future outcomes. With massive amounts of data generated from customers through mobile applications and other handy solutions, businesses can track their customers in real time, their behavior patterns, their purchasing habits and preferences, and their social network. This crucial data can be analyzed through advanced data science tools available today, allowing organizations to bend their business strategies toward success. According to a recent study, 33% of companies that adopted data-driven decisions were six percent more profitable than their counterparts. With the advent of advanced technologies such as big data, ML, IoT, and cloud, organizations are more inclined toward making decisions based on historical and real-time data analysis instead of relying on expert opinions.
Challenge: Lack of adequately skilled workforce
Organizations are nowadays using advanced analytics techniques, such as streaming analytics, ML, and predictive analytics, which are complex in nature and require in-depth analytical knowledge. The skills required to build an ML model are technical skills, and analysis and critical thinking skills. Various end users do not have people with the required skills and knowledge.
Organizations spend most of their time in capturing and correcting data generated from various sources. It is not necessary that all employees working with data have skills in data science. Business knowledge is also required along with appropriate training to build a data-driven, decision-making culture. Thus, the lack of skilled personnel is one of the biggest challenges that could be faced by a majority of business end users.
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 (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
Cloud computing refers to the storage, management, and processing of data via networks of remote servers, which are typically accessed via the Internet. Enterprises mostly in heavily regulated industry verticals, such as BFSI, healthcare and life sciences, and manufacturing, opt for the on-premises deployment model of Data Science Platform. Furthermore, 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 a high level of control and security. Enterprises need to purchase a license or a copy to deploy cloud-based solutions.
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.
Based on vertical, the BFSI segment is expected to account for a larger market size 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 software 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). BFSI segment is expected to account for a larger market size during the forecast period
North America to hold the largest market size during the forecast period
North America is estimated to account for the largest market share during the forecast period. In North America, data discovery and Data Science Platform are considered highly effective by most organizations and verticals. On the other hand, Europe is gradually incorporating these advanced solutions within its enterprises. APAC is witnessing a substantial rise in the adoption of Data Science Platform owing to the increasing digitalization and rising demand for centrally managed systems.
Some of the prominent players in the Data Science Platform Market include:
Segments Covered in the Report
This report forecasts revenue growth at country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2018 to 2032. For this study, Nova one advisor, Inc. has segmented the Data Science Platform market.
Based on Component, the market has the following segments:
Based on Deployment Mode, the market has the following segments:
Based on Organization Size, the Data Science Platform market has the following segments:
Based on Business Function, the market has the following segments:
Based on Vertical, the Data Science Platform market has the following segment
By Region
Cross-segment Market Size and Analysis for
Mentioned Segments
Additional Company Profiles (Upto 5 With No Cost)
Additional Countries (Apart From Mentioned Countries)
Country/Region-specific Report
Go To Market Strategy
Region Specific Market DynamicsRegion Level Market Share Import Export AnalysisProduction AnalysisOthers