Data Science Platforms Market Size is Expected to Hold USD 378.7 Bn By 2030

Published Date : 01 Feb 2023

The global data science platforms market size was evaluated at USD 112.12 billion in 2022 and it is expected to hold around USD 378.7 billion by 2030, growing at a CAGR of 16.43% during the forecast period from 2022 to 2030.

A data science platform is a pre-packaged software program that offers the capabilities necessary for a data science project's whole life cycle. Platforms for data science are essential tools for data scientists. It makes model creation, model dissemination, and data exploration possible. It offers a large-scale computer infrastructure and makes data preparation and visualization easier.

Data science platforms offer a centralized platform that encourages user collaboration. Data science platforms act as a one-stop shop for data modeling since they include the APIs necessary for model creation and testing with little help from outside engineers.

Every business has been touched by the COVID-19 (Coronavirus Disease) pandemic. The data science sector has also been impacted by the epidemic. Due to the quick changes in internet traffic or purchasing trends, the models that were previously utilised for forecasting or segmentation are no longer working. 

The supply chain has been disrupted, and the borders have been secured. As a result, businesses are now concentrating on developing short-, medium-, and long-term data-driven plans in order to make wise decisions.

Report Highlights

  • In terms of the revenue share by component, the platforms sector accounted for about 83.89 percent of total revenue in 2019.
  • In terms of end users, the BFSI sector led the market share for data science platforms in 2022, and it is anticipated that it will continue to do so throughout the forecast period. This is a result of the BFSI sector being more digitalized, the quick uptake of machine learning and artificial intelligence solutions for data management, and improvements in customer experience.
  • In terms of geography, North America was the leader in the data science platform market in 2022. A variety of causes, including the region's fast digitization, the increase in government financing for cutting-edge technologies, the rise of the Internet of Things, and the expansion of the technical base, are responsible for the market's expansion.
  • The COVID-19 had a favourable influence on market expansion and will offer plenty of prospects for expansion during the projection period.
  • Data Science Platform are becoming more popular across all industries since they increase profitability and cut expenses overall.

Data Science Platforms Market Report Scope

Report Coverage Details
Market Size in 2022 USD 112.12 Billion
Market Size by 2030 USD 378.7 Billion
Growth Rate from 2022 to 2030 CAGR of 16.43%
Largest Market North America
Second Largest Market Europe
Fastest Growing Market Asia Pacific
Base Year 2022
Forecast Period 2022 To 2030
Segments Covered By Component, By Application, By Industry Vertical, By Organization Size and BY Deployment Mode
Regions Covered  North America, Europe, Asia-Pacific, Latin America and Middle East & Africa

Regional Snapshots

North America is forecasted to have the largest market share in the data science platform market. Most businesses and industry sectors in North America rate data discovery and Data Science platforms as highly successful. However, Europe is progressively integrating these cutting-edge technology into its businesses. Due to growing digitization and the desire for centrally controlled systems, APAC is seeing a significant increase in the usage of data science platforms.

Market Dynamics


Due to the growth of social media, IoT, and multimedia, which have generated an overwhelming flow of data in both organised and unstructured forms, the amount of data that enterprises collect is constantly rising. For instance, just the previous two years have seen the creation of about 90% of the world's data.

The growth rate of machine-based and human-generated data is often 10 times greater than that of traditional corporate data. For instance, the growth rate of machine data is exponential and 50 times quicker. The majority of data is consumer driven and focused. The majority of data produced worldwide is produced by today's "always-on" customers.

Nowadays, the majority of individuals spend 4-6 hours each day creating and consuming data via a range of gadgets and (social) applications. Every time you swipe, click, or send a message, fresh information is added to a database somewhere in the globe. Since everyone now has a phone, enormous volumes of data are being created.

Massive amounts of unstructured and structured data are eventually produced as a result of the rising corporate data volume, quickening technical advancements, and falling average sales prices of smart gadgets. More than 80% of the information that businesses gather is not stored in a typical relational database. 

It is instead stifled in unstructured texts, postings on social media, machine logs, pictures, and other sources. The enormous growth in data presents chances for businesses to learn new things, which has led to a rise in the need for fresh approaches. This in turn significantly influences the market for data science platforms.


Businesses should research the issues they intend to use the data science platform for. Prior to having a good understanding of the business problem to be solved, using the mechanical technique of finding datasets and executing data analysis turns out to be less productive. When businesses use the data science platform to make wise decisions, this is extremely unhelpful. Even with a defined aim in mind, efforts are ineffective if firms' expectations from the installation of the data science platform are not in line with the objectives.


Businesses are embracing the data-intensive strategy swiftly and taking tremendous efforts to make sure they can compete in the digital age, when consumers are more aware than ever before and rivals are doing all in their power to win over them. They are using various data science tools, technologies, and industry best practises to find the best answers to their complicated business challenges, get more understanding of the behavior and needs of their consumers, and come up with innovative solutions to meet all of their various business needs. 

Organizations may use data science to properly forecast potential outcomes and make better-informed decisions based on real-world situations. Businesses may follow their consumers in real time, their behaviour patterns, their purchasing habits and preferences, and their social networks using the vast volumes of data generated by customers through mobile applications and other practical solutions. With the help of cutting-edge data science tools currently available, organisations can analyse this crucial data and modify their business strategies for success. 

A recent study found that 33 percent of the businesses that used data-driven choices outperformed their competitors in terms of profitability by 6%. Organizations are more oriented toward making important decisions based on previous and real-time data analysis rather of depending on expert views as a result of the introduction of sophisticated advanced technologies including big data, Machine Learning, the Internet of Things (IoT), and the cloud.


Nowadays, businesses use sophisticated analytics methods including streaming analytics, machine learning, and predictive analytics. These methods are difficult and need in-depth analytical expertise. Technical expertise as well as analytical and critical thinking abilities are necessary to develop an ML model. Many end users lack the personnel with the necessary abilities and information.

The majority of an organization's effort is spent collecting and correcting data that is generated from numerous sources. Not every person that handles data must be knowledgeable in data science. To create a culture of data-driven decision-making, business expertise and the accompanying training are also necessary. Therefore, one of the largest difficulties that the majority of company end users may have is a shortage of trained staff.

Recent Developments

  • IBM SPSS Modeler 18.2.2 will receive an update in November 2021 that includes a robust, adaptable data mining workbench that enables users to quickly and efficiently create precise prediction models without scripting.
  • Amazon releases a new upgrade for AWS Amazon SageMaker in December 2021. Deep learning (DL) model training may be sped up by up to 50% with the use of Amazon SageMaker technology.
  • A new PyTorch extension library for agile deep learning experimentation is added in the September 2021 release to Microsoft Machine Learning Studio.
  • In June 2020, the technology firm IBM Corporation teamed up with Anaconda Inc., a supplier of python data science platforms. The adoption of open-source, artificial intelligence-based technology is being simplified through a collaborative effort. This would assist businesses in bridging the data science and artificial intelligence talent gap.

Major Key Players

  • Databricks
  • Rexer Analytics

Market Segmentation

By Component

  • Platform
  • Services

By Application

  • Marketing & Sales
  • Logistics
  • Finance and Accounting
  • Customer Support
  • Others

By Industry Vertical

  • BFSI
  • Retail and E-Commerce
  • IT and Telecom
  • Transportation
  • Healthcare
  • Manufacturing
  • Others

By Organization Size

  • Small and Medium-Sized Enterprises
  • Large Enterprises

By Deployment Mode

  • Cloud
  • On-premises

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