Data Science Platform Market: Information by Component (Platform, Training and Consultancy, Integration and Deployment, Others), Industrial Verticals, and Region — Forecast till 2027

Apr 23, 2020   What is the Data Science Platform? A data science platform is defined as a software solution that supports the execution of data science from data ingestion to business impact for multiple users ranging from data engineers to line-of-business end-users in an integrated and consistent manner. A data science production line comprises four stages, namely; data preparation, model development, DevOps, and business delivery, and the platform is studied to support all these stages. The significant co...
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What is the Data Science Platform?

A data science platform is defined as a software solution that supports the execution of data science from data ingestion to business impact for multiple users ranging from data engineers to line-of-business end-users in an integrated and consistent manner.

A data science production line comprises four stages, namely; data preparation, model development, DevOps, and business delivery, and the platform is studied to support all these stages. The significant components the platform relies on while supporting these phases are transparent data access, strong enterprise, consistent metadata, governance, model building, automated machine learning, operationalized model management, and tools that help measure and improve its impact on business.  

The below-mentioned info-graph depicts a brief outlook of the four stages of data science production.

Data-Science-Production-Line

As the operating environment of several businesses is studied to be deploying the data-driven approach, data science has grown to no longer be an optional investment for the companies undergoing digital but a necessity. For instance, over the past decade, companies were observed of deploying predictive models each quarter to deliver marketing offers for an approx. 10% of their customer base and be satisfied with the operational outcome.  However, the current market scenario witnesses the companies' demand to deliver real-time recommendations to not just a10% but 100% of the customers. Additionally, the companies are being vocal about their want to execute a regular measure of model-driven campaigns across several functional areas of the businesses, namely; sales, operations, manufacturing, and human resources.

Singapore’s Remarkable Progress in Artificial and Analytics Field

Singapore is studied to have made the most significant advances in analytics and artificial intelligence. In 2018, the national initiative AI Singapore (AISG) announced two new initiatives in partnership with the Infocomm Media Development Authority (IMDA) intending to catalyze and boost the country’s AI capabilities to power its idea of building a digital economy. In 2019, the Singaporean government announced its partnership with the World Economic Forum’s Centre for Fourth Industrial Revolution (WEF C4IR) with an aim to help drive the ethical and responsible deployment of AI technologies. The framework is recognized to be the first of its kind to exist throughout Asia. It provides guidance that is both detailed and readily implementable in nature to help organizations throughout the city-state navigate the complex ethical questions that arise in the process of deployment of AI technologies and associated solutions.

According to our analysis, Singapore is followed by Indonesia with an approx. the adoption rate of 24.6% among the businesses for AI and Thailand with 18.97%.

Such a scenario of advances and high adoption rate of AI in the region entails complex operational scenarios which, in return, demand platforms and tools to execute the tasks with ease and accuracy. Businesses are looking for a total solution that will help them develop a better understanding of the overarching business strategy and business challenges in real-world scenarios, and thus, the data science platform appears to be an ideal solution. For instance, self-driving car manufacturers require a system consisting of perception, communication, decision-making, and control. Traditionally, each module was designed separately, but this has been transitioning to more jointly develop where the deployment of data science platform will lead to a synchronized process, involving multi-criteria optimization, automatic modeling, and data management.

The below-mentioned info-graph classifies the regions studied in the market on the basis of growth (%) for the period 2020–2027.

basis-of-growth-for-the-period-2020–2027

According to our analysis in the global data science platform market, the telecom sector is studied to grow at a CAGR of 33.9% during the period 2020-2027.

How has COVID-19 impacted the Global Data Science Platform Market?

There is no doubt that the novel COVID-19 outbreak has severely impacted several industrial verticals, and the aftereffects are too early to be studied. However, unlike other markets data science platform market is analyzed to have a positive impact as its features are helping sectors regain sustainability. Below mentioned are some examples of how data science and its associated platforms & tools are helping sectors amid the outbreak.

  • Help in tracking the spread: Graph databases are considered instrumental in depicting a visual representation of how COVID-19 spreads as it studies the link between people, places, and things. Data scientists refer these entities like nodes, and the connections are termed as “Edges”.  It is studied that in the early days of the outbreak, i.e., Q1 of 2020, China being the primary source of the virus, built a graph database tool named “Epidemic Spread”. This thread enabled people to identify their travel details, namely flight numbers, and car license plates to track them. The database will further help link the fellow passengers that have taken the same trips.
  • Help to devise a speedier way to handle contact tracing: Contact tracing is one of the effective measures to slow down the spread of COVID-19, which constitutes of getting in touch with an individual’s close contacts after being tested positive to suggest self-isolation. Medical experts, along with data scientists at Oxford University, have been studied to have teamed up and designed a mobile phone-based solution with the help of a data science platform to eliminate the need for people to call the contacts manually. Instead of implementing the manual method, those parties get text messages confirming the need for self-isolation. Thus making the process of contact tracing less time-consuming and more efficient.

Such applications of data science and associated platforms in the period of COVID-19 pave the way for several untapped growth opportunities for the global data science platform market.

Key Players

Some of the primary key players are IBM Corporation, Microsoft Corporation, Alphabet Inc., Altair Engineering, Inc., Alteryx, Inc., MathWorks, SAS Institute Inc., RapidMiner, Inc., Cloudera, Inc., Anaconda, Inc., Wolfram, Dataiku, Civis Analytics, H2O.ai, Domino Data Lab, Inc., RStudio, Inc., Rapid Insight, DataRobot, Inc., Rexer Analytics, SAP, and Databricks.

Data Science Platform Market Segmentation

By Component

  • Platform
  • Training & Consultancy
  • Integration & Deployment
  • Support & Maintenance
  • Others

By Verticals

  • BFSI
  • IT & Telecom
  • Retail & E-commerce
  • Manufacturing
  • Energy & Power
  • Others

By Regions

  • North America
  • Europe
  • Asia-Pacific
  • South America
  • Middle East
  • Africa

Frequently Asked Questions (FAQs)

IT & Telecom Segment is expected to be the leading segment in Data Science Platform Market during the forecast period.
The Asia Pacific is expected to hold the highest market share in Data Science Platform Market.
IBM Corporation, Microsoft Corporation, Alphabet Inc., Altair Engineering, Inc., Alteryx, Inc., MathWorks, SAS Institute Inc., RapidMiner, Inc., Cloudera, Inc., Anaconda, Inc., Wolfram, Dataiku, Civis Analytics are the top players in Data Science Platform Market.
We will send you an email with login credentials to access the report. You will also be able to download the pdf.
Basically choose Pay by Purchase Order when you are checking out. We will connect with you via email to set up your order.
Call us, email us, or chat with us! We encourage your questions and feedback. We have a research concierge team available at all times and included in every purchase.

What is the Data Science Platform?

A data science platform is defined as a software solution that supports the execution of data science from data ingestion to business impact for multiple users ranging from data engineers to line-of-business end-users in an integrated and consistent manner.

A data science production line comprises four stages, namely; data preparation, model development, DevOps, and business delivery, and the platform is studied to support all these stages. The significant components the platform relies on while supporting these phases are transparent data access, strong enterprise, consistent metadata, governance, model building, automated machine learning, operationalized model management, and tools that help measure and improve its impact on business.  

The below-mentioned info-graph depicts a brief outlook of the four stages of data science production.

Data-Science-Production-Line

As the operating environment of several businesses is studied to be deploying the data-driven approach, data science has grown to no longer be an optional investment for the companies undergoing digital but a necessity. For instance, over the past decade, companies were observed of deploying predictive models each quarter to deliver marketing offers for an approx. 10% of their customer base and be satisfied with the operational outcome.  However, the current market scenario witnesses the companies' demand to deliver real-time recommendations to not just a10% but 100% of the customers. Additionally, the companies are being vocal about their want to execute a regular measure of model-driven campaigns across several functional areas of the businesses, namely; sales, operations, manufacturing, and human resources.

Singapore’s Remarkable Progress in Artificial and Analytics Field

Singapore is studied to have made the most significant advances in analytics and artificial intelligence. In 2018, the national initiative AI Singapore (AISG) announced two new initiatives in partnership with the Infocomm Media Development Authority (IMDA) intending to catalyze and boost the country’s AI capabilities to power its idea of building a digital economy. In 2019, the Singaporean government announced its partnership with the World Economic Forum’s Centre for Fourth Industrial Revolution (WEF C4IR) with an aim to help drive the ethical and responsible deployment of AI technologies. The framework is recognized to be the first of its kind to exist throughout Asia. It provides guidance that is both detailed and readily implementable in nature to help organizations throughout the city-state navigate the complex ethical questions that arise in the process of deployment of AI technologies and associated solutions.

According to our analysis, Singapore is followed by Indonesia with an approx. the adoption rate of 24.6% among the businesses for AI and Thailand with 18.97%.

Such a scenario of advances and high adoption rate of AI in the region entails complex operational scenarios which, in return, demand platforms and tools to execute the tasks with ease and accuracy. Businesses are looking for a total solution that will help them develop a better understanding of the overarching business strategy and business challenges in real-world scenarios, and thus, the data science platform appears to be an ideal solution. For instance, self-driving car manufacturers require a system consisting of perception, communication, decision-making, and control. Traditionally, each module was designed separately, but this has been transitioning to more jointly develop where the deployment of data science platform will lead to a synchronized process, involving multi-criteria optimization, automatic modeling, and data management.

The below-mentioned info-graph classifies the regions studied in the market on the basis of growth (%) for the period 2020–2027.

basis-of-growth-for-the-period-2020–2027

According to our analysis in the global data science platform market, the telecom sector is studied to grow at a CAGR of 33.9% during the period 2020-2027.

How has COVID-19 impacted the Global Data Science Platform Market?

There is no doubt that the novel COVID-19 outbreak has severely impacted several industrial verticals, and the aftereffects are too early to be studied. However, unlike other markets data science platform market is analyzed to have a positive impact as its features are helping sectors regain sustainability. Below mentioned are some examples of how data science and its associated platforms & tools are helping sectors amid the outbreak.

  • Help in tracking the spread: Graph databases are considered instrumental in depicting a visual representation of how COVID-19 spreads as it studies the link between people, places, and things. Data scientists refer these entities like nodes, and the connections are termed as “Edges”.  It is studied that in the early days of the outbreak, i.e., Q1 of 2020, China being the primary source of the virus, built a graph database tool named “Epidemic Spread”. This thread enabled people to identify their travel details, namely flight numbers, and car license plates to track them. The database will further help link the fellow passengers that have taken the same trips.
  • Help to devise a speedier way to handle contact tracing: Contact tracing is one of the effective measures to slow down the spread of COVID-19, which constitutes of getting in touch with an individual’s close contacts after being tested positive to suggest self-isolation. Medical experts, along with data scientists at Oxford University, have been studied to have teamed up and designed a mobile phone-based solution with the help of a data science platform to eliminate the need for people to call the contacts manually. Instead of implementing the manual method, those parties get text messages confirming the need for self-isolation. Thus making the process of contact tracing less time-consuming and more efficient.

Such applications of data science and associated platforms in the period of COVID-19 pave the way for several untapped growth opportunities for the global data science platform market.

Key Players

Some of the primary key players are IBM Corporation, Microsoft Corporation, Alphabet Inc., Altair Engineering, Inc., Alteryx, Inc., MathWorks, SAS Institute Inc., RapidMiner, Inc., Cloudera, Inc., Anaconda, Inc., Wolfram, Dataiku, Civis Analytics, H2O.ai, Domino Data Lab, Inc., RStudio, Inc., Rapid Insight, DataRobot, Inc., Rexer Analytics, SAP, and Databricks.

Data Science Platform Market Segmentation

By Component

  • Platform
  • Training & Consultancy
  • Integration & Deployment
  • Support & Maintenance
  • Others

By Verticals

  • BFSI
  • IT & Telecom
  • Retail & E-commerce
  • Manufacturing
  • Energy & Power
  • Others

By Regions

  • North America
  • Europe
  • Asia-Pacific
  • South America
  • Middle East
  • Africa

Frequently Asked Questions (FAQs)

IT & Telecom Segment is expected to be the leading segment in Data Science Platform Market during the forecast period.
The Asia Pacific is expected to hold the highest market share in Data Science Platform Market.
IBM Corporation, Microsoft Corporation, Alphabet Inc., Altair Engineering, Inc., Alteryx, Inc., MathWorks, SAS Institute Inc., RapidMiner, Inc., Cloudera, Inc., Anaconda, Inc., Wolfram, Dataiku, Civis Analytics are the top players in Data Science Platform Market.
We will send you an email with login credentials to access the report. You will also be able to download the pdf.
Basically choose Pay by Purchase Order when you are checking out. We will connect with you via email to set up your order.
Call us, email us, or chat with us! We encourage your questions and feedback. We have a research concierge team available at all times and included in every purchase.