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Data Science Platform Market Report

RA00077

Data Science Platform Market by Type (Solutions and Services) by End Use (Telecommunication, BFSI, Healthcare, Transport and Logistics, Manufacturing, and Others), Regional Outlook (North America, Europe, Asia-Pacific, LAMEA), Global Opportunity Analysis and Industry Forecast, 2019–2026

RA00077

Pages: 140

Feb 2020

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Global Data Science Platform Market Insights 2026:

The data science platform market was $25.7 billion in 2018 and is predicted reach revenue of $224.3 billion by 2026, growing at a CAGR of 31.1% in the forecast period. North America market was $9.8 billion in 2018 and is predicted to generate a revenue of $80.3 billion by 2026. Asia-Pacific region was $5.2 billion in 2018 and is predicted to generate a revenue of $48.0 billion by 2026. 

Data science is the study of data which involves developing methods of recording, storing and analyzing data so that one can extract the necessary information, which can help the organization to take necessary decisions. Data science involves obtaining meaningful insights from raw and unstructured data which is processed through programming and analytical and business skills. The principal purpose of the data science is to find the pattern within the data, which uses various statistical techniques to analyze and draw insights from the data. The goal of data science is to derive conclusion from the data.

Market Drivers:

Increasing adoption of data analytical tools is a major driving factor for the growth of the data science platform market.

Analytical tools of data science help the organization to take predictive decision. Data science helps the user to build, assess and control data. In the era of digitalization, data science and its analytical tools play a vital role in taking the business decisions. Organization uses the structured and unstructured data to add a meaningful insight, which can further add information to take decisions. Data science platform helps to update business processes and acquire new customers. Moreover, the tools can provide vast possibilities for learning the unobserved consumer purchasing pattern. Due to these factors, it is being predicted that the analytical tools can be the major driving factor in the data science platform market. Adoption of data science platform market in emerging market is at emerging stage and is largely untapped which can create a huge opportunity in the forecast year.    

Market Restraints:

Lack of domain expertise and data breach is considered to be the major restraints for the data science platform market.

Data science platforms are used as an approach in order to find hidden information from a large set of structured and unstructured data. These data are to be handled with caution; if any sort of information is misplaced or not considered, the whole process could go wrong and the work would need to be started from scratch. As data science is a new field, many professionals are not that experienced to handle the complex issues. Due to the unavailability of domain expertise in the respective field is considered to be the major restrains of the digital science platform. Most of the data science tools are used from open sources. There are high chances of data breach of the company as most companies use open sources; this is a major concern for the market. 

Data Science Platform Market Segmentation, by Type:

Service type is considered to be the most profitable segment in the forecast period.

Data Science Platform Market Segmentation, by Type

Source: Research Dive Analysis

Service type is considered to have the highest growth in the forecast period. Service type was $8.2 billion by 2018 and is predicted to grow by generating a revenue of $76.0 billion by 2026 Service type assists the client to solve the toughest challenges by predicting the demand of the client, which helps to improve customer satisfaction and guides to build the business strategies. The growth of the service segment is mainly driven by the growing complexity in the operational field and increasing use of business Intelligence(BI) tools, and this is expected to drive the service segment in data science platform market.

Data Science Platform Market, by End use:

Data Science Platform Market, by End use

Source: Research Dive Analysis

Banking, financial services and insurance hold the largest market share of 23.7% in 2018. Banking, financial services and insurance segment was $6.09 billion in 2018 and is predicted to grow at a CAGR of 29.4% in the forecast period. Data science plays a very crucial role in Banking, financial services and insurance in analyzing the data to provide better experience to their customers. With the help of data science platform, fraud in Banking, financial services and insurance segments can be detected, which adds value to the clients, provides the valuable insights about the trend in the credit market and helps the policymakers to take decision and build strategies. 

Data Science Platform Market, by Region:

Asia-Pacific holds the highest growth rate in the forecast period.

Data Science Platform Market, by Region
 

Source: Research Dive Analysis

North America Data Science Platform Market Outlook 2026:

North America market was $9.8 billion in 2018 and is predicted to generate a revenue of $80.3 billion by 2026. The market in North America is predicted to grow due to the growing demand of internet of things (IoT) and cloud. Internet of things (IoT) and cloud increase the demand for data science tools for data fetching and handling. 

Asia-Pacific Data Science Platform Market Overview 2026:

Asia-Pacific region was $5.2 billion in 2018 and is predicted to generate a revenue of $48.0 billion by 2026. The need for data processing is predicted to boost the demand for this region. With the growing economy and massive investment by the major tech companies are expected to drive the data science platform market across the region.

Key Participants in the Data Science Platform Market 

The major key player in the data science platform market are Databricks, Alphabet Inc. (Google), Domino Data Lab, Inc., Dataiku, Civis Analytics, Cloudera, Inc., Anaconda, Inc., IBM Corporation, Altair Engineering, Inc., and Microsoft Corporation among others.

Aspect

Particulars

  Historical Market Estimations

  2018-2019

  Base Year for Market Estimation

  2018

  Forecast timeline for Market Projection

  2019-2026

  Geographical Scope

  North America, Europe, Asia-Pacific, LAMEA

  Segmentation by Type

  • Solutions

  • Services

  Segmentation by Enduse

  • Telecommunication

  • BFSI

  • Healthcare

  • Transport and Logistics

  • Manufacturing

  • Others

  Key Countries Covered

U.S., Canada, Germany, France, UK, Italy, Japan, China, India, South Korea, Australia, Middle East, Africa

  Key Companies Profiled

  • Databricks

  • Alphabet Inc. (Google)

  • Domino Data Lab, Inc.

  • Dataiku

  • Civis Analytics

  • Cloudera, Inc.

  • Anaconda, Inc.

  • IBM Corporation

  • Altair Engineering, Inc.

  • Microsoft Corporation

Source: Research Dive Analysis


FREQUENTLY ASKED QUESTIONS?
 

A. The global data science platform market size was over $25.7 billion in 2018, and is further anticipated to reach $ 224.3 billion by 2026.

A. Databricks and IBM corporation are some of the key players in the global data science platform market.

A. North America possess great investment opportunities for the investors to witness the most promising growth in the coming years.

A. North America Data science platform market is projected to grow at 30.1% CAGR during the forecast period.

A. Product development with respect to the analytical tools are the key strategies opted by the operating companies in this private nursing care services market.

A. IBM corporation and Alphabet Inc. are the companies investing more on R&D activities for developing new products and technologies.

1. Research Methodology

1.1. Desk Research
1.2. Real time insights and validation
1.3. Forecast model
1.4. Assumptions and forecast parameters

1.4.1. Assumptions
1.4.2. Forecast parameters

1.5. Data sources

1.5.1. Primary
1.5.2. Secondary

2. Executive Summary

2.1. 360° summary
2.2. type trends
2.3. End use trends

3. Market Overview

3.1. Market segmentation & definitions
3.2. Key takeaways

3.2.1. Top investment pockets
3.2.2. Top winning strategies

3.3. Porter’s five forces analysis

3.3.1. Bargaining power of consumers
3.3.2. Bargaining power of suppliers
3.3.3. Threat of new entrants
3.3.4. Threat of substitutes
3.3.5. Competitive rivalry in the market

3.4. Market dynamics

3.4.1. Drivers
3.4.2. Restraints
3.4.3. Opportunities

3.5. Technology landscape
3.6. Regulatory landscape
3.7. Patent landscape
3.8. Strategic overview

4. Data Science Platform, by type

4.1. Service

4.1.1. Market size and forecast, by region, 2017-2028
4.1.2. Comparative market share analysis, 2018 & 2028

4.2. Solution

4.2.1. Market size and forecast, by region, 2017-2028
4.2.2. Comparative market share analysis, 2018 & 2028

5. Data Science Platform, by End use

5.1. Telecommunication

5.1.1. Market size and forecast, by region, 2017-2028
5.1.2. Comparative market share analysis, 2018 & 2028

5.2. BFSI

5.2.1. Market size and forecast, by region, 2017-2028
5.2.2. Comparative market share analysis, 2018 & 2028

5.3. Healthcare

5.3.1. Market size and forecast, by region, 2017-2028
5.3.2. Comparative market share analysis, 2018 & 2028

5.4. Transport and Logistics

5.4.1. Market size and forecast, by region, 2017-2028
5.4.2. Comparative market share analysis, 2018 & 2028

5.5. Manufacturing

5.5.1. Market size and forecast, by region, 2017-2028
5.5.2. Comparative market share analysis, 2018 & 2028

5.6. Others

5.6.1. Market size and forecast, by region, 2017-2028
5.6.2. Comparative market share analysis, 2018 & 2028

6. Data Science Platform, by Region

6.1. North Region

6.1.1. Market size and forecast, by service type, 2017-2028
6.1.2. Market size and forecast, by end-use, 2017-2028
6.1.3. Market size and forecast, by country, 2017-2028
6.1.4. Comparative market share analysis, 2018 & 2028
6.1.5. U.S

6.1.5.1. Market size and forecast, by service type, 2017-2028
6.1.5.2. Market size and forecast, by end-use, 2017-2028
6.1.5.3. Comparative market share analysis, 2018 & 2028

6.1.6. Canada

6.1.6.1. Market size and forecast, by service type, 2017-2028
6.1.6.2. Market size and forecast, by end-use, 2017-2028
6.1.6.3. Comparative market share analysis, 2018 & 2028

6.2. Europe

6.2.1. Market size and forecast, by service type, 2017-2028
6.2.2. Market size and forecast, by end-use, 2017-2028
6.2.3. Market size and forecast, by country, 2017-2028
6.2.4. Comparative market share analysis, 2018 & 2028
6.2.5. UK

6.2.5.1. Market size and forecast, by service type, 2017-2028
6.2.5.2. Market size and forecast, by end-use, 2017-2028
6.2.5.3. Comparative market share analysis, 2018 & 2028

6.2.6. Germany

6.2.6.1. Market size and forecast, by service type, 2017-2028
6.2.6.2. Market size and forecast, by end-use, 2017-2028
6.2.6.3. Comparative market share analysis, 2018 & 2028

6.2.7. France

6.2.7.1. Market size and forecast, by service type, 2017-2028
6.2.7.2. Market size and forecast, by end-use, 2017-2028
6.2.7.3. Comparative market share analysis, 2018 & 2028

6.2.8. Italy

6.2.8.1. Market size and forecast, by service type, 2017-2028
6.2.8.2. Market size and forecast, by end-use, 2017-2028
6.2.8.3. Comparative market share analysis, 2018 & 2028

6.2.9. Rest of Europe

6.2.9.1. Market size and forecast, by service type, 2017-2028
6.2.9.2. Market size and forecast, by end-use, 2017-2028
6.2.9.3. Comparative market share analysis, 2018 & 2028

6.3. Asia-Pacific

6.3.1. Market size and forecast, by service type, 2017-2028
6.3.2. Market size and forecast, by end-use, 2017-2028
6.3.3. Market size and forecast, by country, 2017-2028
6.3.4. Comparative market share analysis, 2018 & 2028
6.3.5. China

6.3.5.1. Market size and forecast, by service type, 2017-2028
6.3.5.2. Market size and forecast, by end-use, 2017-2028
6.3.5.3. Comparative market share analysis, 2018 & 2028

6.3.6. India

6.3.6.1. Market size and forecast, by service type, 2017-2028
6.3.6.2. Market size and forecast, by end-use, 2017-2028
6.3.6.3. Comparative market share analysis, 2018 & 2028

6.3.7. Japan

6.3.7.1. Market size and forecast, by service type, 2017-2028
6.3.7.2. Market size and forecast, by end-use, 2017-2028
6.3.7.3. Comparative market share analysis, 2018 & 2028

6.3.8. South Korea

6.3.8.1. Market size and forecast, by service type, 2017-2028
6.3.8.2. Market size and forecast, by end-use, 2017-2028
6.3.8.3. Comparative market share analysis, 2018 & 2028

6.3.9. Australia

6.3.9.1. Market size and forecast, by service type, 2017-2028
6.3.9.2. Market size and forecast, by end-use, 2017-2028
6.3.9.3. Comparative market share analysis, 2018 & 2028

6.3.10. Rest of Asia Pacific

6.3.10.1. Market size and forecast, by service type, 2017-2028
6.3.10.2. Market size and forecast, by end-use, 2017-2028
6.3.10.3. Comparative market share analysis, 2018 & 2028

6.4. LAMEA

6.4.1. Market size and forecast, by service type, 2017-2028
6.4.2. Market size and forecast, by end-use, 2017-2028
6.4.3. Market size and forecast, by country, 2017-2028
6.4.4. Comparative market share analysis, 2018 & 2028

6.4.5. Middle East

6.4.5.1. Market size and forecast, by service type, 2017-2028
6.4.5.2. Market size and forecast, by end-use, 2017-2028
6.4.5.3. Comparative market share analysis, 2018 & 2028

6.4.6. Africa

6.4.6.1. Market size and forecast, by service type, 2017-2028
6.4.6.2. Market size and forecast, by end-use, 2017-2028
6.4.6.3. Comparative market share analysis, 2018 & 2028

6.4.7. Rest of LAMEA

6.4.7.1. Market size and forecast, by service type, 2017-2028
6.4.7.2. Market size and forecast, by end-use, 2017-2028
6.4.7.3. Comparative market share analysis, 2018 & 2028

7. Company Profiles

7.1. Databricks

7.1.1. Business overview
7.1.2. Financial performance
7.1.3. Product portfolio
7.1.4. Recent strategic moves & developments
7.1.5. SWOT analysis

7.2. Alphabet Inc. (Google)

7.2.1. Business overview
7.2.2. Financial performance
7.2.3. Product portfolio
7.2.4. Recent strategic moves & developments
7.2.5. SWOT analysis

7.3. Domino Data Lab, Inc.,

7.3.1. Business overview
7.3.2. Financial performance
7.3.3. Product portfolio
7.3.4. Recent strategic moves & developments
7.3.5. SWOT analysis

7.4. Dataiku

7.4.1. Business overview
7.4.2. Financial performance
7.4.3. Product portfolio
7.4.4. Recent strategic moves & developments
7.4.5. SWOT analysis

7.5. Civis Analytics

7.5.1. Business overview
7.5.2. Financial performance
7.5.3. Product portfolio
7.5.4. Recent strategic moves & developments
7.5.5. SWOT analysis

7.6. Cloudera, Inc.

7.6.1. Business overview
7.6.2. Financial performance
7.6.3. Product portfolio
7.6.4. Recent strategic moves & developments
7.6.5. SWOT analysis

7.7. Anaconda, Inc.

7.7.1. Business overview
7.7.2. Financial performance
7.7.3. Product portfolio
7.7.4. Recent strategic moves & developments
7.7.5. SWOT analysis

7.8. IBM Corporation

7.8.1. Business overview
7.8.2. Financial performance
7.8.3. Product portfolio
7.8.4. Recent strategic moves & developments
7.8.5. SWOT analysis

7.9. Altair Engineering, Inc.

7.9.1. Business overview
7.9.2. Financial performance
7.9.3. Product portfolio
7.9.4. Recent strategic moves & developments
7.9.5. SWOT analysis

7.10. Microsoft Corporation

7.10.1. Business overview
7.10.2. Financial performance
7.10.3. Product portfolio
7.10.4. Recent strategic moves & developments
7.10.5. SWOT analysis

Data science is study of data that involves emerging methods of recording, storing and analyzing the data, to easily extract the needed information to take the necessary decisions for business. The phrase ‘Data Science Platform’ can be described as a software including a variety of technologies for advanced analytics uses and machine learning. It allows data scientists within a single environment to discover actionable insights from data, plan a strategy, and also in communicating the collected insights throughout an enterprise. As data science projects involve a number of different tools designed for the modeling process, it is important for the data science teams to have a centralized location for collaborating easily.

Capabilities of Data Science Platforms

As per the blog published by Research Dive , the prime data science platforms provide the flexibility of open source tools and the scalability of adaptable compute resources. A quality data science platform incorporates best practices that have been refined and developed over years of software engineering. Version control is one of those best practices, which allows the data science team to collaborate on projects without losing already done work. In addition, a good data science platform will line up data architecture of any type. Also, to better facilitate the collaboration, a data science platform also helps the data scientists to unburden low-value tasks, such as running reports, reproducing past results, and scheduling jobs.

Application Areas of Data Science Platforms

Data science platforms offer collaborative and flexible environments by enabling the organizations to integrate data-driven decisions into customer facing and operational systems. This improves the customer experience and also enhances business outcomes.

The active use of data science and machine learning is boosting the telecommunication industry. The telecom companies operate with intense data flow as they majorly function with vast communication networks and infrastructures. Analyzing and processing this data with the help of data science platforms is one of the most practical solution.

Data science platforms has also significantly helped in the emergence of wearable and applications that can monitor patients on a constant basis. This helps in preventing potential health problems. When it comes to finding cure, data science platform also plays a crucial role in the progress of pharmaceutical research. Thus, to develop cure, machine learning algorithms are used to analyze and extract biological samples from patients.

The Banking, Financial Services and Insurance (BFSI) sector has a huge amount of data that is stored. Thus, to analyze this massive amount of data, it is now essential to incorporate the implementation of big data analytics. In BFSI sector, the importance of data science platform is crucial and hence it is integrated into all decision-making processes that are based on actionable insights from customer data.

Forecast Analysis of Data Science Platform Market

Global market for data science platform is projected to witness a significant growth during the forecast period from 2019 to 2026. Several factors such as adoption of artificial intelligence, increasing demand for public cloud, explosive growth of Internet of Things (IoT) applications, machine learning, rise and revolution in demand of big data are expected to drive the demand for data science platform market growth.

Lack of domain expertise and data breach are some of the major restrains for the growth of data science platform market. Data science is a novel field to many and there are a number of professionals that are not that experienced in the respective field to handle the complex issues. Hence, the availability of domain expertise in the respective field is considered to be the major restrains of the data science platform.

Increasing adoption of data analytical tools is giving significant uplift to the growth of the global market in the near future. As per the Research Dive report statistics, the global data science platform market  is anticipated to grow at 31.1% CAGR and will reach up to $224.3 billion during the period of forecast. The report highlights prominent players operating in the global market. Some of them are Alphabet Inc. (Google), Databricks, Domino Data Lab, Inc., Civis Analytics, Dataiku, Cloudera, Inc., IBM Corporation, Anaconda, Inc., Microsoft Corporation., and Altair Engineering, Inc. These players are adopting several strategies such as collaborations, product development, and are also initiating R&D activities to stand strong in the global market.

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