Data Science Platform Market

Data Science Platform Market by Business Function (Marketing, Sales, Logistics, Risk, Customer Support, Human Resources, & Operations), Deployment Model, Vertical, and Region - Global Forecast to 2021

Report Code: TC 5003 Feb, 2017, by marketsandmarkets.com

[136 Pages Report]  The overall data science platform market is expected to grow from USD 19.58 billion in 2016 to USD 101.37 billion by 2021, at a CAGR of 38.9% from 2016 to 2021. Various enterprises are using the data science platform to improve their decision-making skills as well as gain a deeper insights into consumers buying patterns and shopping behavior. Most of the IT giants prevailing in this market are actively participating in organic and inorganic strategies. Factors such as enterprises focusing on ease of use methods to drive their businesses and advancement in big data technologies are driving the growth of data science platform market. The base year considered for the study is 2015, and the forecast has been provided for the period between 2016 and 2021.

Market Dynamics

Drivers

  • Enterprise focusing on ease of use methods to drive their business
  • Advancement in big data technologies

Restraints

  • Lack of reliability on data science among the enterprises
  • Government rules and regulations
  • Data governance

Opportunities

  • Higher inclination of enterprises towards data intensive business strategies
  • High ROI through end-to-end data science platform implementation

Challenges

  • High investment costs
  • Data privacy, security, and reliability
  • Requirement to constantly update the data science platform in order to cope with advance data sources, tools, and technologies

Enterprise focusing on ease of use methods to drive their business drives the global data science platform market

The companies are emphasizing on managing and measuring their contents in order to provide the desired outcome that the end-user seeks. Advanced analytics and deploying predictive models have now become essential part for every business that intends to improve decision making processes and deliver required results to customers. Data science platform provides the real-time data accessibility which benefits the organizations to process and study data; which is further used to produce frequent plans for business enhancements and to gain competitive advantage over competitors. Scalability is a crucial issue and thus companies are employing more data scientists in order to unlock the value of the ever-increasing data. For instance, Salesforce employs over 175 data scientists to address the challenges of rising amount of data. A data science platform brings the required data together in one place, which allows data scientists to effectively collaborate with different analytical technologies and provide actionable insights. Moreover, the companies in order to promote the data science are forming alliance; in September 2016, Continuum Analytics, who created Open Data Science platform Anaconda partnered with IBM to enhance open source analytics for the enterprises. Therefore, the ever-growing enterprise focus on ease of use methods drives the growth of data science platform market.

Data Science Platform Market

The following are the major objectives of the study.

  • To describe and forecast the global data science platform market on the basis of business functions, deployment models, verticals, and regions
  • To forecast the market size of the five main regional segments, namely, North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA), and Latin America
  • To strategically analyze subsegments with respect to individual growth trends, future prospects, and contribution to the total market
  • To provide detailed information regarding the major factors influencing the growth of the market (drivers, restraints, opportunities, and challenges)
  • To analyze the opportunities in the market for stakeholders and to provide details of a competitive landscape for major players
  • To comprehensively analyze core competencies of key players
  • To track and analyze competitive developments such as mergers & acquisitions, new product developments, and partnerships & collaborations in the market  

During this research study, major players operating in the data science platform market in various regions have been identified, and their offerings, regional presence, and distribution channels have been analyzed through in-depth discussions. Top-down and bottom-up approaches have been used to determine the overall market size. Sizes of the other individual markets have been estimated using the percentage splits obtained through secondary sources such as Hoovers, Bloomberg BusinessWeek, and Factiva, along with primary respondents. The entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews with industry experts such as CEOs, VPs, directors, and marketing executives for key insights (both qualitative and quantitative) pertaining to the market. The figure below shows the breakdown of the primaries on the basis of the company type, designation, and region considered during the research study.

Data Science Platform Market

To know about the assumptions considered for the study, download the pdf brochure

The data science platform market comprises a network of players such as Microsoft Corporation, IBM Corporation, Google Inc., Wolfram, DataRobot Inc., Sense Inc., Rapid Miner Inc., Domino Data Lab, Dataiku, Alteryx Inc., and Continuum Analytics Inc. The key innovators considered for the study include companies such as BRIDGEi2i Analytics, DataRPM Corporation, Rexer Analytics, FEATURE LABS and Civis Analytics.

Major Market Developments

  • In November 2016, Microsoft launched the next generation of SQL Server and Azure Data Lake, and several new capabilities such as DocumentDB Emulator, Kafka for HDInsight and R Server for Azure HDInsight to help developers build intelligent applications.
  • In October 216, IBM launched Watson Data Platform that delivers data ingestion engine and cognitive powered decision-making tools that allows data scientists to leverage Artificial Intelligence for business use.
  • In September 2016, RapidMiner partnered with Experfy, a consulting marketplace. The partnership focusses on amplifying RapidMiner’s data science platform in connecting small, medium, and large enterprises to experts and makes them aware of RapidMiner’s product.

Key Target Audience for Data Science Platform Market

  • Analytics service providers
  • Mobile application providers
  • Consulting service providers
  • Government organizations
  • Resellers
  • Research organizations
  • Enterprise users
  • Technology providers

Scope of the Data Science Platform Market Research Report

By Business Function:  

  • Marketing
  • Sales
  • Logistics
  • Risk
  • Customer Support
  • Human Resources
  • Operations

By Deployment Model

  • On-premises
  • On-demand  

By Vertical:

  • BFSI
  • Healthcare and Life Sciences
  • IT & Telecom
  • Retail and Consumer Goods
  • Media and Entertainment
  • Manufacturing
  • Transportation and Logistics
  • Energy and Utilities
  • Government and Defense
  • Others

By Geography:

  • North America
  • Europe
  • Asia Pacific (APAC)
  • Latin America
  • Middle East and Africa (MEA)

Critical questions which the report answers

  • What are new application areas which the data science platform providing companies are exploring?
  • Which are the key players in the market and how intense is the competition?

Available Customizations:
Based on the given market data, MarketsandMarkets offers customizations in the reports as per the client’s specific requirements. The available customization options are as follows:

Geographic Analysis

  • Further country-wise breakdown of the market in APAC based on verticals

Company Information

  • Detailed analysis and profiling of additional market players (Up to 5)

The overall data science platform market is expected to grow from USD 19.58 billion in 2016 to USD 101.37 billion by 2021 at a CAGR of 38.9%. Enterprises focusing on ease of use methods to drive their businesses and advancement in big data technologies are the key factors driving the growth of this market.

Big data analytics have shown significant growth from past few years due to the advent of technologies such as IoT and Smart Cities. These have generated massive amount of data through every device such as connectors, mobile devices, routers, switches. Organizations adopt analytics solutions to get actionable insights from structured and unstructured data. Data science appeared as a field which comprise all the steps, such as, data cleaning, preparation and analysis, and provided result, which has overcome the complexity issues associated with dealing massive amount of data among data scientists. The whole ecosystem of data science consists of database sources, data wrangling, and data analytics. Data science platform integrates big data and big data analytic capabilities. It helps enterprises to tap the ultimate advantage of data getting generated from various departments such as marketing, HR, and sales. This helps enterprises in enhancing operational efficiencies, predicting anomalies faster in a business cycle, and serving consumers intrinsic needs by comprehending consumers buying behavior, patterns, tastes, and thereby enhancing overall customer experience. Much of the data generated over the last few years can provide deep insights that act instrumental in improving sales, redesign marketing strategies, and comprehending changing taste and behavior of the target customers. Data Scientists are working hard to uncover the hidden patterns, trends, and analyze the data to gain significant insights.

Various enterprises are using the data science platform to improve their decision making skills as well as gain a deeper insights into consumers buying patterns and shopping behavior. BFSI, retail & consumer goods, IT & Telecom, and healthcare & life sciences are few of the verticals, which have shown tremendous adoption of data science platforms. This is due to the data-centric characteristics of these verticals.

The data science platform market has been segmented, on the basis of business function, into Marketing, Sales, Logistics, Risk, Customer Support, Human Resources, and Operations. The marketing application is the most revered application by the data scientist community, as its helps them to unlock consumer insights and plan strategies to roll out data-driven campaigns that truly resonate with the target customers.


The data science platform market in APAC is expected to grow at the highest CAGR during the forecast period. APAC is the fastest-growing market for data science platform. The APAC market is expected to leverage on the Big Data tools and provide enormous opportunities of growth in the burgeoning data science platform market. Vendors existing in the region are expected to witness a phenomenal adoption of data science platforms during the forecast period, as the region encompasses some of the developing economies such as India and China. Indian market is expected to show a downward spiral trend in the adoption of data science platform owing to the demonetization effects the country might exhibit in coming few years. The overall APAC market is expected to adopt data science platform significantly owing to increased FDIs, flexible government policies advocating the growth of digitalization, industrialization, and smart city initiatives by various governments which will be instrumental in adoption of the technology by leaps and bounds. As a result, APAC holds a significant share of the overall data science platform market.

Data Science Platform Market

Enterprise focusing on ease of use methods to drive their business and advancement in big data technologies drive the growth of data science platform market

BFSI
The future of banking is dependent on how efficiently an organization utilizes new technologies for exploiting its collected transactional data to draft patterns representing customer behavior; to further assist organization to make desired enhancements and to customize existing offerings. The BFSI vertical is experiencing a major shift due to modernization in big data technologies. This is further fueling the growth of advanced analytics and data mining methodologies which are expected to bring significant change in the BFSI vertical during the coming years.

Retail and Consumer Goods  
Consumers are more informed today owing to their ability of having instant access to information at a click on their mobile devices. With ever growing number of users online, retailers and consumer goods companies have significant opportunities to utilize this data about their customer to optimize their business strategies. Retailers, for example, have understood this reality and are in ever increasing number leveraging from data using several data science tools and technologies. Retailers such as Amazon, Nordstrom, Warby Parker, Rebecca Minkoff, and IKEA are constantly innovating in analyzing customer experience using data science in order to distinguish themselves from other players in the retail and consumer goods vertical. IKEA, one of the largest furniture retailers, is leveraging data science to offer a unique shopping experience to its customers. It provides a personalized digital content and product catalogs to its customers using image recognition and augmented reality and is innovating in its segment by combining in-store and online shopping.

IT and Telecom
The enormous amount of unstructured & structured data is a huge challenge for enterprises to process using traditional data processing methods such as manual and automatic techniques. Despite the existence of datasets is not new; the recent years have witnessed substantial investments in data science solutions that facilitate the computing and analysis of accumulated data. With righteous accessibility to these massive data sets, companies operating in the IT & telecom vector are emerging as major adaptors of the data science technologies. These technologies and organizations analytics capabilities provide several benefits to companies operating in this vertical including higher customer satisfaction due to enhanced customer experience, assistance in establishment of smarter networks, and also help in generating new revenue sources.

Manufacturing
The manufacturing vertical encompasses some of the most mission-critical and data-intensive production processes which deploy heavy duty data analytics and data management systems. The vertical handles data from multiple sources such as in-factory data, along with analog data, images, raw sensor data, and information churned out from applications inside the factory and other sources including enterprise resource planning (ERP) systems, manufacturing execution systems, time and attendance logs, supplier information, and various process automation and control systems. Companies such as Merck & Co., a U.S.-based global pharmaceutical company, is utilizing its own data science platform built on a Hortonworks Hadoop distribution running on Amazon Web Services for optimizing its production process.

Critical questions the report answers:

  • Where will all these developments take the industry in the mid to long term?
  • What are the upcoming industry applications for data science platform?
Lack of reliability on data science among the enterprises, government rules and regulations and data governance are some of the factors restraining the growth of the market. Various data privacy rules are obstructing the enterprises to provide flexibility for data access. For instance, the European Union (EU) Data Protection Directive is an example for mandatory data privacy legislation. The directive intends to provide a single set of pre-defined rules for data protection across EU. In addition to GDPR, government regulations namely the Data Protection Act (1998), Personal Data Protection Law Number 25,326 (the 'PDPL') October 2000 for Argentina, and others are hindering the growth in deployment of data science platform among enterprises. In order to comply with regulatory requirements, the data science platforms are required to have mechanisms in place to track the processing being done on the data itself.

Key players in the include Microsoft Corporation, IBM Corporation, Google Inc., Wolfram, DataRobot Inc., Sense Inc., Rapid Miner Inc., Domino Data Lab, Dataiku, Alteryx Inc., and Continuum Analytics Inc. The key innovators considered for the study include companies such as BRIDGEi2i Analytics, DataRPM Corporation, Rexer Analytics, FEATURE LABS and Civis Analytics.

To speak to our analyst for a discussion on the above findings, click Speak to Analyst

Table of Contents

1 Introduction (Page No. - 13)
    1.1 Objectives of the Study
    1.2 Market Definition
    1.3 Market Scope
           1.3.1 Markets Covered
           1.3.2 Years Considered in the Report
    1.4 Currency
    1.5 Limitations
    1.6 Stakeholders

2 Research Methodology (Page No. - 16)
    2.1 Research Data
           2.1.1 Secondary Data
                    2.1.1.1 Key Data From Secondary Sources
           2.1.2 Primary Data
                    2.1.2.1 Key Data From Primary Sources
                    2.1.2.2 Key Industry Insights
                    2.1.2.3 Breakdown of Primaries
    2.2 Market Size Estimation
    2.3 Market Breakdown and Data Triangulation
    2.4 Research Assumptions

3 Executive Summary (Page No. - 24)

4 Premium Insights (Page No. - 28)
    4.1 Attractive Market Opportunities in the Data Science Platform Market
    4.2 Market: By Top Three Business Functions and Regions
    4.3 Lifecycle Analysis, By Region
    4.4 Market Investment Scenario
    4.5 Market: Top Three Verticals

5 Market Overview (Page No. - 31)
    5.1 Introduction
    5.2 Market Segmentation
           5.2.1 By Business Function
           5.2.2 By Deployment Model
           5.2.3 By Vertical
           5.2.4 By Region
    5.3 Market Evolution
    5.4 Market Dynamics
           5.4.1 Drivers
                    5.4.1.1 Enterprises are Focusing on Ease of Use Methods to Drive Their Business
                    5.4.1.2 Advancement in Big Data Technologies
           5.4.2 Restraints
                    5.4.2.1 Lack of Reliability on Data Science Among the Enterprises
                    5.4.2.2 Government Rules and Regulations
                    5.4.2.3 Data Governance
           5.4.3 Opportunities
                    5.4.3.1 Higher Inclination of Enterprises Toward Data Intensive Business Strategies
                    5.4.3.2 High Roi Through End-To-End Data Science Platform Implementation
           5.4.4 Challenges
                    5.4.4.1 High Investment Cost
                    5.4.4.2 Data Privacy, Security, and Reliability
                    5.4.4.3 Requirement to Constantly Update the Data Science Platform to Cope With Advanced Data Sources, Tools, and Technologies

6 Industry Trends (Page No. - 39)
    6.1 Introduction
    6.2 Value Chain Analysis
    6.3 Data Science Platform Use Cases
           6.3.1 Introduction
                    6.3.1.1 Customer Relationship Analytics
                    6.3.1.2 Human Resource Analytics
                    6.3.1.3 Clinical Trial Medication Compliance
                    6.3.1.4 Digital Agriculture
                    6.3.1.5 Churn Analytics
    6.4 Data Science Platform Ecosystem

7 Data Science Platform Market, By Business Function (Page No. - 44)
    7.1 Introduction
    7.2 Marketing
    7.3 Sales
    7.4 Logistics
    7.5 Risk
    7.6 Customer Support
    7.7 Human Resources
    7.8 Operations

8 Data Science Platform Market Analysis, By Deployment Model (Page No. - 53)
    8.1 Introduction
    8.2 On-Premises
    8.3 On-Demand

9 Data Science Platform Market Analysis, By Vertical (Page No. - 57)
    9.1 Introduction
    9.2 Banking, Financial Services, and Insurance
    9.3 Healthcare and Life Sciences
    9.4 IT and Telecom
    9.5 Retail and Consumer Goods
    9.6 Media and Entertainment
    9.7 Manufacturing
    9.8 Transportation and Logistics
    9.9 Energy and Utilities
    9.10 Government and Defense
    9.11 Others

10 Geographic Analysis (Page No. - 70)
     10.1 Introduction
     10.2 North America
     10.3 Europe
     10.4 APAC
     10.5 Latin America
     10.6 Middle East and Africa

11 Competitive Landscape (Page No. - 85)
     11.1 Overview
     11.2 Competitive Situations and Trends
             11.2.1 Partnerships, Collaborations, and Agreements
             11.2.2 New Product Launches
             11.2.3 Expansions
             11.2.4 Acquisition
     11.3 Data Science Platform Market: Vendor Comparison
     11.4 Vendor Inclusion Criteria
     11.5 Vendors Evaluated

12 Company Profiles (Page No. - 96)
     12.1 Introduction
     12.2 Microsoft
(Business Overview, Products Offered, Recent Developments, MnM View, SWOT Analysis, and Key Strategies)
     12.3 IBM
     12.4 Google
     12.5 Wolfram
     12.6 Datarobot
     12.7 Cloudera
     12.8 Rapidminer
     12.9 Domino Data Lab
     12.10 Dataiku
     12.11 Alteryx
     12.12 Continuum Analytics
*Details on Business Overview, Products Offered, Recent Developments, MnM View, SWOT Analysis, and Key Strategies Might Not Be Captured in Case of Unlisted Companies.
     12.13 Key Innovators
             12.13.1 Bridgei2i Analytics
             12.13.2 Datarpm
             12.13.3 Rexer Analytics
             12.13.4 Feature Labs
             12.13.5 Civis Analytics

13 Appendix (Page No. - 128)
     13.1 Key Insights
     13.2 Discussion Guide
     13.3 Knowledge Store: Marketsandmarkets’ Subscription Portal
     13.4 Introducing RT: Real-Time Market Intelligence
     13.5 Available Customization
     13.6 Related Reports
     13.7 Author Details


List of Tables (44 Tables)

Table 1 Data Science Platform Market Size and Growth, 2014–2021 (USD Billion, Y-O-Y %)
Table 2 Market Size, By Business Function, 2014–2021 (USD Billion)
Table 3 Marketing: Market Size, By Region, 2014–2021 (USD Million)
Table 4 Sales: Market Size, By Region, 2014–2021 (USD Million)
Table 5 Logistics: Market Size, By Region, 2014–2021 (USD Million)
Table 6 Risk: Market Size, By Region, 2014–2021 (USD Million)
Table 7 Customer Support: Market Size, By Region, 2014–2021 (USD Million)
Table 8 Human Resources: Market Size, By Region, 2014–2021 (USD Million)
Table 9 Operations: Market Size, By Region, 2014–2021 (USD Million)
Table 10 Market Size, By Deployment Model, 2014–2021 (USD Billion)
Table 11 On-Premises: Market Size, By Region, 2014–2021 (USD Million)
Table 12 On-Demand: Market Size, By Region, 2014–2021 (USD Million)
Table 13 Data Science Platform Market Size, By Vertical, 2014–2021 (USD Billion)
Table 14 BFSI: Market Size, By Region, 2014–2021 (USD Million)
Table 15 Healthcare and Life Sciences: Market Size, By Region, 2014–2021 (USD Million)
Table 16 IT and Telecom: Market Size, By Region, 2014–2021 (USD Million)
Table 17 Retail and Consumer Goods: Market Size, By Region, 2014–2021 (USD Million)
Table 18 Media and Entertainment: Market Size, By Region, 2014–2021 (USD Million)
Table 19 Manufacturing: Market Size, By Region, 2014–2021 (USD Million)
Table 20 Transportation and Logistics: Market Size, By Region, 2014–2021 (USD Million)
Table 21 Energy and Utilities: Market Size, By Region, 2014–2021 (USD Million)
Table 22 Government and Defense: Market Size, By Region, 2014–2021 (USD Million)
Table 23 Others: Market Size, By Region, 2014–2021 (USD Million)
Table 24 Market Size, By Region, 2014–2021 (USD Billion)
Table 25 North America: Data Science Platform Market Size, By Vertical, 2014–2021 (USD Billion)
Table 26 North America: Market Size, By Business Function, 2014–2021 (USD Billion)
Table 27 North America: Market Size, By Deployment Model, 2014–2021 (USD Million)
Table 28 Europe: Market Size, By Vertical, 2014–2021 (USD Billion)
Table 29 Europe: Market Size, By Business Function, 2014–2021 (USD Million)
Table 30 Europe: Market Size, By Deployment Model, 2014–2021 (USD Million)
Table 31 APAC: Data Science Platform Market Size, By Vertical, 2014–2021 (USD Billion)
Table 32 APAC: Market Size, By Business Function, 2014–2021 (USD Billion)
Table 33 APAC: Market Size, By Deployment Model, 2014–2021 (USD Million)
Table 34 Latin America: Market Size, By Vertical, 2014–2021 (USD Million)
Table 35 Latin America: Market Size, By Business Function, 2014–2021 (USD Billion)
Table 36 Latin America: Market Size, By Deployment Model, 2014–2021 (USD Billion)
Table 37 MEA: Data Science Platform Market Size, By Vertical, 2014–2021 (USD Million)
Table 38 MEA: Market Size, By Business Function, 2014–2021 (USD Million)
Table 39 MEA: Market Size, By Deployment Model, 2014–2021 (USD Million)
Table 40 Partnerships, Agreements, and Collaborations, 2014–2017
Table 41 New Product Launches, 2014-2016
Table 42 Expansions, 2014-2016
Table 43 Acquisitions, 2015–2016
Table 44 Evaluation Criteria


List of Figures (43 Figures)

Figure 1 Data Science Platform Market: Market Segmentation
Figure 2 Market: Research Design
Figure 3 Breakdown of Primary Interviews: By Company, Designation, and Region
Figure 4 Market Size Estimation Methodology: Bottom-Up Approach
Figure 5 Market Size Estimation Methodology: Top-Down Approach
Figure 6 Data Triangulation
Figure 7 Data Science Platform Market: Assumptions
Figure 8 Top Three Largest Revenue Segments of the Market, 2016
Figure 9 North America is Expected to Hold the Largest Market Share in the Market
Figure 10 Advancement in the Big Data Technology is Expected to Drive Growth of the Market During the Forecast Period
Figure 11 Logistics Business Function is Expected to Hold the Largest Market Share in the Market in 2016
Figure 12 APAC is Expected to Have the Highest Growth Opportunity in the Market During the Forecast Period
Figure 13 Market Investment Scenario: APAC is the Best Market to Invest in During the Forecast Period
Figure 14 BFSI, is Estimated to Have the Largest Market Size During the Forecast Period
Figure 15 Data Science Platform Market Segmentation: By Business Function
Figure 16 Market Segmentation: By Deployment Model
Figure 17 Market Segmentation: By Vertical
Figure 18 Market Segmentation: By Region
Figure 19 Evolution of the Market
Figure 20 Market: Drivers, Restraints, Opportunities, and Challenges
Figure 21 Market: Value Chain Analysis
Figure 22 Logistics Business Function is Expected to Have the Largest Market Size During the Forecast Period
Figure 23 On-Premises Deployment Model is Expected to Have the Larger Market Size During Forecast Period
Figure 24 Retail and Consumer Goods Vertical is Expected to Have the Largest Market Size By 2021
Figure 25 North America is Expected to Have the Largest Market Size in the Data Science Platform Market During the Forecast Period
Figure 26 North America Market Snapshot
Figure 27 APAC Market Snapshot
Figure 28 Companies Adopted Partnerships, Collaborations, and Agreements as Their Key Growth Strategy From 2012 to 2016
Figure 29 Market Evaluation Framework
Figure 30 Battle for Market Share: Partnerships, Collaborations, and Agreements, and New Product Launches Were the Key Strategies in the Data Science Platform Market
Figure 31 Evaluation Overview Table: Product Offering
Figure 32 Evaluation Overview Table: Business Strategy
Figure 33 Geographic Revenue Mix of the Top Three Market Players
Figure 34 Microsoft: Company Snapshot
Figure 35 Microsoft: SWOT Analysis
Figure 36 IBM: Company Snapshot
Figure 37 IBM: SWOT Analysis
Figure 38 Google: Company Snapshot
Figure 39 Google: SWOT Analysis
Figure 40 Wolfram: SWOT Analysis
Figure 41 Datarobot: SWOT Analysis
Figure 42 Cloudera: SWOT Analysis
Figure 43 Rapidminer: SWOT Analysis


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