Innovations will Boost Data Science Platform Market Growth

Posted by mahendra lunkad on November 27th, 2017

Data science is an outset to combine statistics, data analysis, and their related techniques to understand and evaluate real phenomena with data. It provides methods and concepts drawn from many areas within the broad areas of mathematics, databases, information science, statistics and computer science in a specific way from the subdomains of machine learning, cluster analysis, data mining,  and visualization.

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Data science is moving from the edge to the core of the business. Statistical reports do not satisfy the demands of data science clients. In addition, data science platform simplifies the use of interactive apps and application program interfaces to operationalize outcomes and authorize users.

 Data science platform provides real-time data streaming and more advanced data pipelines which will support redefine big data into different categories such as actionable and fast data. A massive increase in data volume is the driving force for the data science platform. This movement of Data volume will continue to evolve shortly.

Data science platform aid to facilitate a high level of association across data scientists, business analysts, data engineers, and developers in different fields of business. Data Science platforms help the organizations to prepare data, build models and operationalize analytics.

Data Science Platform: Market Dynamics

The increasing demand for public cloud, adoption of artificial intelligence, the explosive growth of Internet of things (IoT) applications and machine learning, revolution and rise in demand of big data, etc. are the factor that are expected to drive the demand of data science platform market. Key IT Trends in enterprises are an adoption of big data technology, security, and governance, IT centralization, open standard, and libraries, etc. are adapted to data science platform to provide efficient enterprise solutions across various sectors.

The emergence of unified, multidisciplinary cloud-based development environments assist the data scientist to enhance data science skills, and collaborate the machine learning and analytics into the core business. Data science platforms are used as an approach in order to find concealed bits of knowledge from large measures of classified and unstructured information, utilizing techniques such as insights, machine learning, predictive analytics, and data mining. This area consists of different branches which help to identify changes in the way organizations solve the issues and gain competitive advantage. The growth of analytics apps and APIs which are accessed on browser and device is the recent trend in data service platform market.

Data explosion, lack of domain expertise and lack of analytical capabilities are few challenges in the data science platform market. 

Data Science Platform: Market Segmentation

Based on the application data science platform market is segmented into healthcare and life science, automotive, manufacturing, banking, financial service and insurance (BFSI), information technology and telecommunications, media and entertainment, retail and consumer goods, energy and utilities, transportation and logistics and government. Based on business function, it is segmented into marketing, logistics, operations and Human resources. Data science platform market is segmented based on deployment type such as on premises and on-demand. 

Segmentation Overview

Based on Application

• Healthcare and life science
• Information technology and telecommunication
• Automotive
• Manufacturing
• BFSI
• Media and entertainment
• Retail and consumer goods
• Energy and utilities
• Transportation and logistics
• Government

Based on deployment type

• On-premises
• Cloud-based

Based on Business function

• Marketing
• Logistics
• Operations
• Human Resources

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Data Science Platform: Competition Landscape

Some of the prominent players offering the data science platform are Cloudera Inc., RapidMiner, Inc., Domino Data Lab, Inc., Kaggle Inc., Yhat Inc., Micropole S.A., Dataiku SAS, Continuum Analytics, Inc., C&F Insight technology solutions, Civis Analytics, Inc., and IBM Corporation.