Data Science Platform Market Definition
The data science platform is a package of different tools which takes care of the entire data modeling process. Data science platform gives power data scientists to carve out valuable insights from data collected at sources. The data science platform gives an advantage to businesses to make data-driven decisions to maximize their output and enhance customer satisfaction. Moreover, Data science tools create value by mining large amounts of structured and unstructured data to identify patterns that can help an organization to more effectively manage costs and achieve competitive advantage. For example, logistics companies analyze weather conditions, traffic patterns, and other factors to improve delivery speeds and reduce costs. As technology is developing day by day, the data science platform provides better flexibility and scalability by adding the latest data science tools to the inventory.
The market study is broken down by Type (Automation Tools, Proprietary or GUI-Driven Data Science Platforms and Code-First Data Science Platforms) and major geographies with country level splits. According to AMA, the Global Data Science Platform market is expected to see growth rate of 29.5%
Research Analyst at AMA estimates that United States Players will contribute to the maximum growth of Global Data Science Platform market throughout the predicted period.
IBM Corporation (United States), Microsoft Corporation (United States), Alphabet Inc. (Google) (United States), SAP (Germany), SAS Institute Inc. (United States), Alteryx, Inc. (United States), Altair Engineering, Inc. (United States), RapidMiner, Inc. (United States), Cloudera, Inc. (United States) and MathWorks (Australia) are some of the key players that are part of study coverage. Additionally, the Players which are also part of the research are Domino Data Lab, Inc. (United States), RStudio, Inc. (United States), Databricks (United States), Wolfram (United States) and Dataiku (United States).
AdvanceMarketAnalytics has segmented the market of Global Data Science Platform market by Type, Application and Region.
On the basis of geography, the market of Data Science Platform has been segmented into South America (Brazil, Argentina, Rest of South America), Asia Pacific (China, Japan, India, South Korea, Taiwan, Australia, Rest of Asia-Pacific), Europe (Germany, France, Italy, United Kingdom, Netherlands, Rest of Europe), MEA (Middle East, Africa), North America (United States, Canada, Mexico). If we see Market by Function, the sub-segment i.e. Marketing & Sales will boost the Data Science Platform market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth. If we see Market by Enterprise Size, the sub-segment i.e. Large Enterprises will boost the Data Science Platform market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth. If we see Market by Deployment Type, the sub-segment i.e. On-Premises will boost the Data Science Platform market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth. If we see Market by Industry Vertical, the sub-segment i.e. Information Technology and Telecom will boost the Data Science Platform market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth. If we see Market by Component, the sub-segment i.e. Software will boost the Data Science Platform market. Additionally, the rising demand from SMEs and various industry verticals gives enough cushion to market growth.
Market Leaders and their expansionary development strategies
On February 13, 2020, Alphabet-owned Google has completed its USD 2.6 billion buyouts of privately held big data analytics firm Looker Data Sciences after winning clearance from Britainís competition watchdog.
On February 20, 2020, Oracle has announced the availability of the Oracle Cloud Data Science Platform. This Oracle Cloud Infrastructure Data Science platform will help enterprises to collaboratively build, train, manage and deploy machine learning models to increase the success of data science projects.
- Integration of Data into the Core Business Processes Has Grown Significantly
- Rise of automated Data Science and Machine Learning
- Super-sized Data Science in the Cloud
- Advances in Quantum Computing
- Growing Demand from End-Use Industries owing to Flexible and Collaborative Environments
- Enable Organizations to Incorporate Data-Driven Decisions into Operational and Customer-Facing Systems
- Reduction in Redundancy and Stimulates Innovation
- Increased Efficiency, And Generation of New Revenue Streams
- Pattern Identification Helps Institutions and Companies Recognize New Market Opportunities
- High Investment Made by Enterprises owing to Changes in Technology
- Variation in Prices owing to the Deployment Type, Cloud or On-premise
- Data Privacy and Security, Hidden Security Vulnerabilities
- Shortage of Skilled Workforce Puts Pressure on Tool Developers to Reduce Complexity
- Less Return on Investment owing to Lack of Integration and Disciplined Central Management Knowledge
- Moving From On-premise to Cloud-based Model owing to Rising in Cost
Key Target AudienceIndependent Software Vendors, Business Analytics Software Providers, IT Service Providers, Cloud Service Providers, System Integrators, Application Design and Software Developers and Others
About ApproachThe research aims to propose a patent-based approach in searching for potential technology partners as a supporting tool for enabling open innovation. The study also proposes a systematic searching process of technology partners as a preliminary step to select the emerging and key players that are involved in implementing market estimations. While patent analysis is employed to overcome the aforementioned data- and process-related limitations, as expenses occurred in that technology allows us to estimate the market size by evolving segments as target market from total available market.
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