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.
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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 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 a 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 a data science platform will lead to a synchronized process, involving multi-criteria optimization, automatic modeling, and data management.
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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.
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.
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.
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.
|Market Size||USD in Billion By 2030|
|Forecast Units||Value (USD Million)|
|Report Coverage||Revenue Forecast, Competitive Landscape, Growth Factors, Environment & Regulatory Landscape and Trends|
|Segments Covered||by Component (Platform, Training and Consultancy, Integration and Deployment, Others), Industrial Verticals|
|Geographies Covered||North America, Europe, Asia-Pacific, LAME and Rest of the World|
|Key Companies Profiled/Vendors||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, HO.ai, Domino Data Lab, Inc., RStudio, Inc., Rapid Insight, DataRobot, Inc., Rexer Analytics, SAP, and Databricks.|
|Key Market Opportunities||Increasing Prevalence Of The Technology Industry Enhances The Expansion Of The Data Science Platform Market Share|