Current trends of the Data Science Platform market and analysis of future aspects 2021-2031 – Instant Interview

Global Data Science Platform Market: Overview

Around the world, datasets of varying types and volumes are exploding in ways never seen before. There is a staggering increase in disparate data collected from millions of connected devices and machine learning systems and continues to grow unhindered. A plethora of business processes generate random data, which makes no sense and sits unused for discovery. The proliferation of big data and burgeoning IoT assets, in both developing and developed regions, is a crucial aspect supporting the evolution of data science platforms. As a result, these developments have propelled advanced analytics and predictive modeling into the realm of optimal decision-making, where these data are discovered for useful patterns and actionable insights.

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Data scientists, however, are engrossed in understanding every bit and byte of that data, they typically work in silos using stand-alone tools and methods. This is a key constraint for organizations employing their expertise, which cannot grow for long without collaboration and replicability. To make matters worse, it takes a mammoth of engineering and infrastructure resources to support data scientists. Therefore, there is a pressing need for data science platforms that can produce data models with an application interface in mind that, once the models are built, can be integrated by the design team. ‘engineering. These platforms not only facilitate the work of data scientists in gleaning vast volumes of data to discover relevant patterns, but help them add value. The key function of a data science platform is to unite people, tools, data preparation, machine learning, and to help deploy predictive models.

Global Data science platform Market: Insight

A good data science platform helps find and understand past work to make it easier for data scientists. They help explore data on large machines without the need for DevOps and infrastructure setup and facilitate the seamless use of new tools and packages. Simply put, they combine statistics, data analysis and related techniques to understand and evaluate data.

Nowadays, entities are adopting data science platforms to analyze data from various sources. They allow companies to discover appropriate answers to different business-related queries by providing them with valuable information. Additionally, they help sort data for effective network planning and optimization through the review of historical transportation route data.

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Global Data science platform Market: Key trends

The key driver of the global data science platform market is organizations’ need for processes that facilitate simpler use of data to uncover actionable insights to grow their businesses. This has been necessitated by the rapid adoption of public cloud, artificial intelligence, Internet of Things (IoT) applications, machine learning, etc. Additionally, advancements in big data technologies and massive growth in data have also aided the growth of the market.

On the other hand, the factors that thwart the global data science platform market are unreliability of data science among enterprises and government rules and regulations.

Global Data science platform Market: Market potential

Very recently, in March 2017, the global internet giant Google acquired the data science platform Kaggle. Together, the two companies will nurture a thriving community of machine learning developers and data scientists, giving them direct access to a sophisticated cloud-based machine learning environment. More than 800,000 users make up the Kaggle community, ranging from companies like Facebook to entities like NASA. They use the platform to outsource data analysis through contests. They distribute prizes to the user who generates the best data models.

Based on business functions, the global data science market can be categorized into marketing, logistics, sales, risk, operations, customer support, and human resources. Among these, the logistics segment is expected to gain maximum market share in the coming years by developing at a rapid pace.

End-use industries that are rapidly adopting data science platforms include transportation and logistics, information and telecommunications, banking, financial services and insurance, government and defense, healthcare and life sciences, energy and utilities, media and entertainment, manufacturing, retail and consumer. goods, energy and utilities, etc.

A data science platform can be installed onsite or on demand. Of the two, the on-demand segment is expected to see increased demand over the next two years due to low operating cost, ease of deployment, and improved scalability and integration.

Global Data science platform Market: Regional Perspectives

Geographically, North America is a key region as it is home to significant market players and a large number of organizations that have been at the forefront of adopting new technologies. In the coming years, however, Asia-Pacific is likely to overtake all other regions in terms of growth rate due to increased foreign direct investment (FDI), political support for greater digitalization, initiatives smart cities of different governments and the rapid pace of industrialization. The fast growing developing economies of China, India, Australia and New Zealand are mainly driving the market in the region.

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Global Workplace Transformation Market: Competitive Analysis

Prominent names competing in the global data science platform market listed in the report are Domino Data Lab, IBM, Wolfram, Microsoft Corporation, DataRobot Inc., RapidMiner Inc., Sense Inc., Dataiku, Alteryx , Inc. and Continuum Analytics, Inc.

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