Over the last decade, data science has been rapidly progressing both as a technology and as a discipline. Best practices have been created by the leading businesses and it is now becoming part of the operational core for organizations. However, there is a need for a next step for product evolution in data science platform that supports and provides both business users an integrated solution for managing, building, and optimizing predictive models.
Nowadays, Data Science Platform is the most talked about topic in data science meet-ups, conferences, and top publications. According to a Research Dive analyst review, the concept of data science platform is not novel in the big data space but the need of data science platform in business is still unknown to many.
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Need for a Data Science Platform
1) To Enable Better Teamwork with Data Scientists
If the data scientists are solving the same problem in several ways, the productivity will decrease as it won’t deliver effectual value to the organization. One of the best solutions to ensure effective teamwork with data scientists is to provide them with a centralized flexible platform and the required set of tools to work upon. By using a data science platform, it ensures that all the contributions of the data scientists i.e. data models, data visualizations, and code libraries exist in a single shared reachable location. This helps data scientists to reuse the code, facilitate better discussion around research projects, and share best practices to make data science easily scalable and less resource exhaustive.
2) Help Minimalize Engineering Effort
With data science platforms, the data scientists get help in moving analytical models into production without any need of additional engineering effort or DevOps. For instance, if a company wants to build a product recommendation engine then the data scientist will require the efforts of a software engineer for testing, refining and integrating the data model before the users start seeing the product recommendations on the basis of their behavior. A data science platform makes sure that the data models are accessible behind an API so that the data scientists do not have to depend much on engineering efforts.
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3) Help to Offload a Number of Low Value Tasks
The burden of data scientists is released with the help of data science platforms. The burden of low value tasks such as reproducing past results, configuring environments for non-technical users, running reports, and scheduling jobs is offloaded from data scientists.
4) Facilitate Faster Research and Experimentation
Data scientists do not have to deal with extra data management tasks, as data science platforms allow people to see what and how others are working on. Moreover, whenever there is a new hire in the data science team, the employee can quickly start working as it is easier to restore the work of the people who leave through a unified platform over various isolated tools.
The Market Overview
Currently, the global market for data science platform is progressing rapidly and is about to positively grow in the near future. According to the Research Dive report, the global data science platform market is projected to garner a revenue of $224.3 billion at a 31.1% CAGR from 2019 to 2026. This is majorly due to the growing adoption of analytical tools across the globe for learning the unobserved customer purchasing pattern. The key prominent players of the market are adopting several strategies such as product development along with many approaches such as collaborations and R&D activities to stand strong in the global market. The major players of the global data science market include Alphabet Inc. (Google), Databricks, Domino Data Lab, Inc., Civis Analytics, Dataiku, Cloudera, Inc., IBM Corporation, Anaconda, Inc., Microsoft Corporation., and Altair Engineering, Inc.
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