Open-source tools for analytics have been available for decades. However, it is only recently that enterprises have started to adopt and even hire data scientists for analytics needs. In a recent survey on Artificial Intelligence (AI) and Machine Learning (ML), the majority of respondents have stated that open-source was extremely important to their AI/ML efforts. Along with open-source, commercial products are also being used by organizations for analytics. About 45% of respondents stated in the same survey that they use both open-source and commercial products. This has made commercial vendors recognize the significance of open-source and hence there is an increase in enterprises implementing open-source in their products. 

What’s the use of Open-source?

Open-source ecosystems come with numerous benefits. For starters, the code is free to use and can easily be considered as a low-cost entry point for advanced analytics. It provides new ways to handle new data types, pre-built templates are also available to make the development process faster. Finally, there’s no dependency on any outside vendors. 

What’s the use of Commercial Products? 

Commercial products are easy to use, provides end-to-end functionality, support from vendors, pipelines and data transformation built-in software, etc

Open-source tools for AI/ML

Many open-source projects are available in the market out there. Out of which R and Phyton are the most popular ones. Although R is slightly ahead of Phyton, many organizations have started to replace R with Phyton programming. Besides R and Phyton, there are other open-source projects and frameworks out there such as TensorFlow, Scala, OpenNLP, Postgresql. 

The balance between Open-source and Commercial Products

Most companies use a combination of both open-source and commercial products, but how will they find the right balance between both? Before adopting organizations have to consider some important factors – Organizational, Deployment, and Environmental factors. 

  • Organizational Factors – Factors such as skills, ease to use, culture, budget, and support has to be considered when choosing the kind of software for development and deployment.
  • Deployment Factors – Batch vs real-time models for decision making and the maturity factors of a firm.
  • Environmental Factors – Compliance and risk tolerance of the company should be measured.

Check out this full report on Using Hydrid Open-source and Commercial Analytics Ecosystem by TDWI Pulse.

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