Why You Need a Data Science Platform More Than You Think

July 1, 2020 | 4 minute read
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Focusing on business outcomes has taken on a new meaning. Before, it was possible for data scientists to get dragged into mundane tasks or time-consuming experimentation with a variety of open source tools in the name of innovation. Collaboration was often an afterthought or extremely difficult to achieve across the enterprise. And the last step of data science, deployment of models to the enterprise, was rarely achieved. 

But today, not achieving data science-driven outcomes arguably has a greater cost than it did previously. 

Simply put, now’s the time to consider a data science platform for your enterprise.

Let’s break down why companies make the leap and how to know when it’s the right time for your organization to make the move.

Try the Accelerated Data Science (ADS) SDK) in Oracle Cloud Infrastructure Data Science for free! 


What the Best Enterprise Data Science Platforms Focus On

The market landscape for data science, machine learning, and AI is fragmented and competitive; its complexity makes it difficult to thoroughly comprehend. Gartner defines a data science and machine learning platform as a cohesive software application that offers a mix of basic building blocks essential for creating many kinds of data science solutions and incorporating such solutions into business processes, surrounding infrastructure, and products. 

The primary users for these platforms are typically expert data scientists, citizen data scientists, data engineers, and machine learning engineers or specialists. 

In general, the best data science platforms aim to:

  • Make data scientists more productive by helping them accelerate and deliver models faster with less error
  • Make it easier for data scientists to work with large volumes and varieties of data
  • Deliver trusted, enterprise-grade artificial intelligence that’s bias-free, auditable, and reproducible 


Why Data Science Platforms Are the Best Way to Use Open Source

Because data scientists often begin learning on open-source tools, they continue to want to use them after moving into enterprise roles. But solely relying on open-source tools can create a number of challenges, including:

  • Difficulty managing different tools with different releases
  • Complications that arise with sharing code and sharing models
  • Governance and security issues
  • Time and cost involved in integrating and maintaining these tools
  • Difficulty in deploying machine learning models into business dashboards and systems

The best way for data scientists to gain the benefits of open source without having to deal with these challenges is to choose a data science platform that offers access to managed open-source tools and libraries. By doing so, data scientists have an effective way to organize and manage their work while still using their favorite tools and libraries. Another upside? Data scientists no longer have to rely on IT to set up or maintain their preferred tools and libraries. 

Additionally, tools alone don’t address the need for team collaboration and the larger data science lifecycle that exists between IT, business analysts, and developers. An effective data science platform ensures that machine learning models can be consistently operationalized across the enterprise and that data from multiple places, like on-premise, in the cloud, and hybrid management environments, can be found, shared, and used productively by teams.

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Collaborative Machine Learning Platform Offerings

At their core, data science platforms have tools that data scientists need to support open-source library languages and frameworks. The right collaborative platform should also offer a rich portfolio of integrated products and components that help with various stages of the data science lifecycle. 

Because collaboration between data scientists, IT, business analysts, and developers is essential to driving productivity and business outcomes, the best data science platforms offer capabilities that cover:

  • Data ingestion
  • Data preparation
  • Data exploration
  • Feature engineering
  • Model creation and training
  • Model testing 
  • Deployment
  • Monitoring 
  • Maintenance

A complete and integrated data science platform should go beyond the core ability to aid in the data science lifecycle. It should provide ways to ingest and transform data. It should provide ways to manage and secure the data. And it should provide additional services, such as an analytics service for visualization, a graph analytics service to augment machine learning, or a data catalog to explore data. Cloud-based data science platforms also offer the additional benefits of managed services, unlimited storage and compute, and a more integrated environment for team collaboration.


Data Science Platforms in Action

One of the best ways to understand a data science platform’s value is through use cases. Interestingly, agronomy has embraced data science to help manage crop disease and fight food scarcity.

An Israeli agricultural company is successfully using a cloud-based data science platform to analyze data captured by autonomous drones that fly over fields and take images of crops. These images are uploaded into the cloud, and machine learning is used to analyze them and spot crop disease. Farmers can then spray for pests when and where pesticides are most needed.

Moving to a cloud-based data science platform enabled the agricultural company to transition from a static application to a dynamic one that uses cloud-native containers and DevOps to support microservices. This has enabled daily global onboarding of new customers and the ability to update application versions in minutes versus the 24 hours that were needed previously.

The platform has also increased access to computing power, enabling farmers to query and compare thousands of images in a matter of minutes to better diagnose the state of their crops.


When Do You Move to a Data Science Platform?

Signs your organization is ready for a data science platform arise when productivity and collaboration show signs of strain, machine learning models can’t be audited or reproduced, and models never make it into production.

Don’t wait for that moment. 

Oracle offers a wide range of services to help with data science. Most notably, it offers two services, Oracle Cloud Infrastructure Data Science, which helps enterprises collaboratively build, train, manage, and deploy machine learning models to increase the success of data science projects today, and Oracle Machine Learning, which uses the power of in-database machine learning to power data science projects. Each of these services offers workshops so you can get started easily. Start innovating today to see what results you can discover.

Oracle Cloud Infrastructure Data Science Workshop

Oracle Machine Learning Workshop

You can also try Oracle Cloud Infrastructure Data Science for free.

To learn more about how Oracle’s data science solutions can benefit your business, visit the Oracle Data Science page, and follow us on Twitter @OracleDataSci.  

Aali Masood

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