Gartner recently published their annual Critical Capabilities for Data Science and Machine Learning Platforms 2018. The Critical Capabilities report is a complement to Gartner’s 2018 “Magic Quadrant for Data Science and Machine Learning Platforms.” The Critical Capabilities report provides prospective buyers with a detailed evaluation of vendors providing data science and machine learning solutions. For this report, Gartner stated that out of 70 competitors, they evaluated 17 platforms across 15 critical capabilities spanning three use cases.
We are very excited to be evaluated as a top 3 provider of all three data science and machine learning uses cases highlighted in the report and scored highest for the production refinement use case. An overview of these use cases and a few of the critical capabilities required to support them are summarized below:
Business Exploration: I like to think of this as you don’t know what you don’t know. Business Exploration typically occurs in the early stages of the analytical process and involves a combination of data exploration, data preparation, and data visualization. Gartner’s research suggests this is practiced by seasoned data scientists and increasingly, citizen data scientists (see my earlier blog post on Citizen Data Scientists). Effective Business Exploration typically involves a combination of data aggregation, data preparation for analytics, and data visualization. Many of these tasks are being increasingly augmented with automation fueled by AI and machine learning.
Advanced Prototyping: When people think about data science, this is typically what’s running through their mind. Advanced prototyping involves applying a combination of machine learning and other advanced analytic techniques to solve problems in novel ways. Garter suggests that many organizations use open source technologies for advanced prototyping. So, for an individual who’s been in the analytics business for over 20 years, I’d suggest that you look for a platform who fully embraces open source technology is compatible with R, Python, etc.
Production Refinement: According to Gartner, this is where data science teams spend most of their time. They further explain that “production refinement is still a vital stage of the machine learning life cycle and is most impacted by the delivery and model management capabilities.” For organizations that want to put their analytic models in production, I believe this is the most important use case and set of capabilities to consider to excel in and TIBCO scored highest. This is further punctuated by a speaker at an event who stated that model deployment and delivery is where data science goes from “academics to economics.”
Given TIBCO’s focus on helping organizations make better decisions and take action smarter and faster, I couldn’t be happier that we were positioned highest in the Production Refinement use case. We know from our customers that our ability to take analytic models, deploy them to production, manage, and update them after they are deployed is critically important. As analytics and industries become more regulated with policies like GDPR, the ability to understand who changed what, when, where, why, and how will become even more paramount in the coming months.
For more information on Gartner’s Critical Capabilities, download a complimentary copy of the report here.
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from TIBCO Software.
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