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Series: Winning in Analytics!

Industrialize your Analytics to Enterprise Level

This article is Part 1 of the series “Winning in Analytics!”. Let’s look at key enablers, to scale your AI initiatives with success.

Diwahar Jawahar
Towards Data Science
3 min readJun 29, 2020

Photo by Tim Mossholder on Unsplash

Dear AI Enthusiasts, we love to realize the full potential of our data! We would love to see our analytics proof of concepts achieve reality! But often the path to industrializing your proof of concepts is arduous.

So, how can we transform your organization from having few analytic proofs of concepts to industrializing analytics to achieve Enterprise AI? Listed below, are key considerations that can help you scale your analytics products.

1. Bring stability to your data platform

Your models are only as good as your data! Ensure high stability and availability of data platforms to scale your analytics models rapidly. Failing to do so may result in unwanted delays in feeding data in a timely manner into decision-making engines.

  • Automate your data pipelines to handle data engineering tasks that are repetitive and/or error-prone.
  • To improve the quality of code, consider an organization-wide testing strategy in place.

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Towards Data Science
Towards Data Science

Published in Towards Data Science

Your home for data science and AI. The world’s leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.

Diwahar Jawahar
Diwahar Jawahar

Written by Diwahar Jawahar

Tech & Analytics Consultant with a penchant for numbers and storytelling. Works as a Senior Consultant at Information Services Group.

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