How to Create an Enterprise AI Strategy: If Your Company Isn’t Good at Analytics, It’s Not Ready for AI
Recently, AtScale participated in the Azure Global Summer 2020 AI/ML DataFest. In the session, Dave Mariani, Co-founder and Chief Strategy Officer at AtScale and Daniel Gray, VP of Solutions Engineering at AtScale explained how succeeding in AI requires being good at data engineering AND analytics.
Management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as machine learning (ML) and artificial intelligence (AI) – setting themselves up for failure from the get-go. It’s key to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
When you think that 80% of knowledge workers and data scientists waste up to half of their time on cleaning up data, it makes perfect sense that ML and AI can only exist on top of a foundation built on a base of solid data and analytics. Another way to think about it is to look at what we at AtScale call “The Analytics Hierarchy of Needs”.
This diagram like Maslow’s Hierarchy of Needs is a classification system that shows that an enterprise needs to be successful at machine learning and artificial intelligence. If the basics aren’t in place, you aren’t going to achieve self actualization.
In the 2020 Big Data & Analytics Maturity Survey that AtScale did with Cloudera and ODPI.org, we learned that machine learning and artificial intelligence is still largely a DIY project for most enterprises today. The over 150 data and analytics leaders, IT/business intelligence practitioners, and business professionals from multiple industries around the globe we surveyed told us that Spark remains the technology of choice, along with Databricks and Cloudera Data Science Workbench. When respondents chose “other” the most frequent responses were: Pecan, Azure, and “don’t know yet.”
So, how can you start modernizing your technology stack to get ready for machine learning and artificial intelligence? Here are three things to think about.
To learn more about how to create your enterprise ML/AI strategy, take a look at our Creating an Enterprise AI Strategy presentation on Slideshare.