Creating an Enterprise AI Strategy: If Your Company Isn’t Good At Analytics, It’s Not Ready For AI
Creating an Enterprise AI Strategy: If Your Company Isn’t Good At Analytics, It’s Not Ready For AI

Abstract: 

Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go.

Baird will explore how teams also need to embrace advanced automation. Lack of automation means people manually use the ‘bug-entry device’ (a.k.a. the keyboard) to process pipelines, do ETL, move data and create downstream assets - these do not scale. All of these activities can and should be automated to avoid the very real outcome epitomized by the infamous AI phrase “garbage in, garbage out”.

If you do not have technology that creates and maintains data pipelines and basic data engineering, your ability to solve problems with AI will become elusive. AI requires a highly sophisticated application of data to draw correlations and operate properly. In other words, if you’re willing to put in a few years of diligent, hard work, you can become an overnight AI success.

Session attendees can expect to obtain several key takeaways from the discussion including:

•Data is KING: Baird will drive home the concept “get the right data, focus and understand how to use it.” This could be the difference between becoming the next Google or going out of business like Altavista. You don’t necessarily need to know WHY that data is important right now. Have you got data that is proprietary or impossible for your competitors to gather? Comb over that and figure out how it relates.

•Choose the Right Framework for Thought: AI done right will fundamentally change your business; whatever you start off believing is the correct path most certainly will not be. Attendees will learn how in highly dynamic situations there are two frameworks Baird recommends merging when thinking about something as transformational as AI: The Golden Triangle of People, Process and Tools and the OODA Loop.

•Focus On Outcomes, Not The Path To Them: Baird will walk through why it is important to avoid the natural tendency to want to understand all the details of why your AI or ML delivers conclusions and outcomes. It is not hyperbolic to say AI changes everything, every facet of the business. Attendees will walk away with knowledge of how capturing that value as soon as possible creates the momentum to enable an AI strategy.

•Avoid the Tyranny of Small Decisions: Within the session, attendees will learn how to avoid situations where a series of small, individually rational decisions negatively change the context of subsequent choices, even to the point where desired alternatives are irreversibly destroyed. While the devil may be in the details, moving a huge chunk of your business to be AI driven means being bold. Everyone will give you a reason why it won’t work. Attendees will be reminded that sometimes incredibly strategic decisions can be made quickly because some element is overwhelming.

Bio: 

Dave is one of the co-founders of AtScale and is the Chief Strategy Officer. Prior to AtScale, he was VP of Engineering at Klout & at Yahoo! where he built the world's largest multi-dimensional cube for BI on Hadoop. Mariani is a Big Data visionary & serial entrepreneur.

Open Data Science

 

 

 

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