Is your data ready for AI?
The headline: Everybody's ready for AI except for your data. The time to prepare your data for AI was yesterday, but it's not too late to start today.
Why it matters: Data is the bedrock of AI. But good data isn't an accident -- it takes a little bit of foresight and a lot of governance, resilience, and patience.
I ran a half-marathon last weekend. It wasn't a race -- a friend hosting a Memorial Day party lives 13.1 miles from me... so I ran there. It took me 2:19. Not much to brag about, but it was a new personal record for me.
I cut through a familiar neighborhood. Its street trees offered shade and its sidewalks kept me safe. Unlike the live oaks in New Orleans, the trees weren't 70+ years old, but also hadn't (yet) caused the sidewalks to heave.
My first job
My first job was with Waterford Township . Starting at 15 years old, I worked in Town Hall after school. I built databases, drafted maps, and worked with developers on new subdivisions. I wore a tie (and thought it was cool).
At 16 years old, one of my job responsibilities included making sure developers installed street trees and sidewalks in new subdivisions. This included collecting a bond to guarantee their work and inspecting they followed our ordinances.
I wasn't popular. Not referring to high school here (although also true), but with the developers. The trees and sidewalks cost money. The bond cost money. And me, a nerdy high school student making $5.50 per hour, was the culprit.
I was cussed out by a developer once. I caught him red-handed in a lie and called him on it. During our call, he never imagined I was 16. His jaw hit the counter when he handed his bond to (115 pound) me in person.
Back to my run
My run was nostalgic. I was running on sidewalks and beneath the shade of the trees I had inspected 25+ years prior. I wasn't popular, but was a public servant and believe the work I did mattered. It made a difference.
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What I realized: The developers weren't thinking 25+ years ahead. They sought to maximize the short-term rather than optimize the long-term. A little bit of foresight with a lot of governance, resilience, and patience paid off:
Back to data
Giving credit: I just returned from 咨科和信 World 2024. 咨科和信 coined that "Everybody's ready for AI except your data." It resonates.
The takeaway: As data professionals, we're responsible to have just a little bit of foresight in helping our organizations succeed with AI. This means preparing today while thinking about the next 25+ years. That will take a lot of:
Call to action: I can't say it any better than Richard Cramer , Informatica's Chief Healthcare Strategist. Take 76 seconds to listen to his perspective on data readiness for AI. Then, assess your organization's readiness or ask for help.
Final thought: Thanks to Joseph Gallo , a fellow public servant, HOA board member, and data executive, who jogged (pun intended) my memory of the street tree and sidewalk inspector bullet on my resume last week.
This article is part of my blog, Running Thoughts on Data. My first post, The Story My Data Cannot Tell, shares the genesis of my blog. The views and postings on this site are my own and do not necessarily represent those of Plante Moran.
Data is an asset. I’m a data investment advisor.
9 个月Mark Kellenberger, Melissa Kellenberger, hope you both are well! These were some fun days - you may find the story nostalgic. Do you remember who the developer was? His name is burned in my memory. ??