Jul 13, 2017 · 23m · mad
AI, Big Data, and Data Governance // Stan Christiaens, Collibra (FirstMark's Data Driven)
0:00 / 0:00
▶ Watch on YouTube →
gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this Data Driven NYC presentation and fireside chat, Collibra Founder & CTO Stan Christiaens explores the common pitfalls of artificial intelligence and demonstrates how robust data governance enables organizations to find, trust, and extract real value from their data assets.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Matt holds 4.9% of the talking time here. How this is scored →
Matt as informed peer 0.8 Guest teaching 3.2 Guest disagreement 1.8 Matt pushing back 0.3
speaking balance: gold is Matt, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 13:00 Rant on data scientist excuses
Christiaens expresses sharp frustration with data scientists complaining about lack of access, declaring that the excuse pisses him off and telling them to hack into internal databases if necessary.
Hardest push from Matt ▶ 18:59 Challenging data lake centralisationTurck presses Christiaens on whether centralized data lakes are superior to distributed repositories, challenging the traditional architecture assumption.
Biggest teaching moment ▶ 17:49 Catalog vs governance explanationChristiaens clarifies a common industry misconception by explaining that standalone data catalogs become useless phone books without overarching governance frameworks.
Matt holds his own ▶ 18:59 Framing agile governance choicesTurck demonstrates strong domain familiarity by articulating the technical trade-offs between centralized data lakes and distributed repository models requiring agile software.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Matt as informed peer | Guest teaching | Guest disagreement | Matt pushing back | Why |
|---|---|---|---|---|---|---|
| Frustrations in Unlocking Value from Data | 0 | 2 | 1 | 0 | This initial segment is a monologue presentation by guest Stan Christiaens outlining corporate frustrations with data value and predictions around the Chief Data Officer role. As a solo presentation, the host does not participate, keeping host-side metrics at zero. | |
| What Was Missed: The Explosion of AI & Machine Learning | 0 | 2 | 1 | 0 | Christiaens continues his presentation, explaining why Collibra previously missed the AI/ML surge due to past AI winter skepticism and NLP busts. The host remains silent throughout the presentation segment. | |
| Pitfall 2: Falling into the Snake Oil Salesman's Trap | 0 | 3 | 2 | 0 | Christiaens critiques AI hype, pointing out failures like IBM Watson's cancer initiative with MD Anderson and Google Flu Trends overfitting. The host is inactive during this presentation segment. | |
| Pitfall 3: Betting Too Much on Algorithms | 0 | 3 | 3 | 0 | Christiaens argues that data rather than algorithms provides true competitive differentiation, venting forcefully about data scientists making excuses about missing data. The host remains in listener mode with no dialogue. | |
| Fireside Chat: Data Governance vs. Data Cataloging | 4 | 5 | 2 | 2 | Host Matt Turck engages in a Q&A session, asking informed questions about data cataloging versus data governance and centralized data lakes versus distributed repos. Christiaens educates the audience on why catalogs without governance act like unmaintained phone books. | |
| Audience Q&A on Data Ownership and Privacy | 1 | 4 | 2 | 0 | An audience member asks about data ownership and privacy rights. Christiaens explains the contrast between European regulatory approaches and Silicon Valley practices while the host facilitates time management. |
Statements from this episode (14)
Data scientists' biggest problem is finding data
“One of their first frustrations is, I can't find the data, right? That's like their biggest problem.”
Data scientists quit when companies fail to operationalize their work
“So these talented people that you then hire are becoming demotivated and will actually go somewhere else just because their work is not actually adding any value to the business.”
Enterprise data needs a dedicated system of record like ERP or CRM
“Data will require a system of records, right, just like the chief financial officer as a CRM an ERP, or the VP of sales as a CRM system, data will have the same kind of need.”
Data users will revolt against Chief Data Officers who restrict access
“Data citizens who are all people who use data to do their work. In our company, product managers are data citizens, for example. Sales ops is data citizens. They will rise up against the data dictator. That's a chief data officer who tries too much to control …”
Big tech uses AI and machine learning as its next feature war
“And three, and this is a belief of me that could be wrong, is that actually the big tech firms, Amazon, Facebook, Google, Microsoft, and all the others, they're using AI and machine learning as their next feature war.”
AI will generate business value first in highly specialized applications
“So AI will have business value first in specialized applications.”
IBM Watson's $60M MD Anderson oncology project was a complete failure
“Now several years later, and sixty million dollars down the drain, that initiative failed. It actually failed.”
Google Flu Trends suffered a 140% prediction error in 2013
“So in 2013, they had a mismatch of a 140% prediction versus the actual situation in the world.”
Competitive advantage in AI comes from proprietary data, not algorithms
“Our belief is that AI and machine learning, you will not win this war by having the better algorithm. The differentiator, the value proposition, is not in the algorithm. It's actually in the data.”
Data treated as a strategic asset requires its own business process
“And data today, if you treat it as a strategic asset, should have its own business process.”
Data catalogs without governance inevitably fail and fall into disuse
“If you have a Data catalog, which is really like a listing of all the data sets and attributes, dictionaries that are out there, without governance around it, it's just like a phone book. It doesn't do anything, it's not controlled, it doesn't work, it's gonna…”
Twitter and LinkedIn's open-source data catalogs go unused without governance
“Reason why I say that is because I've talked to all the high-tech companies like Twitter, LinkedIn, et cetera, and they all have their catalog projects. Many of them have actually open sourced catalog initiatives, But then you find that nobody's actually using…”
Big tech data lock-in risks market monopolization without strict regulation
“The data is already in the hands of these technology companies, so that's definitely going to continue, and I'm hoping that the regulations will impose enough sanctions so that these big, ah, you know, internet giants are actually allowing more control of an i…”
Corporate data ownership must be snuck in to avoid executive reluctance
“Nobody wants to take responsibility. No, no, I don't want to be the data owner. So there, what you have to do, is you have to sort of sneak ownership under the door. You have to sort of say to the business executive, or the process or application owner, yeah, …”