Oct 16, 2014 · 22m · mad
Mike Abbott, KPCB // Data Driven #30 // Oct 2014 (Hosted by FirstMark Capital)
Mike Abbott · 16m spoken Matt Turck · 2m spoken Matthew Leenum · 26s spoken Elliot Noma · 10s spoken
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this fireside chat hosted by FirstMark Capital, venture capitalist and former tech executive Mike Abbott shares insights on the evolution of big data, practical machine learning applications, and enterprise software investment opportunities. Abbott provides tactical advice for tech founders on overcoming enterprise operational hurdles and navigating white space in the big data ecosystem.
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 10.9% of the talking time here. How this is scored →
Matt as informed peer 2.4 Guest teaching 2.4 Guest disagreement 0.6 Matt pushing back 1.0
speaking balance: gold is Matt, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 6:43 Rejection of financial bubble premise
Abbott counters the host's framing of a financial bubble by steering the topic to customer expectation gaps and painkiller vs vitamin product positioning.
Hardest push from Matt ▶ 6:25 Challenging big data valuationsTurck directly pushes Abbott on whether high valuations and early-stage big data companies indicate a market bubble.
Biggest teaching moment ▶ 3:05 Explaining data obesity and Hadoop ROIAbbott educates the host and audience on how easy data storage leads to data obesity and lack of clear ROI on Hadoop clusters.
Matt holds his own ▶ 10:56 Probing NoSQL database market saturationTurck draws on domain knowledge to question whether entrepreneurs should avoid building new NoSQL database companies.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Matt as informed peer | Guest teaching | Guest disagreement | Matt pushing back | Why |
|---|---|---|---|---|---|---|
| Mike Abbott's Tech Journey: From PhD to Twitter & VC | 1 | 2 | 0 | 0 | The host opens with a simple request for the guest's career story. Abbott provides a detailed narrative of his journey across academia, startups, Microsoft, and Twitter without host interruption or challenge. | |
| Big Data Lessons, Data Obesity, and Hadoop ROI | 2 | 3 | 0 | 0 | The host asks an open question about big data lessons from Twitter. Abbott delivers an insightful explanation of data obesity and Hadoop ROI challenges without dynamic friction. | |
| Enterprise Pain Points vs. the Big Data Bubble | 3 | 3 | 2 | 3 | The host presses Abbott on whether big data is in a valuation bubble. Abbott mildly reframes the premise, focusing on the gap between customer expectations and actual product delivery rather than financial valuations. | |
| Oversaturated Big Data Categories and Emerging Tech Opportunities | 4 | 2 | 1 | 2 | The host demonstrates solid domain knowledge by probing into oversaturated markets like NoSQL databases and referencing Kaggle competitions. Abbott elaborates on market realities and self-service analytics. | |
| Evaluating Big Data Startups & VC Pitch Advice | 2 | 2 | 0 | 0 | The host asks standard VC pitch criteria questions before facilitating an audience Q&A session. Abbott takes questions from attendees on machine learning and B2B sales lead generation. |
Statements from this episode (14)
Kleiner Perkins' largest investment check ever was into Twitter
“The largest check that the KP's ever written was in the Twitter.”
Cheap storage causes enterprise and consumer companies to suffer from data obesity
“The cost and ability to store tons of data is, is so prevalent both in consumer and enterprise companies that I think oftentimes you end up in this kind of data obesity state where you start storing data for reasons you don't even know why you're storing it.”
Large enterprise companies will eventually move production workloads onto Hadoop
“I think it will happen.”
Kaggle competition winners rely primarily on feature engineering and algorithm ensembles
“If you look at, you know, who wins most of the Kaggle competitions, it tends to be, you know, combinations of ensembles of different algorithms, but it's really the feature engineering.”
US government intelligence agencies were using big data software in 2004
“We had three-letter agencies using our software 10 years ago, like, plus, and they certainly had more than big data, so it's not new.”
Differentiating big data startups purely on data visualization is getting difficult
“It just feels like between D three and other tools, that that area, it's getting more difficult to differentiate just on visualization. You have to have other pieces.”
Enterprise data lineage and SOX compliance remain major open opportunity areas
“I think that's a, Still, like, a big open area on, in the lineage side, especially when you start linking into things around, like, SOX compliance, and, like, what does that actually mean?”
Data science will always require specialized professionals with strong statistics backgrounds
“I think there's always going to be a need for someone who has a background in statistics and can understand how to use tools to understand what are the questions that should be asked, how to do the feature engineering, to understand your business, whether it b…”
Data analytics startups will eventually dislodge incumbents like SAS
“I do think that the incumbent players like SAS I mean, I think over time will hopefully be dislodged by, you know, some number of startups.”
Big data startups greatly underestimate the requirements for enterprise readiness
“I think that in general, I try to determine does this team have a strong enough understanding of what it's like to sell to the Fortune 2000 or 5000 to know what it means to be enterprise ready? I think that oftentimes, ah, companies greatly underestimate that.”
Kleiner Perkins passes on founders who cannot justify their tech stack choices
“And I'm not saying that the decisions that that company made were wrong, necessarily, but I think that at the end of the day, technology is here to solve a problem, and if you don't know why you selected a particular, For particular, particular technology, I t…”
Most data growth to 40 zettabytes by 2020 will originate from sensors
“If you look at the, you know, the forecasts that are going from the 2.8 zettabytes to 40 zettabytes in 2020 that's, most of that data is gonna be coming from sensors, right?”
Big tech companies, not startups, will drive machine learning innovations
“I think we're gonna probably be seeing innovations from those types of companies, ah, before startups.”
Companies mine consumer property data to identify B2B lead generation opportunities
“Insurance, you know, homes, there's all this, like, if you look at the statistics of, okay, marriage is oftentimes nine months past that, like, you know, different regions of the country, you could also predict maybe when they go buy a home, and so actually, l…”