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Dec 5, 2013 · 29m · mad

Kirill Sheynkman, RTP Ventures // Data Driven #3 // Feb 2012 (Interviewed by Matt Turck)

Kirill Sheynkman · 22m spoken Matt Turck · 2m spoken
0:00 / 0:00
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At a Data Driven NYC event hosted by Matt Turck, RTP Ventures Senior Managing Director Kirill Sheynkman discusses his background as a software engineer turned venture capitalist, evaluating emerging big data technologies, investment strategies, and the realistic monetization of 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 9.5% of the talking time here. How this is scored →

Matt as informed peer 3.2 Guest teaching 5.5 Guest disagreement 2.5 Matt pushing back 2.2
05100:0010:0020:003:54–9:17 · Matt as informed peer 3/10 RTP Ventures Overview and Key Data Portfolio Companies Kirill walks through complex technical details of portfolio companies like GridGain and Drawn to Scale. Matt asks basic clarifying questions to ground the discussion for the audience while letting Kirill drive the technical explanation.9:17–12:51 · Matt as informed peer 1/10 Evaluating Big Data: Overcoming the 'So What?' Wall Kirill delivers an extended breakdown on the 'so what wall' in big data, arguing that industry excitement stops at raw data rather than reaching knowledge or wisdom. Matt steps back completely as Kirill educates the room.12:51–17:50 · Matt as informed peer 2/10 Emerging Opportunities and Technology Trends in Big Data Kirill lays out emerging tech trends including stream processing, graph databases, and machine-to-machine interaction. Matt allows Kirill to deliver a wide-ranging technical monologue without challenging his tech-centric vision.17:50–21:34 · Matt as informed peer 7/10 Monetizing Data vs. Building Data-Driven Businesses Matt pushes back on Kirill's heavily engineering-focused worldview, asking if he ignores non-tech business models due to personal bias. Matt then backs up his point with real-world experience at Bloomberg regarding data monetization.21:34–25:50 · Matt as informed peer 2/10 Audience Q&A: Proprietary Data vs. Open Data Sharing Audience Q&A takes over as Kirill fields questions on open data vs proprietary advantage and executive resistance to automated insights. Matt only interjects with a brief joke.25:50–29:22 · Matt as informed peer 4/10 Audience Q&A: Public Market Trends, IPOs, and VC Investment Cycles Kirill critiques misleading public market IPO statistics and discusses VC lifecycles. Matt provides brief technical assistance when Kirill stumbles on recent M&A deal specifics.3:54–9:17 · Guest teaching 6/10 RTP Ventures Overview and Key Data Portfolio Companies Kirill walks through complex technical details of portfolio companies like GridGain and Drawn to Scale. Matt asks basic clarifying questions to ground the discussion for the audience while letting Kirill drive the technical explanation.9:17–12:51 · Guest teaching 7/10 Evaluating Big Data: Overcoming the 'So What?' Wall Kirill delivers an extended breakdown on the 'so what wall' in big data, arguing that industry excitement stops at raw data rather than reaching knowledge or wisdom. Matt steps back completely as Kirill educates the room.12:51–17:50 · Guest teaching 6/10 Emerging Opportunities and Technology Trends in Big Data Kirill lays out emerging tech trends including stream processing, graph databases, and machine-to-machine interaction. Matt allows Kirill to deliver a wide-ranging technical monologue without challenging his tech-centric vision.17:50–21:34 · Guest teaching 4/10 Monetizing Data vs. Building Data-Driven Businesses Matt pushes back on Kirill's heavily engineering-focused worldview, asking if he ignores non-tech business models due to personal bias. Matt then backs up his point with real-world experience at Bloomberg regarding data monetization.21:34–25:50 · Guest teaching 5/10 Audience Q&A: Proprietary Data vs. Open Data Sharing Audience Q&A takes over as Kirill fields questions on open data vs proprietary advantage and executive resistance to automated insights. Matt only interjects with a brief joke.25:50–29:22 · Guest teaching 5/10 Audience Q&A: Public Market Trends, IPOs, and VC Investment Cycles Kirill critiques misleading public market IPO statistics and discusses VC lifecycles. Matt provides brief technical assistance when Kirill stumbles on recent M&A deal specifics.3:54–9:17 · Guest disagreement 2/10 RTP Ventures Overview and Key Data Portfolio Companies Kirill walks through complex technical details of portfolio companies like GridGain and Drawn to Scale. Matt asks basic clarifying questions to ground the discussion for the audience while letting Kirill drive the technical explanation.9:17–12:51 · Guest disagreement 2/10 Evaluating Big Data: Overcoming the 'So What?' Wall Kirill delivers an extended breakdown on the 'so what wall' in big data, arguing that industry excitement stops at raw data rather than reaching knowledge or wisdom. Matt steps back completely as Kirill educates the room.12:51–17:50 · Guest disagreement 2/10 Emerging Opportunities and Technology Trends in Big Data Kirill lays out emerging tech trends including stream processing, graph databases, and machine-to-machine interaction. Matt allows Kirill to deliver a wide-ranging technical monologue without challenging his tech-centric vision.17:50–21:34 · Guest disagreement 3/10 Monetizing Data vs. Building Data-Driven Businesses Matt pushes back on Kirill's heavily engineering-focused worldview, asking if he ignores non-tech business models due to personal bias. Matt then backs up his point with real-world experience at Bloomberg regarding data monetization.21:34–25:50 · Guest disagreement 3/10 Audience Q&A: Proprietary Data vs. Open Data Sharing Audience Q&A takes over as Kirill fields questions on open data vs proprietary advantage and executive resistance to automated insights. Matt only interjects with a brief joke.25:50–29:22 · Guest disagreement 3/10 Audience Q&A: Public Market Trends, IPOs, and VC Investment Cycles Kirill critiques misleading public market IPO statistics and discusses VC lifecycles. Matt provides brief technical assistance when Kirill stumbles on recent M&A deal specifics.3:54–9:17 · Matt pushing back 2/10 RTP Ventures Overview and Key Data Portfolio Companies Kirill walks through complex technical details of portfolio companies like GridGain and Drawn to Scale. Matt asks basic clarifying questions to ground the discussion for the audience while letting Kirill drive the technical explanation.9:17–12:51 · Matt pushing back 1/10 Evaluating Big Data: Overcoming the 'So What?' Wall Kirill delivers an extended breakdown on the 'so what wall' in big data, arguing that industry excitement stops at raw data rather than reaching knowledge or wisdom. Matt steps back completely as Kirill educates the room.12:51–17:50 · Matt pushing back 1/10 Emerging Opportunities and Technology Trends in Big Data Kirill lays out emerging tech trends including stream processing, graph databases, and machine-to-machine interaction. Matt allows Kirill to deliver a wide-ranging technical monologue without challenging his tech-centric vision.17:50–21:34 · Matt pushing back 6/10 Monetizing Data vs. Building Data-Driven Businesses Matt pushes back on Kirill's heavily engineering-focused worldview, asking if he ignores non-tech business models due to personal bias. Matt then backs up his point with real-world experience at Bloomberg regarding data monetization.21:34–25:50 · Matt pushing back 1/10 Audience Q&A: Proprietary Data vs. Open Data Sharing Audience Q&A takes over as Kirill fields questions on open data vs proprietary advantage and executive resistance to automated insights. Matt only interjects with a brief joke.25:50–29:22 · Matt pushing back 2/10 Audience Q&A: Public Market Trends, IPOs, and VC Investment Cycles Kirill critiques misleading public market IPO statistics and discusses VC lifecycles. Matt provides brief technical assistance when Kirill stumbles on recent M&A deal specifics.

speaking balance: gold is Matt, purple is the guest (3 minute bins)

0:00 · Matt 25.2% · guest 74.8%0:00 · Matt 25.2% · guest 74.8%3:00 · Matt 4.2% · guest 95.8%3:00 · Matt 4.2% · guest 95.8%6:00 · Matt 5.7% · guest 94.3%6:00 · Matt 5.7% · guest 94.3%9:00 · Matt 6% · guest 94%9:00 · Matt 6% · guest 94%12:00 · Matt 12.3% · guest 87.7%12:00 · Matt 12.3% · guest 87.7%15:00 · Matt 5.7% · guest 94.3%15:00 · Matt 5.7% · guest 94.3%18:00 · Matt 7.8% · guest 92.2%18:00 · Matt 7.8% · guest 92.2%21:00 · Matt 19.5% · guest 80.5%21:00 · Matt 19.5% · guest 80.5%24:00 · Matt 0.7% · guest 99.3%24:00 · Matt 0.7% · guest 99.3%27:00 · Matt 8.8% · guest 91.2%27:00 · Matt 8.8% · guest 91.2%
Sharpest disagreement ▶ 19:45 Rejecting 'monetizing data' as a real business model

Kirill forcefully dismisses the popular belief that simply gathering data constitutes a valid revenue strategy, mocking VCs who fund raw data plays without clear value.

 Hardest push from Matt ▶ 17:50 Challenging guest's engineering bias

Matt directly challenges Kirill's technical monologue, questioning if Kirill's focus on infrastructure is merely a personal preference rather than a lack of market opportunity elsewhere.

 Biggest teaching moment ▶ 10:20 Data to wisdom value chain

Kirill reframes the entire big data narrative, educating the audience on why most big data tools fail to deliver actual business knowledge or wisdom.

 Matt holds his own ▶ 21:00 Matt validates data sales difficulty via Bloomberg

Matt asserts his own domain expertise by providing concrete examples from Bloomberg to prove how difficult and unlucrative selling raw data to major institutions actually is.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
RTP Ventures Overview and Key Data Portfolio Companies 3622 Kirill walks through complex technical details of portfolio companies like GridGain and Drawn to Scale. Matt asks basic clarifying questions to ground the discussion for the audience while letting Kirill drive the technical explanation.
Evaluating Big Data: Overcoming the 'So What?' Wall 1721 Kirill delivers an extended breakdown on the 'so what wall' in big data, arguing that industry excitement stops at raw data rather than reaching knowledge or wisdom. Matt steps back completely as Kirill educates the room.
Emerging Opportunities and Technology Trends in Big Data 2621 Kirill lays out emerging tech trends including stream processing, graph databases, and machine-to-machine interaction. Matt allows Kirill to deliver a wide-ranging technical monologue without challenging his tech-centric vision.
Monetizing Data vs. Building Data-Driven Businesses 7436 Matt pushes back on Kirill's heavily engineering-focused worldview, asking if he ignores non-tech business models due to personal bias. Matt then backs up his point with real-world experience at Bloomberg regarding data monetization.
Audience Q&A: Proprietary Data vs. Open Data Sharing 2531 Audience Q&A takes over as Kirill fields questions on open data vs proprietary advantage and executive resistance to automated insights. Matt only interjects with a brief joke.
Audience Q&A: Public Market Trends, IPOs, and VC Investment Cycles 4532 Kirill critiques misleading public market IPO statistics and discusses VC lifecycles. Matt provides brief technical assistance when Kirill stumbles on recent M&A deal specifics.

Statements from this episode (11)

Insight
Sheynkman: Venture capital should be your last job, not your first
“VC should be your last job, not your first job”
Kirill Sheynkman Dec 5, 2013 ▶ 2:03
Assertion Not publicly verifiable
Kirill Sheynkman: Apple uses GridGain for supply chain analytics
“Apple uses them to supply chain analytics.”
Kirill Sheynkman Dec 5, 2013 ▶ 5:28
Assertion Not checkable as stated
Sheynkman: No big data startup has reached actionable knowledge yet
“With the big data world, we're excited about the data part. We're starting to get some information out with visualization and alerting and monitoring, things like that. No one has yet gotten to knowledge.”
Kirill Sheynkman Dec 5, 2013 ▶ 11:05
Insight
Sheynkman: Analytics software must be transparent glass boxes, not black boxes
“We don't want our analytics to be black boxes. We want them to be boxes with a glass top that I can look and see how things happen, open it up, reach in and figure out what went on there.”
Kirill Sheynkman Dec 5, 2013 ▶ 12:02
Prediction Not checkable as stated
Kirill Sheynkman: Automated machine-to-machine processing will dominate real-time data systems
“Really a lot of the trading that goes on is actually done by machines receiving feeds from other machines, and I think the same is true, is going to be true with systems, that systems that understand signals, that, that can react.”
Kirill Sheynkman Dec 5, 2013 ▶ 14:13
Assertion Supported
Kirill Sheynkman: No standard language exists yet for real-time streaming data transformation
“And we have, we don't have a language yet for transforming streaming data and operating on it in real time.”
Kirill Sheynkman Dec 5, 2013 ▶ 15:21
Opinion
Kirill Sheynkman: Tableau is a good company, but limited by Windows-only support
“I like Tableau as a company. It's in Seattle. Unfortunately, they're Windows only.”
Kirill Sheynkman Dec 5, 2013 ▶ 16:03
Insight
Sheynkman: Technology cycles start at infrastructure before moving to applications
“You start with the infrastructure layers, and you start at the bottom, and you gradually move up in terms of things.”
Kirill Sheynkman Dec 5, 2013 ▶ 18:19
Prediction Not checkable as stated
Sheynkman: Businesses built solely on selling raw data are unlikely to succeed
“So now we're going to generate a lot of data, and that's supposed to be a revenue model, and that's supposed to be for us VCs going, oh yeah, they're going to have lots of data, let's, let's, let's give them money. Not really. So what are you going to do with …”
Kirill Sheynkman Dec 5, 2013 ▶ 20:21
Assertion Not checkable as stated
Turck: Startups selling raw data to Bloomberg earn less than projected
“I think that the business of selling data is, is a very difficult business, and from a Bloomberg perspective, we've, you know, spoken, or we speak very often to startups that come to us with the hope to sell their data, raw data, and I think they quite frequen…”
Matt Turck Dec 5, 2013 ▶ 21:01
Opinion
Sheynkman: Core corporate data is a competitive advantage and shouldn't be public
“There's certain things that are very proprietary and they should be proprietary, right? And I think that, that some of the data that Federal Express and Amazon and Walmart have, That's their competitive advantage, and why would they ever want to publicize it? …”
Kirill Sheynkman Dec 5, 2013 ▶ 22:22
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