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Dec 5, 2013 · 22m · mad

Chris Moody, Gnip // Data Driven NYC #3 // Feb 2012

Chris Moody · 16m spoken Matt Turck · 1m spoken
0:00 / 0:00
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

At a Data Driven NYC meetup, Gnip President and COO Chris Moody presents how enterprise organizations leverage multi-channel social data infrastructure, analytical frameworks, and temporal patterns to drive strategic decision-making across business operations.

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 5.4% of the talking time here. How this is scored →

Matt as informed peer 1.0 Guest teaching 2.8 Guest disagreement 0.6 Matt pushing back 0.6
05100:0010:0020:000:45–3:17 · Matt as informed peer 1/10 Gnip Overview and Core Mission Statement Chris opens with a lighthearted joke about Matt framing him as a boring business guy before delivering Gnip's core pitch on public social conversation value for enterprises. The host barely participates after his quick setup.3:17–7:08 · Matt as informed peer 0/10 Gnip Scale, Milestones, and Terminal Data Integration This is a monologue presentation detailing Gnip's scale, Fortune 500 reach, and Bloomberg terminal integration. Because it is a monologue with no host speech, host-side scores are zero.7:08–9:56 · Matt as informed peer 0/10 Evaluating Social Sources: Reaction Time vs. Content Depth Chris delivers a monologue slide presentation explaining how different social sources trade off reaction speed against content depth. Host remains silent.9:56–12:48 · Matt as informed peer 0/10 Case Study: Market Response to Netflix Earnings Chris presents a case study on Netflix earnings reactions and contrasts expected versus unexpected events. Host provides no input during this monologue.12:48–22:00 · Matt as informed peer 4/10 What's Next for Enterprise Social Data Adoption Matt enters to ask an informed question contrasting Gnip with recent presenter DataSift before moderating audience questions. Chris responds politely across topics ranging from government use cases to hiring in Boulder.0:45–3:17 · Guest teaching 3/10 Gnip Overview and Core Mission Statement Chris opens with a lighthearted joke about Matt framing him as a boring business guy before delivering Gnip's core pitch on public social conversation value for enterprises. The host barely participates after his quick setup.3:17–7:08 · Guest teaching 3/10 Gnip Scale, Milestones, and Terminal Data Integration This is a monologue presentation detailing Gnip's scale, Fortune 500 reach, and Bloomberg terminal integration. Because it is a monologue with no host speech, host-side scores are zero.7:08–9:56 · Guest teaching 2/10 Evaluating Social Sources: Reaction Time vs. Content Depth Chris delivers a monologue slide presentation explaining how different social sources trade off reaction speed against content depth. Host remains silent.9:56–12:48 · Guest teaching 2/10 Case Study: Market Response to Netflix Earnings Chris presents a case study on Netflix earnings reactions and contrasts expected versus unexpected events. Host provides no input during this monologue.12:48–22:00 · Guest teaching 4/10 What's Next for Enterprise Social Data Adoption Matt enters to ask an informed question contrasting Gnip with recent presenter DataSift before moderating audience questions. Chris responds politely across topics ranging from government use cases to hiring in Boulder.0:45–3:17 · Guest disagreement 2/10 Gnip Overview and Core Mission Statement Chris opens with a lighthearted joke about Matt framing him as a boring business guy before delivering Gnip's core pitch on public social conversation value for enterprises. The host barely participates after his quick setup.3:17–7:08 · Guest disagreement 0/10 Gnip Scale, Milestones, and Terminal Data Integration This is a monologue presentation detailing Gnip's scale, Fortune 500 reach, and Bloomberg terminal integration. Because it is a monologue with no host speech, host-side scores are zero.7:08–9:56 · Guest disagreement 0/10 Evaluating Social Sources: Reaction Time vs. Content Depth Chris delivers a monologue slide presentation explaining how different social sources trade off reaction speed against content depth. Host remains silent.9:56–12:48 · Guest disagreement 0/10 Case Study: Market Response to Netflix Earnings Chris presents a case study on Netflix earnings reactions and contrasts expected versus unexpected events. Host provides no input during this monologue.12:48–22:00 · Guest disagreement 1/10 What's Next for Enterprise Social Data Adoption Matt enters to ask an informed question contrasting Gnip with recent presenter DataSift before moderating audience questions. Chris responds politely across topics ranging from government use cases to hiring in Boulder.0:45–3:17 · Matt pushing back 0/10 Gnip Overview and Core Mission Statement Chris opens with a lighthearted joke about Matt framing him as a boring business guy before delivering Gnip's core pitch on public social conversation value for enterprises. The host barely participates after his quick setup.3:17–7:08 · Matt pushing back 0/10 Gnip Scale, Milestones, and Terminal Data Integration This is a monologue presentation detailing Gnip's scale, Fortune 500 reach, and Bloomberg terminal integration. Because it is a monologue with no host speech, host-side scores are zero.7:08–9:56 · Matt pushing back 0/10 Evaluating Social Sources: Reaction Time vs. Content Depth Chris delivers a monologue slide presentation explaining how different social sources trade off reaction speed against content depth. Host remains silent.9:56–12:48 · Matt pushing back 0/10 Case Study: Market Response to Netflix Earnings Chris presents a case study on Netflix earnings reactions and contrasts expected versus unexpected events. Host provides no input during this monologue.12:48–22:00 · Matt pushing back 3/10 What's Next for Enterprise Social Data Adoption Matt enters to ask an informed question contrasting Gnip with recent presenter DataSift before moderating audience questions. Chris responds politely across topics ranging from government use cases to hiring in Boulder.

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

0:00 · Matt 20% · guest 80%0:00 · Matt 20% · guest 80%3:00 · Matt 0% · guest 100%3:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%6:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%9:00 · Matt 0% · guest 100%12:00 · Matt 15% · guest 85%12:00 · Matt 15% · guest 85%15:00 · Matt 0% · guest 100%15:00 · Matt 0% · guest 100%18:00 · Matt 1.8% · guest 98.2%18:00 · Matt 1.8% · guest 98.2%21:00 · Matt 6% · guest 94%21:00 · Matt 6% · guest 94%
Sharpest disagreement ▶ 14:32 Chris sets boundaries on DataSift comparison

When asked about rival DataSift, Chris firmly limits the overlap by stating 'that's kind of where the comparison stops' before emphasizing Gnip's enterprise focus.

 Hardest push from Matt ▶ 14:01 Matt presses guest on competitor positioning

Matt pushes Chris to directly position Gnip relative to rival DataSift, noting DataSift presented at the prior meetup.

 Biggest teaching moment ▶ 18:29 Chris corrects assumption about analytics capabilities

Chris clarifies Gnip's core strategy to an audience member, explaining that Gnip strictly acts as a raw data pipe rather than building higher-level sentiment analytics.

 Matt holds his own ▶ 14:01 Matt leverages past meetup context to query competitor status

Matt displays domain knowledge of Gnip's competitive landscape by recalling DataSift's recent presentation and asking Chris to delineate their respective positions.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Gnip Overview and Core Mission Statement 1320 Chris opens with a lighthearted joke about Matt framing him as a boring business guy before delivering Gnip's core pitch on public social conversation value for enterprises. The host barely participates after his quick setup.
Gnip Scale, Milestones, and Terminal Data Integration 0300 This is a monologue presentation detailing Gnip's scale, Fortune 500 reach, and Bloomberg terminal integration. Because it is a monologue with no host speech, host-side scores are zero.
Evaluating Social Sources: Reaction Time vs. Content Depth 0200 Chris delivers a monologue slide presentation explaining how different social sources trade off reaction speed against content depth. Host remains silent.
Case Study: Market Response to Netflix Earnings 0200 Chris presents a case study on Netflix earnings reactions and contrasts expected versus unexpected events. Host provides no input during this monologue.
What's Next for Enterprise Social Data Adoption 4413 Matt enters to ask an informed question contrasting Gnip with recent presenter DataSift before moderating audience questions. Chris responds politely across topics ranging from government use cases to hiring in Boulder.

Statements from this episode (12)

Disclosure
Moody: Gnip normalizes over 100 social feeds into a single stream
“So what Gnip fundamentally does is we take data from lots of different social sources, I think there's something like over a hundred feeds of different social sources. We bring that data in, we normalize it so that you consume it as if it were one stream, not …”
Chris Moody Dec 5, 2013 ▶ 2:47
Assertion Not checkable as stated
Moody: Gnip serves eight of the nine largest social monitoring firms
“We're now serving, social media monitoring was the first industry that really adopted this data for businesses purposes, business purposes, and we're serving eight of the nine largest.”
Chris Moody Dec 5, 2013 ▶ 3:31
Assertion Not checkable as stated
Moody: Gnip processes over three billion social data activities daily
“It's over three billion activities a day.”
Chris Moody Dec 5, 2013 ▶ 3:55
Assertion Supported
Moody: Twitter chose Gnip as its first commercial data partner
“They chose Gnip to be the first partner To bring this stuff, ah, to market.”
Chris Moody Dec 5, 2013 ▶ 6:03
Assertion Not checkable as stated
Chris Moody: Twitter is the fastest breaking news source outside Bloomberg
“Twitter has become kind of the quickest way to find out about, ah, breaking news outside of Bloomberg.”
Chris Moody Dec 5, 2013 ▶ 7:20
Opinion
Moody: Twitter is ideal for PR crisis management due to reaction speed
“Something like Twitter is great for that, right? Because, again, it's super quick reaction time, and I don't need to know a lot of details.”
Chris Moody Dec 5, 2013 ▶ 8:51
Insight
Moody: Blog comments offer a distinct sentiment profile from primary content
“Comments represent the world's reaction to a point of view, so you get a very different sentiment around that data.”
Chris Moody Dec 5, 2013 ▶ 11:30
Insight
Moody: Social data curves repeat consistent patterns for expected vs unexpected events
“And the thing about this and why this is important is because these curves appear over and over again. Doesn't matter what the type of event is. We're seeing some general characteristics around expected versus unexpected.”
Chris Moody Dec 5, 2013 ▶ 12:20
Disclosure
Moody: Gnip serves 90% of Fortune 500 companies
“When I say we serve 90% of the Fortune 500, I've gotten a few questions like, oh, really, because there's some super boring companies in the Fortune 500. Like, are they really using social data? And the answer is they are”
Chris Moody Dec 5, 2013 ▶ 12:57
Assertion Supported
Moody: The U.S. government uses social media data for intelligence gathering
“The U.S. Government is a huge consumer of social data and in some use cases that your head probably would immediately go to or around, you know, intelligence and so forth.”
Chris Moody Dec 5, 2013 ▶ 16:43
Prediction Held up
Moody: Social data will integrate into the $40B business intelligence industry
“The entire, you know, there's a forty billion dollar business intelligence industry that is built around these traditional data warehouses and data marts that are driving a lot of the businesses within large companies Clearly, social is going to be entering in…”
Chris Moody Dec 5, 2013 ▶ 19:46
Assertion Contradicted
Moody: Boulder, Colorado, has the highest PhDs per capita in America
“They're actually, per capita, have more PhDs than any other city in the United States.”
Chris Moody Dec 5, 2013 ▶ 20:49
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