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Dec 5, 2013 · 1h 0m · mad

Panel discussion // Data Driven NYC #9 // Nov 2012

Christopher Ahlberg · 12m spoken Hjalmar Gislason (Halmar) · 11m spoken Nick Walter · 11m spoken Josh Becker · 7m spoken Matt Turck · 2m spoken Tony Baer · 51s spoken Micah Jepson · 50s spoken Michael Selick · 42s spoken Grant Case · 39s spoken Aaron Franco · 31s spoken Joe Gallardo · 22s spoken
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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 event hosted by Matt Turck, four data-driven startup founders discuss their entrepreneurial origins, market strategies, technical architectures, and solutions to real-world customer adoption challenges. Through panel discussion and audience Q&A, the speakers share actionable insights on building, scaling, and selling specialized big data platforms across legal, sports, quantitative, and threat intelligence domains.

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 1.3 Guest teaching 1.7 Guest disagreement 0.8 Matt pushing back 0.7
05100:0015:0030:0045:001:00:000:00–5:13 · Matt as informed peer 1/10 Panelist Introductions and Entrepreneurial Backgrounds Matt Turck welcomes the panelists and invites them to share their entrepreneurial backgrounds. The panelists deliver lighthearted, agreeable origin stories, including Nick Walter winning money on a game show to bootstrap his company.5:13–11:02 · Matt as informed peer 3/10 Market Readiness and Selling Big Data Solutions Matt frames the topic around the gap between VC hype and enterprise market readiness. When Christopher Ahlberg gives a broad answer, Matt pushes back to ask specifically about selling to conservative verticals like government and legal.11:02–17:01 · Matt as informed peer 3/10 Technical Stacks and Processing Architectures Matt inquires about technical stacks and asks if the guests use Hadoop. The guests educate the room on why conventional big data stacks like Hadoop aren't necessary for their workloads, revealing pragmatically 'janky' setups like Excel macros alongside Python and Postgres.17:01–21:28 · Matt as informed peer 0/10 Audience Q&A: Identifying Needs and Sports Competition Matt opens the floor to audience Q&A and acts solely as a facilitator. Audience members question Christopher on customer problem discovery and Nick on market crowding in sports analytics.21:28–31:20 · Matt as informed peer 0/10 Audience Q&A: Patent NLP and Predictive Back-Testing Audience members ask technical questions regarding patent claims processing and back-testing predictive algorithms. The panel explains confidence scores, historical node calculations, and overwhelming transparency strategies without host intervention.31:20–55:31 · Matt as informed peer 1/10 Audience Q&A: Industry Resistance, Sales, and IP Rights The audience asks about real-time definitions, industry pushback, sales tactics, and IP rights. Christopher Ahlberg forcefully shuts down the idea of value pricing for horizontal software, while Matt briefly interjects to ask about pricing models.0:00–5:13 · Guest teaching 0/10 Panelist Introductions and Entrepreneurial Backgrounds Matt Turck welcomes the panelists and invites them to share their entrepreneurial backgrounds. The panelists deliver lighthearted, agreeable origin stories, including Nick Walter winning money on a game show to bootstrap his company.5:13–11:02 · Guest teaching 2/10 Market Readiness and Selling Big Data Solutions Matt frames the topic around the gap between VC hype and enterprise market readiness. When Christopher Ahlberg gives a broad answer, Matt pushes back to ask specifically about selling to conservative verticals like government and legal.11:02–17:01 · Guest teaching 3/10 Technical Stacks and Processing Architectures Matt inquires about technical stacks and asks if the guests use Hadoop. The guests educate the room on why conventional big data stacks like Hadoop aren't necessary for their workloads, revealing pragmatically 'janky' setups like Excel macros alongside Python and Postgres.17:01–21:28 · Guest teaching 1/10 Audience Q&A: Identifying Needs and Sports Competition Matt opens the floor to audience Q&A and acts solely as a facilitator. Audience members question Christopher on customer problem discovery and Nick on market crowding in sports analytics.21:28–31:20 · Guest teaching 2/10 Audience Q&A: Patent NLP and Predictive Back-Testing Audience members ask technical questions regarding patent claims processing and back-testing predictive algorithms. The panel explains confidence scores, historical node calculations, and overwhelming transparency strategies without host intervention.31:20–55:31 · Guest teaching 2/10 Audience Q&A: Industry Resistance, Sales, and IP Rights The audience asks about real-time definitions, industry pushback, sales tactics, and IP rights. Christopher Ahlberg forcefully shuts down the idea of value pricing for horizontal software, while Matt briefly interjects to ask about pricing models.0:00–5:13 · Guest disagreement 0/10 Panelist Introductions and Entrepreneurial Backgrounds Matt Turck welcomes the panelists and invites them to share their entrepreneurial backgrounds. The panelists deliver lighthearted, agreeable origin stories, including Nick Walter winning money on a game show to bootstrap his company.5:13–11:02 · Guest disagreement 1/10 Market Readiness and Selling Big Data Solutions Matt frames the topic around the gap between VC hype and enterprise market readiness. When Christopher Ahlberg gives a broad answer, Matt pushes back to ask specifically about selling to conservative verticals like government and legal.11:02–17:01 · Guest disagreement 0/10 Technical Stacks and Processing Architectures Matt inquires about technical stacks and asks if the guests use Hadoop. The guests educate the room on why conventional big data stacks like Hadoop aren't necessary for their workloads, revealing pragmatically 'janky' setups like Excel macros alongside Python and Postgres.17:01–21:28 · Guest disagreement 1/10 Audience Q&A: Identifying Needs and Sports Competition Matt opens the floor to audience Q&A and acts solely as a facilitator. Audience members question Christopher on customer problem discovery and Nick on market crowding in sports analytics.21:28–31:20 · Guest disagreement 1/10 Audience Q&A: Patent NLP and Predictive Back-Testing Audience members ask technical questions regarding patent claims processing and back-testing predictive algorithms. The panel explains confidence scores, historical node calculations, and overwhelming transparency strategies without host intervention.31:20–55:31 · Guest disagreement 2/10 Audience Q&A: Industry Resistance, Sales, and IP Rights The audience asks about real-time definitions, industry pushback, sales tactics, and IP rights. Christopher Ahlberg forcefully shuts down the idea of value pricing for horizontal software, while Matt briefly interjects to ask about pricing models.0:00–5:13 · Matt pushing back 0/10 Panelist Introductions and Entrepreneurial Backgrounds Matt Turck welcomes the panelists and invites them to share their entrepreneurial backgrounds. The panelists deliver lighthearted, agreeable origin stories, including Nick Walter winning money on a game show to bootstrap his company.5:13–11:02 · Matt pushing back 2/10 Market Readiness and Selling Big Data Solutions Matt frames the topic around the gap between VC hype and enterprise market readiness. When Christopher Ahlberg gives a broad answer, Matt pushes back to ask specifically about selling to conservative verticals like government and legal.11:02–17:01 · Matt pushing back 1/10 Technical Stacks and Processing Architectures Matt inquires about technical stacks and asks if the guests use Hadoop. The guests educate the room on why conventional big data stacks like Hadoop aren't necessary for their workloads, revealing pragmatically 'janky' setups like Excel macros alongside Python and Postgres.17:01–21:28 · Matt pushing back 0/10 Audience Q&A: Identifying Needs and Sports Competition Matt opens the floor to audience Q&A and acts solely as a facilitator. Audience members question Christopher on customer problem discovery and Nick on market crowding in sports analytics.21:28–31:20 · Matt pushing back 0/10 Audience Q&A: Patent NLP and Predictive Back-Testing Audience members ask technical questions regarding patent claims processing and back-testing predictive algorithms. The panel explains confidence scores, historical node calculations, and overwhelming transparency strategies without host intervention.31:20–55:31 · Matt pushing back 1/10 Audience Q&A: Industry Resistance, Sales, and IP Rights The audience asks about real-time definitions, industry pushback, sales tactics, and IP rights. Christopher Ahlberg forcefully shuts down the idea of value pricing for horizontal software, while Matt briefly interjects to ask about pricing models.

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

0:00 · Matt 16.6% · guest 83.4%0:00 · Matt 16.6% · guest 83.4%3:00 · Matt 25.7% · guest 74.3%3:00 · Matt 25.7% · guest 74.3%6:00 · Matt 16.4% · guest 83.6%6:00 · Matt 16.4% · guest 83.6%9:00 · Matt 13.8% · guest 86.2%9:00 · Matt 13.8% · guest 86.2%12:00 · Matt 3.8% · guest 96.2%12:00 · Matt 3.8% · guest 96.2%15:00 · Matt 9.9% · guest 90.1%15:00 · Matt 9.9% · guest 90.1%18:00 · Matt 0% · guest 100%18:00 · Matt 0% · guest 100%21:00 · Matt 0.7% · guest 99.3%21:00 · Matt 0.7% · guest 99.3%24:00 · Matt 0% · guest 100%24:00 · Matt 0% · guest 100%27:00 · Matt 0% · guest 100%27:00 · Matt 0% · guest 100%30:00 · Matt 0% · guest 100%30:00 · Matt 0% · guest 100%33:00 · Matt 0% · guest 100%33:00 · Matt 0% · guest 100%36:00 · Matt 0% · guest 100%36:00 · Matt 0% · guest 100%39:00 · Matt 0% · guest 100%39:00 · Matt 0% · guest 100%42:00 · Matt 0% · guest 100%42:00 · Matt 0% · guest 100%45:00 · Matt 0% · guest 100%45:00 · Matt 0% · guest 100%48:00 · Matt 2.4% · guest 97.6%48:00 · Matt 2.4% · guest 97.6%51:00 · Matt 3.1% · guest 96.9%51:00 · Matt 3.1% · guest 96.9%54:00 · Matt 0.2% · guest 99.8%54:00 · Matt 0.2% · guest 99.8%57:00 · Matt 1% · guest 99%57:00 · Matt 1% · guest 99%1:00:00 · Matt 100% · guest 0%1:00:00 · Matt 100% · guest 0%
Sharpest disagreement ▶ 50:46 Dismissal of Value Pricing for Horizontal Tools

Christopher Ahlberg forcefully rejects the premise of attempting value-based pricing for horizontal analytics software, telling the audience to not even try starting because it is a complete waste of time.

 Hardest push from Matt ▶ 6:38 Host Redirects from Hype to Sales Readiness

Matt Turck interrupts Christopher Ahlberg's broad answer about database funding to steer the conversation back to customer readiness and the realities of selling forward-thinking software to governments.

 Biggest teaching moment ▶ 11:27 Dispelling the Hadoop Big Data Myth

Hjalmar Gislason breaks down why Hadoop and NoSQL database setups fail for aggregate quantitative analysis, explaining why traditional relational databases like Postgres are superior for their architecture.

 Matt holds his own ▶ 5:13 Framing Big Data VC Gap

Matt Turck demonstrates strong industry context by highlighting the disconnect between venture capital excitement around big data startups and the sluggish adoption rate among enterprise customers.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Panelist Introductions and Entrepreneurial Backgrounds 1000 Matt Turck welcomes the panelists and invites them to share their entrepreneurial backgrounds. The panelists deliver lighthearted, agreeable origin stories, including Nick Walter winning money on a game show to bootstrap his company.
Market Readiness and Selling Big Data Solutions 3212 Matt frames the topic around the gap between VC hype and enterprise market readiness. When Christopher Ahlberg gives a broad answer, Matt pushes back to ask specifically about selling to conservative verticals like government and legal.
Technical Stacks and Processing Architectures 3301 Matt inquires about technical stacks and asks if the guests use Hadoop. The guests educate the room on why conventional big data stacks like Hadoop aren't necessary for their workloads, revealing pragmatically 'janky' setups like Excel macros alongside Python and Postgres.
Audience Q&A: Identifying Needs and Sports Competition 0110 Matt opens the floor to audience Q&A and acts solely as a facilitator. Audience members question Christopher on customer problem discovery and Nick on market crowding in sports analytics.
Audience Q&A: Patent NLP and Predictive Back-Testing 0210 Audience members ask technical questions regarding patent claims processing and back-testing predictive algorithms. The panel explains confidence scores, historical node calculations, and overwhelming transparency strategies without host intervention.
Audience Q&A: Industry Resistance, Sales, and IP Rights 1221 The audience asks about real-time definitions, industry pushback, sales tactics, and IP rights. Christopher Ahlberg forcefully shuts down the idea of value pricing for horizontal software, while Matt briefly interjects to ask about pricing models.

Statements from this episode (12)

Disclosure
Nick Walter funded numberFire with a $100,000 game show win
“I was the one who wants to be a millionaire with, ah, Regis Philbin about two and a half years ago. I ended up winning a 100,000 dollars on the show, and I promptly quit my job and, ah, did Numberfire.”
Nick Walter Dec 5, 2013 ▶ 2:19
Insight
Gislason: Data startups must sell solutions, not big data itself
“Nobody's selling big data solutions. We're selling solutions that use big data kind of technologies, and you have to frame that differently for kind of, you know, depending on who you're talking to.”
Hjalmar Gislason (Halmar) Dec 5, 2013 ▶ 10:05
Insight
Gislason: Hadoop and NoSQL are poor for quantitative data aggregation
“Actually we found that Hadoop and most, kind of, no, no SQL solutions are not very good for, kind of, quantitative data when you need to aggregate and, kind of, go across these things.”
Hjalmar Gislason (Halmar) Dec 5, 2013 ▶ 11:31
Disclosure
numberFire handles part of its predictive data modeling using Excel macros
“It's done on one hand, a lot of Python into R on one hand. Another part of it is actually done in Excel in a series of macros done by our chief analyst.”
Nick Walter Dec 5, 2013 ▶ 13:46
Assertion Not checkable as stated
Recorded Future queries billions of cloud rows in under 500 milliseconds
“We can now do sub seconds, sub 500 milliseconds to five, six billions of rows of data in the cloud, which ends up being pretty cool”
Christopher Ahlberg Dec 5, 2013 ▶ 16:31
Assertion Not checkable as stated
Lex Machina pays 10 cents per page for government court documents
“Because we basically call a government database and we pay the same 10 cents per page to download a document that anyone does.”
Josh Becker Dec 5, 2013 ▶ 22:36
Insight
Gislason: Research organizations buy data tools for revenue, not labor savings
“We've found it's impossible to sell this to research organizations on the merits that it will save work internally. It, you know, we have to make the argument that it will bring in more revenue”
Hjalmar Gislason (Halmar) Dec 5, 2013 ▶ 40:53
Insight
DataMarket failed at freemium but succeeded with high-priced direct enterprise sales
“We had a hard time giving our solution away, but then it turns out we can sell it for a lot of money if we sit down with the right people.”
Hjalmar Gislason (Halmar) Dec 5, 2013 ▶ 44:08
Insight
Ahlberg: Value-based pricing for horizontal big data tools is a waste
“In, to do horizontal Analytical tools. Horizontal big data tools. And actually be able to do value pricing in various segments. You know, don't even try starting. It's a complete utter waste of time.”
Christopher Ahlberg Dec 5, 2013 ▶ 50:35
Prediction Didn’t hold up
DataMarket will adopt D3.js once IE7 and IE8 market share drops
“For the technical guys in the room, moving from Protovis, which, what, which is what we've built our stuff on, and made compatible to Internet Explorer seven and eight, which is about 20% of the business traffic on the web still, ah, to D three in probably a c…”
Hjalmar Gislason (Halmar) Dec 5, 2013 ▶ 56:18
Prediction Not checkable as stated
Walter: RFID sensors will track players in 3D within 3-4 years
“One of the biggest sort of, like, recent trends in sports is just capturing as much data as possible through RFID sensors and things like that. So you can imagine a future of maybe three or four years from now where each player, the ball itself, is, is connect…”
Nick Walter Dec 5, 2013 ▶ 56:46
Disclosure
Recorded Future migrated to D3.js and dropped support for pre-IE9 browsers
“We did do the D three move here, as we hear, and just said screw it, and, you know, IE nine, or, you know, just nothing older than that, and that was a big sort of gutsy move to make, and to say we're going to run with that.”
Christopher Ahlberg Dec 5, 2013 ▶ 59:23
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