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May 24, 2017 · 24m · mad

Fake News, Alternative Facts and the Enterprise // Satyen Sangani, Alation (FirstMark's Data Driven)

Satyen Sangani · 18m spoken Matt Turck · 40s spoken
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In this presentation at FirstMark's Data Driven NYC, Alation Co-Founder and CEO Satyen Sangani explores how data manipulation, technical errors, and self-service analytics generate 'alternative facts' within corporate enterprises. He demonstrates how enterprise data catalogs provide critical context, lineage, and metadata to restore organizational trust in analytics.

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 3% of the talking time here. How this is scored →

Matt as informed peer 0.6 Guest teaching 3.3 Guest disagreement 1.5 Matt pushing back 0.4
05100:0010:0020:000:54–2:59 · Matt as informed peer 0/10 Case Study: Alternative Facts and Public Narratives Satyen delivers a solo keynote presentation introducing Alation and drawing a parallel between public 'alternative facts' (e.g., inauguration attendance numbers) and enterprise data analysis. The host does not speak during this monologue segment.2:59–5:44 · Matt as informed peer 0/10 The Enterprise Alternative Facts Thesis Satyen outlines how enterprise analytics create as much noise as signal due to unvetted sources, comparing traditional curated media to modern uncurated digital proliferation. The host is not present during this presentation segment.5:44–7:46 · Matt as informed peer 0/10 Evolution of the Enterprise Data Ecosystem Satyen traces the evolution from slow, centralized BI data warehouses to rapid, uncurated self-service tools like Tableau where authors construct their own truths. The host does not participate in this monologue section.7:46–10:20 · Matt as informed peer 0/10 Manipulating Analytics: Selective Sampling and Metric Definitions Satyen demonstrates how analysts manipulate churn rates and customer counts by selecting specific time windows or formula definitions to support desired narratives. The host is absent from this presentation block.10:20–13:12 · Matt as informed peer 0/10 Technical Errors and Inadvertent Alternative Facts Satyen illustrates technical errors, join mistakes, and intentional metric gaming using the Tinder app review prompt as a case study for corporate alternative facts. The host remains off-mic during the monologue.13:12–17:54 · Matt as informed peer 5/10 Core Recommendations: Context, Reproducibility, and Data Catalogs Matt Turck steps in post-presentation to compliment the talk and ask detailed follow-ups about Alation's user experience, governance capabilities, and whether data lakes are useful or a waste of time. Satyen constructively explains how data catalogs layer over data lakes.17:54–20:41 · Matt as informed peer 0/10 Audience Q&A: Data Version Control and Blockchain Applications An audience member asks technical questions comparing Alation to Git/Pachyderm for geospatial versioning and potential blockchain integration. Satyen provides analytical comparisons while the host only facilitates.20:41–24:18 · Matt as informed peer 0/10 Audience Q&A: Defining Metrics vs. Answering Questions Audience members ask about defining metrics versus answering questions and a philosophical query on truth arbiters. Satyen frames science and rational skepticism as the solution while Matt Turck briefly manages time constraints.0:54–2:59 · Guest teaching 3/10 Case Study: Alternative Facts and Public Narratives Satyen delivers a solo keynote presentation introducing Alation and drawing a parallel between public 'alternative facts' (e.g., inauguration attendance numbers) and enterprise data analysis. The host does not speak during this monologue segment.2:59–5:44 · Guest teaching 3/10 The Enterprise Alternative Facts Thesis Satyen outlines how enterprise analytics create as much noise as signal due to unvetted sources, comparing traditional curated media to modern uncurated digital proliferation. The host is not present during this presentation segment.5:44–7:46 · Guest teaching 3/10 Evolution of the Enterprise Data Ecosystem Satyen traces the evolution from slow, centralized BI data warehouses to rapid, uncurated self-service tools like Tableau where authors construct their own truths. The host does not participate in this monologue section.7:46–10:20 · Guest teaching 4/10 Manipulating Analytics: Selective Sampling and Metric Definitions Satyen demonstrates how analysts manipulate churn rates and customer counts by selecting specific time windows or formula definitions to support desired narratives. The host is absent from this presentation block.10:20–13:12 · Guest teaching 4/10 Technical Errors and Inadvertent Alternative Facts Satyen illustrates technical errors, join mistakes, and intentional metric gaming using the Tinder app review prompt as a case study for corporate alternative facts. The host remains off-mic during the monologue.13:12–17:54 · Guest teaching 3/10 Core Recommendations: Context, Reproducibility, and Data Catalogs Matt Turck steps in post-presentation to compliment the talk and ask detailed follow-ups about Alation's user experience, governance capabilities, and whether data lakes are useful or a waste of time. Satyen constructively explains how data catalogs layer over data lakes.17:54–20:41 · Guest teaching 3/10 Audience Q&A: Data Version Control and Blockchain Applications An audience member asks technical questions comparing Alation to Git/Pachyderm for geospatial versioning and potential blockchain integration. Satyen provides analytical comparisons while the host only facilitates.20:41–24:18 · Guest teaching 3/10 Audience Q&A: Defining Metrics vs. Answering Questions Audience members ask about defining metrics versus answering questions and a philosophical query on truth arbiters. Satyen frames science and rational skepticism as the solution while Matt Turck briefly manages time constraints.0:54–2:59 · Guest disagreement 2/10 Case Study: Alternative Facts and Public Narratives Satyen delivers a solo keynote presentation introducing Alation and drawing a parallel between public 'alternative facts' (e.g., inauguration attendance numbers) and enterprise data analysis. The host does not speak during this monologue segment.2:59–5:44 · Guest disagreement 2/10 The Enterprise Alternative Facts Thesis Satyen outlines how enterprise analytics create as much noise as signal due to unvetted sources, comparing traditional curated media to modern uncurated digital proliferation. The host is not present during this presentation segment.5:44–7:46 · Guest disagreement 1/10 Evolution of the Enterprise Data Ecosystem Satyen traces the evolution from slow, centralized BI data warehouses to rapid, uncurated self-service tools like Tableau where authors construct their own truths. The host does not participate in this monologue section.7:46–10:20 · Guest disagreement 2/10 Manipulating Analytics: Selective Sampling and Metric Definitions Satyen demonstrates how analysts manipulate churn rates and customer counts by selecting specific time windows or formula definitions to support desired narratives. The host is absent from this presentation block.10:20–13:12 · Guest disagreement 2/10 Technical Errors and Inadvertent Alternative Facts Satyen illustrates technical errors, join mistakes, and intentional metric gaming using the Tinder app review prompt as a case study for corporate alternative facts. The host remains off-mic during the monologue.13:12–17:54 · Guest disagreement 1/10 Core Recommendations: Context, Reproducibility, and Data Catalogs Matt Turck steps in post-presentation to compliment the talk and ask detailed follow-ups about Alation's user experience, governance capabilities, and whether data lakes are useful or a waste of time. Satyen constructively explains how data catalogs layer over data lakes.17:54–20:41 · Guest disagreement 1/10 Audience Q&A: Data Version Control and Blockchain Applications An audience member asks technical questions comparing Alation to Git/Pachyderm for geospatial versioning and potential blockchain integration. Satyen provides analytical comparisons while the host only facilitates.20:41–24:18 · Guest disagreement 1/10 Audience Q&A: Defining Metrics vs. Answering Questions Audience members ask about defining metrics versus answering questions and a philosophical query on truth arbiters. Satyen frames science and rational skepticism as the solution while Matt Turck briefly manages time constraints.0:54–2:59 · Matt pushing back 0/10 Case Study: Alternative Facts and Public Narratives Satyen delivers a solo keynote presentation introducing Alation and drawing a parallel between public 'alternative facts' (e.g., inauguration attendance numbers) and enterprise data analysis. The host does not speak during this monologue segment.2:59–5:44 · Matt pushing back 0/10 The Enterprise Alternative Facts Thesis Satyen outlines how enterprise analytics create as much noise as signal due to unvetted sources, comparing traditional curated media to modern uncurated digital proliferation. The host is not present during this presentation segment.5:44–7:46 · Matt pushing back 0/10 Evolution of the Enterprise Data Ecosystem Satyen traces the evolution from slow, centralized BI data warehouses to rapid, uncurated self-service tools like Tableau where authors construct their own truths. The host does not participate in this monologue section.7:46–10:20 · Matt pushing back 0/10 Manipulating Analytics: Selective Sampling and Metric Definitions Satyen demonstrates how analysts manipulate churn rates and customer counts by selecting specific time windows or formula definitions to support desired narratives. The host is absent from this presentation block.10:20–13:12 · Matt pushing back 0/10 Technical Errors and Inadvertent Alternative Facts Satyen illustrates technical errors, join mistakes, and intentional metric gaming using the Tinder app review prompt as a case study for corporate alternative facts. The host remains off-mic during the monologue.13:12–17:54 · Matt pushing back 3/10 Core Recommendations: Context, Reproducibility, and Data Catalogs Matt Turck steps in post-presentation to compliment the talk and ask detailed follow-ups about Alation's user experience, governance capabilities, and whether data lakes are useful or a waste of time. Satyen constructively explains how data catalogs layer over data lakes.17:54–20:41 · Matt pushing back 0/10 Audience Q&A: Data Version Control and Blockchain Applications An audience member asks technical questions comparing Alation to Git/Pachyderm for geospatial versioning and potential blockchain integration. Satyen provides analytical comparisons while the host only facilitates.20:41–24:18 · Matt pushing back 0/10 Audience Q&A: Defining Metrics vs. Answering Questions Audience members ask about defining metrics versus answering questions and a philosophical query on truth arbiters. Satyen frames science and rational skepticism as the solution while Matt Turck briefly manages time constraints.

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

0:00 · Matt 0% · guest 100%0:00 · Matt 0% · guest 100%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 0% · guest 100%12:00 · Matt 0% · guest 100%15:00 · Matt 23% · guest 77%15:00 · Matt 23% · guest 77%18:00 · Matt 0% · guest 100%18:00 · Matt 0% · guest 100%21:00 · Matt 1.1% · guest 98.9%21:00 · Matt 1.1% · guest 98.9%24:00 · Matt 11% · guest 89%24:00 · Matt 11% · guest 89%
Sharpest disagreement ▶ 2:25 Challenging enterprise analytics investments

Satyen forcefully asserts that corporate investments in data analytics generate as much noise and complexity as genuine signal, directly challenging the audience's underlying operational assumptions.

 Hardest push from Matt ▶ 17:03 Challenging the value of data lakes

Matt Turck explicitly asks Satyen if data lakes are a waste of time given Alation's ability to crawl data wherever it sits.

 Biggest teaching moment ▶ 17:16 Explaining the limitations of file systems in data lakes

Satyen educates the host on why file systems in data lakes fail to provide organizational context for consumers, explaining how data catalogs solve the 'pulling fish out of the lake' dilemma.

 Matt holds his own ▶ 16:24 Synthesizing product value proposition

Matt Turck demonstrates domain expertise by succinctly framing Alation's dual role in combining governance control with recommendation-based data leverage.
the scores for every segment, with the reasoning behind each
ChapterTopicMatt as informed peerGuest teachingGuest disagreementMatt pushing backWhy
Case Study: Alternative Facts and Public Narratives 0320 Satyen delivers a solo keynote presentation introducing Alation and drawing a parallel between public 'alternative facts' (e.g., inauguration attendance numbers) and enterprise data analysis. The host does not speak during this monologue segment.
The Enterprise Alternative Facts Thesis 0320 Satyen outlines how enterprise analytics create as much noise as signal due to unvetted sources, comparing traditional curated media to modern uncurated digital proliferation. The host is not present during this presentation segment.
Evolution of the Enterprise Data Ecosystem 0310 Satyen traces the evolution from slow, centralized BI data warehouses to rapid, uncurated self-service tools like Tableau where authors construct their own truths. The host does not participate in this monologue section.
Manipulating Analytics: Selective Sampling and Metric Definitions 0420 Satyen demonstrates how analysts manipulate churn rates and customer counts by selecting specific time windows or formula definitions to support desired narratives. The host is absent from this presentation block.
Technical Errors and Inadvertent Alternative Facts 0420 Satyen illustrates technical errors, join mistakes, and intentional metric gaming using the Tinder app review prompt as a case study for corporate alternative facts. The host remains off-mic during the monologue.
Core Recommendations: Context, Reproducibility, and Data Catalogs 5313 Matt Turck steps in post-presentation to compliment the talk and ask detailed follow-ups about Alation's user experience, governance capabilities, and whether data lakes are useful or a waste of time. Satyen constructively explains how data catalogs layer over data lakes.
Audience Q&A: Data Version Control and Blockchain Applications 0310 An audience member asks technical questions comparing Alation to Git/Pachyderm for geospatial versioning and potential blockchain integration. Satyen provides analytical comparisons while the host only facilitates.
Audience Q&A: Defining Metrics vs. Answering Questions 0310 Audience members ask about defining metrics versus answering questions and a philosophical query on truth arbiters. Satyen frames science and rational skepticism as the solution while Matt Turck briefly manages time constraints.

Statements from this episode (8)

Insight
Controlling data allows people to construct whatever narrative they want
“You control the data, and you build a narrative in a way that supports the claim that you want to make.”
Satyen Sangani May 24, 2017 ▶ 2:54
Opinion
Enterprise data analytics investments create as much noise as signal
“My contention to you in this audience is that the investment in data analytics that every single person in this room is making for the companies that you are making is building as much noise as signal.”
Satyen Sangani May 24, 2017 ▶ 3:00
Opinion
Enterprises suffer from internal fake news and alternative facts
“You have an alternative facts problem that exists inside of the enterprise with as much fake news, with as much lack of understanding, as you do in the public domain.”
Satyen Sangani May 24, 2017 ▶ 3:37
Insight
Information overload drives confirmation bias because parsing data is too painful
“You look for the information that supports the claim that you want to make, because parsing through all the information is just too much of a pain.”
Satyen Sangani May 24, 2017 ▶ 5:31
Assertion Not checkable as stated
Legacy data warehouse projects historically took up to two years
“To stand up a business object's universe and cube in a Teradata warehouse, or a Netezza warehouse, or whatever warehouse could take anything on the order of two years, right?”
Satyen Sangani May 24, 2017 ▶ 6:17
Insight
Measuring churn against contracts up for renewal is most accurate
“Churned revenues are the revenues over the things that were up for renewal. Now, in theory, that's the most accurate claim. That's the most accurate metric, because if something wasn't for renewal, then you can't really say that it churned, right?”
Satyen Sangani May 24, 2017 ▶ 9:31
Opinion
Organizations must require analytical labeling to clarify data context
“We've got to kind of figure out analytical labeling, right? That it's, that's not accept enough, good enough for us to kind of accept information that's proffered to us without understanding how that information was produced, where that information came from, …”
Satyen Sangani May 24, 2017 ▶ 13:24
Opinion
The tech industry is far from using blockchain for enterprise data provenance
“I think we're far away from that because most of the tools have very different ways of manipulating data and very different ways of speaking to data.”
Satyen Sangani May 24, 2017 ▶ 20:18
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