Apr 13, 2019 · 29m · a16z
The Future of Decision-Making: 3 Startup Opportunities
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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 video from Andreessen Horowitz (a16z), host Frank Chen and investor Jad Naus discuss how corporate digital transformation is shifting from legacy Business Intelligence to real-time Operational Intelligence. They analyze the technical infrastructure requirements, key startup investment opportunities, and go-to-market strategies for building software companies in traditional, non-IT industries.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. How this is scored →
The host as informed peer 5.5 Guest teaching 4.8 Guest disagreement 1.0 The host pushing back 1.3
speaking balance: gold is the host, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 14:01 Jad corrects host's assumption about target users
Jad directly counters Frank's premise about business analysts, clarifying that operational intelligence tools target actual line operational workers rather than analysts.
Hardest push from the host ▶ 17:52 Frank challenges Jad on why ETL is uniquely difficultFrank presses Jad on why the ETL layer specifically is the hardest part of the stack to transform for non-technical users.
Biggest teaching moment ▶ 23:39 Jad quizzes Frank on ExxonMobil capital metricsJad quizzes Frank on deployed capital metrics in heavy industries, using ExxonMobil's 230 billion dollars to illustrate how small efficiency gains create massive financial impact.
The host holds their own ▶ 10:05 Frank details real-time ML bake-offs and Cresta.aiFrank demonstrates deep technical domain knowledge by detailing how advanced ML teams run nightly bake-offs across multiple live models and citing real-time sales AI tool Cresta.ai.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | The host as informed peer | Guest teaching | Guest disagreement | The host pushing back | Why |
|---|---|---|---|---|---|---|
| Functional Shifts in Product Management and Growth Marketing | 5 | 4 | 1 | 1 | Frank draws on his own background as a former product manager to validate Jad's points about flying to customers and manual surveys. He also offers a creative metaphor comparing traditional marketing to Don Draper's typewriters and stories. | |
| Transition from Business Intelligence to Operational Intelligence | 6 | 5 | 1 | 2 | Frank demonstrates familiarity with traditional business intelligence infrastructure and cracks a well-known industry joke about BI reports costing ten million dollars for the wrong question. Jad explains the core definition of operational intelligence. | |
| Real-Time and Continuous Monitoring in Operational Intelligence | 8 | 4 | 1 | 1 | Frank displays high technical fluency by bringing up live multi-model machine learning bake-offs and citing real-time sales advice startup Cresta.ai. Jad builds on these technical examples to illustrate continuous monitoring. | |
| Enabling Self-Service Analytics for Non-Technical Operational Roles | 5 | 5 | 2 | 2 | Frank summarizes the shift from business intelligence to self-service operational tools. Jad gently reframes Frank's takeaway, pointing out that the target end-users are actual operational line workers rather than business analysts. | |
| Redesigning the Data Infrastructure Stack for Business Users | 6 | 6 | 1 | 2 | Frank probes Jad on how data infrastructure layers need to evolve for non-technical users and specifically presses on why ETL is the hardest layer to transform. Jad breaks down the pipeline layers and explains ETL's domain specificity. | |
| Three Major Startup Categories in Operational Intelligence | 5 | 7 | 1 | 1 | Jad outlines three operational intelligence startup categories and educates Frank on non-IT sectors with thin margins like ACS Group, Costco, and ExxonMobil. Frank accurately guesses ExxonMobil's deployed capital in the hundreds of billions. | |
| Founder Strategies and Market Realities in Non-IT Sectors | 4 | 6 | 1 | 1 | Jad explains founder realities when selling into conservative industries, emphasizing long sales cycles and educating investors. Frank synthesizes the advice into a concise takeaway on embracing professional services. | |
| Conclusion and Community Engagement | 5 | 1 | 0 | 0 | Frank closes the episode by framing real-time decision-making around the competitive threat of Amazon before delivering standard channel call-to-actions. |
Statements from this episode (14)
Naus: Digital transformation has two phases, digitization and automation
“I would bucket the things that people do in digital transformation into two, ah, areas. The first one is around moving from these manual paper processes to more, Digital ones that are easy to change, faster to modify, more agile. The second thing that people t…”
Naus: New tools automate product data collection for PMs
“What's happening now is we have a new generation of tools that actually allow the automation of data collection from the product.”
Naus: Marketing roles have evolved into technical marketing engineering
“What has happened over the past few years is the rise of this kind of marketing engineering role to a certain degree.”
Naus: Automation will force enterprise middle managers to become operational analysts
“Everybody is going to end up becoming more of an analyst in that sense in the enterprise.”
Naus: Operational intelligence requires immediate answers, unlike eventual legacy BI
“The first one is that it has to be immediate. It can't be eventual like BI. You can't just say, oh, I need to answer this question and then get an answer like three months later. It has to be answered in the moment.”
Engineering monitoring disciplines are expanding into marketing, product, and sales
“And now we're actually seeing these kinds of engineering disciplines kind of migrate into other functions of the org, right? Like marketing seems to have been the first one to go after that and then product management, and we're actually seeing now people tryi…”
Naus: ROI-based software sales will expand into non-IT operational roles
“That same kind of sale hasn't yet happened in these other orgs. It's a little harder to prove the ROI. But I think it'll get there.”
Naus: Operational intelligence software must be self-service for non-technical users
“It has to be self service, not full service. You can't have somebody else going and doing all the work for you. Those tools have to actually give you insights that are catered to you. And you have to actually be able to ask questions yourself out of these tool…”
Naus: Every layer of the data stack must become usable by non-technical workers
“I think every layer, functionally each layer is going to remain the same, like at the core, it's going to be doing the same things. But each layer is going to have new non-functional requirements. Each layer is going to have to be usable by a non-technical per…”
Naus: Airbnb created and open-sourced data presentation tool Superset
“So, Airbnb, for example, built Superset, and they luckily open sourced it to the world, and now it's used by hundreds of companies.”
Naus: ETL modernization is difficult due to domain specificity and manual integration
“I think two reasons why ETL has been so hard. The first one is it actually requires domain specificity. Like, ETL for healthcare is not going to look the same as, ah, ETL for financials. For ride sharing or whatever, like the ontologies, the things that they c…”
Naus: Traditionally non-IT industries will benefit most from operational intelligence software
“And I think that, ah, the industries that are going to win the most out of operational intelligence are going to be these kind of like, ah, traditionally non-IT buyers.”