Over most of the last decade, general-purpose tools were the cool thing to build. Rather than tracking, storing, and reporting on product usage in Mixpanel and tracking, storing, and reporting on sales metrics in Salesforce, we tried to store all our data in one centralized warehouse, and report on all of our data using a universal analytics layer. This was one of the motivating beliefs behind the modern data stack: Pivot the vertically-integrated tools sideways, and replace them with horizontal platforms.
BI, however, is more complicated. Most BI tools have two major components: first, a data model in which people define metrics, and how they should be calculated; and second, an interface where people choose which metrics they want to see and how they want to visualize them. The former thing defines the rules that govern the latter thing.
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Most BI tools are the inverse. They govern inputs, and provide no guidance on how to make sense of the outputs. And so we screw it up. We screw it up because data interpretation is hard; because the line wiggles; because most analysis is just squinting and directional vibes.
Metric trees. Metric trees are a structured set of business metrics that are intended to represent how a company actually functions. A revenue metric tree, for example, might break revenue into subcomponents like number of purchases and average revenue per purchase; the number of purchases might then get split into new customer purchases and repeat customer purchases; and so on. Interpretation is baked into metric trees: When a metric changes, walk down the tree to figure out why it changed.
Just as tools like Linear are unafraid to tell us how we should build software, BI tools could be bolder in telling us how to think about our business. Tell us how to interpret a time-series chart; force us to think about decomposing our business into its important components; give us a rigid summary table of our business that encourages us to debate what we do about the numbers on the page and now how we graph them; impose frameworks on us, and make us choose one.
Yes, this is gendered, but ten years ago (and today, but ten years ago too), tech executives were almost all men. Back then, if you were a jaded forty-something-year-old tech female executive, you were probably Sheryl Sandberg, Marissa Mayer, or Meg Whitman.
Ten years ago, if you were a jaded forty-something-year-old tech executive, you bought a Porsche and a house in Jackson. You opened a bar in Oakland, or a coffee shop in Mill Valley. You learned a lot about woodworking, Patek Philippe, and divorces. You Benjamin Buttoned yourself\u2014got hair plugs, dressed down by two generations, and met your new 28-year-old fianc\u00E9e in the middle.1
Buy some HVAC businesses. Or laundromats. Or family dental offices. \u201CTech startups are all childish hype,\u201D you say. \u201CThey\u2019re kids playing company. Did you know that car washes have a 12 percent EBITDA margin, but are valued at only 3x revenue? I\u2019m raising a $25 million SPV to roll up all the regional car washes in north Florida,2 streamline back office redundancies, optimize their digital marketing strategies, introduce a new recurring subscription service, and leverage AI.\u201D You tell your investors that you have a network of experienced industry veterans as advisors, but you know that you won\u2019t need them. You were the CRO of an enterprise infrastructure security company that sold to half of the Fortune 500; how hard can car washes be?3
Start a venture studio. \u201CI\u2019ve got a lot of great ideas, but my time is too valuable to gamble it all on just one company. Instead, I\u2019ll incubate a bunch of startups in a venture studio, figure out the best one, and then hire a young, ambitious CEO to take it over. I\u2019m more of a zero-to-one founder; my superpower is strategy and product vision. It\u2019s not that I don\u2019t want to do the work\u2014Jensen Huang and I have both been saying this for years\u2014it\u2019s just that it would be inefficient.\u201D4
Bootstrap an AI startup. \u201CDid you see Sam Altman say that somebody will soon build a one-person billion-dollar company? I wasn\u2019t going to do another startup, but AI changes everything. In my twenties, I cared too much about fundraising announcements and which VC invited me to which parties. That stuff is all a distraction. This time, I want to keep the company lean, focus on profitability from the beginning, and own as much of it as I can. I don\u2019t know what I\u2019m going to build yet, but ChatGPT and I are talking through it.\u201D5
For most people, these are the inevitable attractions. However, if you\u2019re a jaded forty-something-year-old tech executive from a data company, you might be tempted by a fourth option: Build a data product, for a specific vertical.
For you, after having spent years making operational middleware for data teams, the grass (and money) in other departments is starting to look greener. \u201CAnalytics teams are cost centers,\u201D you might say, \u201CTheir budgets get cut too quickly. We have to sell customers on our product and on the value of their data teams. Rather than making yet another BI tool, wouldn\u2019t it be easier to create an analytics application for ecommerce marketing departments? Or a sales forecasting app? I want to get back to the basics\u2014helping the actual business people who provide actual business value.\u201D
In the analytics market, this our version of the bundling and unbundling pendulum. We make generic BI tools; we make department-specific applications. When we get tired of making \u201Ccustom tools [that] help others look at very narrow, specific datasets,\u201D we make more generic BI tools; then we make department-specific applications again.
It\u2019s the circle of life in Silicon Valley: Bundle, unbundle; centralize, decentralize; pivot horizontally, pivot vertically. Build multipurpose analytics and BI platforms that are sold to data teams; build domain-specific tools that prestructure common departmental data sources and predefine popular business metrics. Do your analysis freehand; trace a ready-made template. Alternate between the two forms of BI. Though each cycle works a little better than the last, nothing fundamentally changes.
Final Cut Pro and Photoshop are the first type of tool. They make it (relatively) easy for filmmakers and artists to turn the ideas in their head into images on a screen. But Final Cut doesn\u2019t tell editors how to make those images, and Photoshop doesn\u2019t box in designers around some Adobe-imposed best practices. These tools, and tons more like them, are microphones for their users\u2019 skills\u2014they both amplify greatness, and are unforgiving of mistakes.
We use the second type of software for protection. People need to pay our taxes; TurboTax fills out their forms and makes sure they\u2019re right (powered by TurboTax\u00AE CompleteCheck\u2122!). Managers need to keep their teams aligned around a set of goals; Lattice not only helps them track those goals, but encourages that they do it in a very particular way. If you try to use Lattice to manage people using some deranged goal-setting framework instead of one they sanctioned, the tool will get in your way. And that\u2019s the point\u2014to tell us what to do, and protect us from our creative flights of fancy.
In the data market, open-ended products like Mode, Hex, and even Excel fit into the first category. These tools give analysts code editors and spreadsheets, and let people do whatever they want with them\u2014model quarterly discounted future cash flows, paint pictures, nuke the global economy, whatever. They\u2019re products for quantitative creativity.
If you\u2019re the person who\u2019s trying to answer questions\u2014like an executive who wants to know how much money you made last quarter, or a marketer who wants to see how an ad campaign is performing\u2014BI tools are designed to keep you from messing up. You can only look at a list of predefined metrics, and you can only explore those metrics in pre-approved ways. Just as Lattice is meant to keep managers from improvising how they set goals, BI tools are designed to keep people from creatively expressing what revenue means to them. There are lines, and you have to color in them.
If you\u2019re the person defining the metrics, however, you can do whatever you want. If you want to define your financial metrics in an insane way, your BI tool probably won\u2019t nudge you toward a more generally accepted definition.8 Nobody is watching the watchmen. If an analyst wants to creatively express what revenue means to them, they can.
Most domain-specific templates and data tools\u2014the stuff we\u2019re flirting with again, now\u2014transform the second \u201Ccreative\u201D experience into a more governed, \u201Cprotected\u201D experience. For example, Google Analytics defines sessions automatically, according to some unchangeable internal logic. PostHog, a product analytics tool, calculates retention cohorts based on some methodology of their choosing. SaaSGrid has \u201Call the pre-built transformations you need: Date ranges, Live vs Contracted ARR, Trailing Average, Retention Baselines, S&M:Sales Offsets, Expenses Segmentation.\u201D 152ee80cbc
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