Customer Empathy is Dead

A line in the sand is drawn. Imaginationland is on one side. Customers, the FTC, and hedge funds are on the other.

Lauren Balik
19 min readApr 2


One hundred duck-sized Census Reverse ETL flamingos could beat up one Snowflake polar bear-sized duck.

Customer empathy is the ability to see the world from the customer’s perspective. It is understanding what a person experiences when they use your products or services. It’s also understanding what customers of your competitors experience when using competitive products and services. It’s also understanding that ‘the customer’ can refer to many different personas, ranging from individual employees to managers to procurements teams to CFOs signing off on contracts. Most products and services do not have one customer at their accounts, but several, possibly many.

Put simply, customer empathy is getting over oneself and exhibiting adult behavior.

Customer empathy is key to building successful businesses, or at least it is outside the Silicon Valley world of venture capital injection after venture capital injection not only willing into existence future zombie company after zombie company, but keeping zombie companies alive long after they should have died or at least made some kind of exit.

Customer empathy is thrown out the window with too much venture capital. Founders and employees become too spoiled per their output. Categories become too crowded. Venture capital dollars create races to the bottom until someone can differentiate. ASPs fall, which is often dressed up as “PLG” or “bottoms-up” selling. In some cases, products become free just to get growth metrics going, subsidized by all the venture capital dollars.

Customers become users, with far fewer rights and far less leverage with the vendor products they use. In many cases users become fans. Some people just go on the internet and root for products in order to sound cool and to get shares and likes when they mention product vendors.

The complete lack of customer empathy is why MotherDuck will fail spectacularly. There’s no customer empathy and there will not be customer empathy. It’s possible there’s user empathy of DuckDB, the underlying technology. It’s possible there’s fan empathy of DuckDB. DuckDB has users and fans.

The lack of customer empathy was proven as soon as MotherDuck released the VC-spread “Big Data is Dead” post, which no fewer than 3 VCs emailed to me within 24 hours of its release, asking for my opinions on how BS this is.

The entire argument of cooked up phony discourse tells the tale of someone who admits to spending much of the past decade overselling data wares, burdening customers with unnecessary infrastructure and SaaS for which they pay greatly. Allegedly this person has now become reformed and sees the light and must atone for sins of overselling Big Data technology for the last decade by, you guessed it, selling a new thing.

The collection basket will be passed around right after the organist’s rendition of “Angels We Have Heard on High” and before communion. Put the money in the basket. We recommend giving 10% of your salary. Put the money in the basket. We give you a cracker and some wine, and we sing songs. Put the money in the basket. Our award-winning youth choir is traveling to St. Paul, Minnesota next month to sing “Angels We Have Heard on High” at the Midwest Regionals. Put the money in the basket.

For all you 25 year-old ‘analytics engineers’ or junior venture capitalists who may be reading this and struggling to keep up, this game is best laid out in a recent Last Week Tonight episode.

John Oliver, after eviscerating the concept of timeshares for over 13 minutes, goes deep into the next phase of the timeshare grift: Timeshare Exit Plans.

Timeshare Exit Plans like DuckDB and MotherDuck, which promise to free you from cloud bills, are even griftier than timeshares.

Watch this video (the exit plans start in around 13:40) before investing time or energy into MotherDuck, DuckDB, or anyone else pitching you a plan to get out of your cloud warehouse bills.

Of course, as a savvy businessman and absurdist comedian, John Oliver has his ending segment as comedian Rachel Dratch pitching Timeshare Exit Plan Exit Plans. These are exit plans for timeshare exit plans. This grift, like data stacks, can continue indefinitely if allowed, as long as consumers are stupid.

Let’s run through a scenario here through the concept of the Almighty Customer Empathy.

Let’s take me, Lauren Balik, 32, of New York, New York as an example. I have degrees in economics and history from the University of Virginia, Class of 2013. I’m self-taught since college in systems engineering, I know SQL and Python and Java and all the frameworks like the back of my hand, and I have used these in professional settings for a decade. I know the 3 major cloud platforms well, I’ve used all of the major BI tools, visual tools and libraries, streaming, I wasted time on Hadoop and Hive and that whole debacle, I’ve busted my chops for years as an analyst and data engineer and manager of these teams. I’ve done all kinds of data work ranging from market analysis for investment firms and Fortune 1000 companies to simple e-commerce SaaS pipeline building, to hiring and firing data teams. For the last 3 years I’ve run a small consulting firm, I work 80+ hours most weeks running my business and killing it, and at this point I mostly clean up data messes and simply get things done.

Now, let’s say I take a job as a ‘Head of Data’ at a growth stage company. The company could be SaaS or infra or e-commerce or media, whatever, it doesn’t matter.

Here is what I may be inheriting. Note: this is essentially a combination of and very close to two of the many “Modern Data Stack’ customers I’ve worked with in the past few years.

What’s actually going on here is that analytics engineering headcount (mostly using dbt as ~75% of their jobs) is increasing. There is an explosion of tables and objects in the warehouse, and of dashboards, eventually more people get hired to make and manage more tables, tables, and more tables and dashboards, and these people add costs to Snowflake, making more and more tables and objects to feed increased dashboards and analyses which decay in incremental value to the business. Each incremental data head supports incremental LOB staff incrementally lower and lower in the org chart. In 2021 the Data Team was mostly working with the exec team on high level initiatives. The team generally only supported the most business critical problems. Now, two years later, they’re measuring everything, making custom and bespoke DAG nodes of CTEs in dbt, and headcount costs have more than doubled because there are too many tables and objects to manage and the data team’s tech spend has nearly doubled — all to make ‘insights’ for people lower and lower on the org chart.

Now, here is how stupid MotherDuck’s value proposition is. If MotherDuck is the antidote to my complexity and cloud warehouse bills that have grown year-over-year, I, Lauren Balik, the Head of Data, need to go to my boss who is the CEO or CTO or CFO and — here is where customer empathy comes into play—quite literally admit that the last three years were a waste and now we need to buy a new product that rhymes with the word “Motherfuck” which is not something I am going to do. Yes, I realize MotherDuck is a play on being a managed DuckDB, the mother of a duck. MotherDuck also a pun on Motherfuck, a potty word used by bad boys who need their mouths washed out with soap.

Do you know why I am not going to tell my boss that MotherDuck/Motherfuck is the solution to our problems?

I am not going to tell my boss that we need to get rid of Snowflake and use MotherDuck/Motherfuck because I am a hard working transgender person and I depend on my job for a MotherDucking salary and my MotherDucking health insurance and to put MotherDucking food on my MotherDucking table for my MotherDucking family.

I would look like a MotherDucking, Motherfucking idiot and I would risk my credibility and my job for a company named MotherDuck, with a CEO who dressed up in a duck suit to claim “Big Data is Dead” after pumping Big Data for years, who hired paid MotherDuck influencers months-to-possibly-a-year before putting a commercially available product in the market.

I am not risking my job for entitled assholes’ inability to take work seriously.

In reality, my Snowflake bills are going up because my predecessor Head of Data kept hiring analytics engineers and the team has not effectively managed their outputs. The team is just making tables and objects and stacking CTEs on CTEs via dbt. Headcount is up. Snowflake bills are up. The marginal data work goes to stakeholders lower and lower on the org chart with less and less impact.

Really, I should be firing analytics engineers and making sure to remove unnecessary complexity. I should be firing dashboards. I should be firing dbt models. I should be firing full dbt table scans and full refreshes that dbt documentation recommends. It’s possible that all this “transformation” is hardly needed and I could just use Snowflake TASKs instead of dbt. But nobody ever talks about that, at least not in the data content mill.

I should be firing Fivetran potentially, when all we mostly use it for is Postgres and Salesforce replications, and I should look into Keboola (or similar) which can give me the same thing with better SLA adherence and better uptime for $10k a year and allows me to “transform and denormalize” before dumping data into my Snowflake, which also will get rid of 25 dbt models and lower my Snowflake bills by a run rate of $30k this year.

If I fire 1 Analytics Engineer, 1 Sr Analytics Engineer, Fivetran, the Fivetran dbt models in Snowflake that merely roll up Fivetran normalized tables and cost me $30k a year, I’m looking at $185k + $200k + $75k + $30k saved, which is $490,000 a year saved. Plus, then I use Keboola and end up paying about $10k, so net I’m at $480,000 saved over a year, my dashboards are faster and we’ll reduce forward sprawl by not making more dashboards and more dbt-produced tables and views and more objects and more CTEs stacked to infinity.

This starter plan of action, my 90 day plan for success for getting the team efficient and back on track is lower overhead than moving dbt model after dbt model over to a new platform, and I don’t have to go in front of my boss and play the MotherDuck/Motherfuck embarrassment game.

MotherDuck just isn’t for me or people like me. It’s not for serious adults who work because they rely on their jobs for salary and health insurance, who need to get work done.

MotherDuck will be a Silly Toy for Silly Boys who like to play with Silly Toys and goof off on the company dime. I have to get stuff done in jobs I hold — sorry if that makes you uncomfortable, but that’s the way the world works for someone like me.

The DuckMan Cometh.

Silly Toys For Silly Boys

MotherDuck is financed, among others, by Andreessen Horowitz (large investor in Databricks in addition to dbt, Census, and other things that increase cloud warehouse billings), Amplify Partners (financed dbt, Hightouch, Datafold, has money all over data plays that increase cloud warehouse billings), and Altimeter Capital (holds over $2B in public Snowflake stock at this time of writing, Snowflake is their largest public holding, investor in Airbyte, dbt, and others that increase cloud warehouse billings).

Let me tell you about the current state that is infrastructure and DevTool and SaaS venture capital. It’s a MotherDucking mess, a farce of corruption that would make even the fattest pocket-lining, pension fund-raiding among us barf in disgust.

These MotherDucker venture capitalists will do whatever it takes to make a quick buck, no matter who gets hurt in the process of their ~2% management fees. These venture capitalists are like Tiger sharks, eyes on the sides of their heads watching one another because half of them will disappear in the next two years, constantly on the move less they die, unable to see directly in front of them so they open their jaws and intake whatever sits in their path. These beasts prey upon the locked-away inner beast in us all: the bully, the swindler, the glutton, for the venture capital beasts are Close to The Money and everyone wants to be Close to The Money, as to be Close to The Money is to be Close to God and to be Close to God is to be Close to a Single Source of Truth.

And what do data engineers and data analysts and analytics engineers get out of this ruse?

We get Airbyte.

Airbyte may be the biggest venture capital MotherDuckers of them all, the Big One, a pure content mill. It’s just one piece of content after another, with two Silly Boys running the company as post-modern digital colonialism — quite literally expecting their heavily brown and black and Eastern European user base to create incremental API connectors and maintain incremental API connectors for free while they claim a billion and a half valuation.

Now, I know what you’re thinking.

“But Lauren, isn’t Airbyte supposed to be the next big thing in data integration? Isn’t it supposed to be the answer to our problems? A venture capitalist I met on Twitter told me this. Why would a venture capitalist on Twitter lie?”

Well, buckaroo, Airbyte is a joke. Just last week they laid off over 20% of their staff, and they need to lay off more and just give the money back. You’re not a customer when you use Airbyte, you’re just a user. Possibly, you’re a fan. You’re a MotherDucking beta tester with Airbyte, that’s all you are. There is no Customer Empathy because there are no real customers, just beta testers, even the very few people paying for the product are still effectively beta testing it after 2 years.

Don’t even get me started on the performance. It’s like running this on a MotherDucking Commodore 64. Airbyte can hardly handle database replication over 100GBs.

Pull it together, lads. This isn’t rocket science.

Airbyte is such a joke that Reddit r/dataengineering users “MyDixonsCider” (say that out loud) and “ThunderCuntAU” seem to have done more due diligence on Airbyte than any venture capitalist willing to give tens of millions of dollars of other peoples’ money to Airbyte’s founders to make the twentieth-worst version of a data replication EL tool that exists on market.

Reddit users MyDixonsCider (My Dick’s Inside Her) and ThunderCuntAU seem to have done more due diligence than Airbyte’s investors Coatue Management and Altimeter Capital. Meltano sucks too — it’s also based on the same broken, out-of-date Singer project and the Meltano product is also fundamentally broken after several years and millions in funding.

Airbyte is simply yet another Silly Toy for Silly Boys. It’s not for people who have to get work done, like me or apparently Reddit users MyDixonsCider or ThunderCuntAU.

MyDixonsCider and ThunderCuntAU are likely Silly Boys, as evidenced by their sophomoric screen names, and Airbyte is even too silly for them.

Also, why is the founder of a billion dollar-plus valued company throwing barbs back and forth on Reddit when he can’t even stand by the quality of his product? Does he think his users who have used his product are dumb? Again, no customer empathy. But he arguably has no customers, just beta testers, wasting time on the balance sheets of their employers to build his product, and he still can’t deliver.

As a connector library, Airbyte’s entire ability to serve customers and generate revenue depends on whether or not their connectors work. Half-assing these by outsourcing connector development to “the community” of people who aren’t going to work for free indefinitely just shows how completely out of touch their entire premise is.

It’s been 3 years and $180M+ in venture money. There’s a roadmap for what these types of connector catalog companies should look like. Airbyte still fails.

The Actual Costs of Silly Toys: Government Regulation and Hedge Fund Short-Sellers

At the core of stupidity that is Silly Toys to shoot data around is that there are actual consequences to this delusional behavior.

We’re going up the Mekong here on out, down a murky river into the heart of darkness that is modern data engineering, a self-congratulatory package that reeks of privilege and entitlement.

This is not for the faint. All you 25 year-old analytics engineers and junior venture capitalists and data influencers who grew up in houses with 3-car garages or larger and spend their days posting Twitter content waxing philosophical about dbt, so disconnected from reality that you think you're doing something important by learning how to use SQL and a version control system to make more tables, tables, and tables to pump Snowflake stock by increasing revenue collection, well you should turn back now. Run.

When everyone is guilty, guilty of data influencing products they’ve never used to create faux social proof, guilty of demanding an extra $20k a year salary by slapping the word “engineer” onto their analytics job title, the only way out is getting caught.

We are going to look into the abyss. We are going into Reverse ETL.

In 2021, Spruce Point Capital Management, a known short-seller, released a 100+ page report on the activities of publicly traded Lightspeed Commerce, a software provider in the e-commerce and point-of-sale space.

While there is of course a thesis about market analysis (eg. Shopify and Amazon will outcompete) what is more damning is that much of the report simply rests on more severe accusations, that a number of inconsistencies exist in the company’s non-GAAP metrics they report publicly, which they use to guide investors.

This entire report is a true goldmine of farcical “analytics engineering” that went on for years since the mid-2010s and beyond the IPO.

They quite literally appear to have engineered analytics, cooking up metrics for years. They couldn’t get a handle on basic things like customer counts or GTV.

You can download the report here.

Of course, when Spruce Point publicized their reports the short was on. This led even the broader decline in tech stocks that occurred just months later.

In just a few weeks 40% of the market capitalization of Lightspeed was wiped off the face of the Earth, billions in shareholder value destroyed all because someone saw the Angel of Death that is analytics engineering for what it is, the cooking of metrics and falsification of reality to pump equity.

How did they screw up this bad for years? How did they cook their data up for years to allegedly mislead investors, their IPO bankers, and everyone else?

Recently, Reverse ETL vendor Hightouch put out a piece with their VP of Finance promoting Palantir-style “forward-deployed” strategic Finance in the data warehouse, with stories of how at Lightspeed Commerce she led initiatives to consolidate finance with dbt in Snowflake.

My goodness. The same corporate development person, then VP of Data whose work is core to the short sell is now selling Reverse ETL.

All the usual suspects are here in this piece. We find Tristan Handy of dbt Labs. We find Snowflake. By God, we find the Brooklyn Data Company, the outside consulting arm of dbt Labs.

There is an entire 100+ page short-seller report attacking every single thing this person touched from corporate acquisitions producing questionable reporting outcomes to questionable analytics engineering practices getting mixed with financials, for years before and after IPO. Billions of dollars of shareholder value were wiped out almost instantaneously. Now she is peddling Reverse ETL after a stint in venture capital.

Again, nothing about the financial operations of Lightspeed Commerce before, during, or after this initiative seems to make sense and they could not even accurately report on high level metrics like customer counts, ARPU, and GTV nor could they justify GTV or customer counts from some of the very same acquisitions this person made before she adopted Snowflake and started making Kimball models on the cloud with dbt Labs.

You can’t make this up, it’s too good.

There simply was no strategic finance. It’s just made up. It was all a sham of moving stuff to dbt Labs, then Snowflake, then laundering it through BI tools, then getting shorted by hedge funds because none of the actual data quality issues were ever solved because they were never going to be solved because cooking up metrics was more important than presenting reality to investors.

They got caught by hedge funds that profited from calling them out for all of their analytics engineering and shooting data in and out of the data warehouse and changing metrics back and forth for years.

The piece has a cute but ultimately useless case about using Google Ads and Marketo and how that saved some money. Well, that’s nice if that saved some money, but that’s an order of magnitude off the billions of dollars shareholders lost.

It’s this inability to take anything seriously at a data or technology or strategic level that is the downfall of analytics engineering. Being involved with such a negative outcome —making bad acquisitions and cooking metrics on a finance team, then cooking metrics and creating infrastructure and systems to cook more metrics and shoot data around while building a “Strategic Finance” operation that ultimately led to the destruction of over a third of the company’s market capitalization — is not something to brag about.

In fact, it’s an embarrassment and an indictment of how ridiculous all of this is, this lifestyle engineering of corporate data.

Of course, per the article and LinkedIn, this person then became a venture capitalist.

How many other “forward-deployed” Modern Data Stack teams are out there at how many publicly traded companies reporting non-GAAP metrics cooked up in the cloud data warehouse with dbt Labs and other VC-backed Modern Stack products? There are many. Do your own research, I’m not giving away all my alpha for free.

But the delusion of bad data practices as the analytics engineering lifestyle, with bad data laundered through Snowflake (et al.) doesn’t just stop with the hedge funds. Even the United States Federal Trade Commission has gotten involved.

For the past several years I’ve heard again and again that Drizly, an alcohol marketplace and delivery app, has had one of the best data teams, a Light Upon a Hill for Modern Data Teams to look toward for guidance.

  • Materialize, a real-time streaming application vendor, has case studies about Drizly, and they’ve hosted joint community events.
  • Fivetran, an EL vendor, has them featured in materials from 2020, with a Drizly data team of 9 headcount claimed in the piece.
  • Census, a Reverse ETL vendor, has materials from 2022, with a Drizly data team of 28 headcount claimed in the piece.
  • Dagster, an orchestration tool, has materials from 2021.
  • dbt Labs has gobs of materials.

It’s a plethora of venture capital-backed tools with large marketing budgets, all hungry for use cases and social proof of their glory at Drizly, full of affirmations of affections.

Surely then, Drizly must be a beacon of truth. They must know something we all don’t know, for they are clearly going absolutely ham on dbt and now have a data team of at least 28 heads, which is likely somewhere around $6M a year in fully loaded cost.

Well, the United States Federal Trade Commission thinks differently.

This one’s got everything, and it’s well worth the read.

They were spending all their time with Silly Toys and they didn’t even have basic security measures in place and they exposed customer data to crypto miners stealing their cloud compute, then hackers who sold the customer data on the dark web after they failed to address their security issues even after being notified two years earlier.

Now Drizly is in data time out, and the CEO has a walking consent order on him in perpetuity, even if he works somewhere else.

Destroy unnecessary data: Drizly is required to destroy any personal data it collected that is not necessary for it to provide products or services to consumers. It must also document and report to the Commission what data it destroyed.

Limit future data collection: Going forward, Drizly must refrain from collecting or storing personal information unless it is necessary for specific purposes outlined in a retention schedule. It must also must publicly detail on its website the information it collects and why such data collection is necessary.

Implement an information security program: Drizly is required to implement a comprehensive information security program and establish security safeguards to protect against the security incidents outlined in the complaint. This includes measures such as providing security training for its employees; designating a high-level employee to oversee the information security program; implementing controls on who can access personal data; and requiring employees to use multi-factor authentication to access databases and other assets containing consumer data.

Why MotherDuck Will Fail

MotherDuck will fail because they will not fundamentally solve any problems for serious adults and serious business use cases.

I am never going to go in front of my boss and tell my boss, or a procurements team, that we are doing to sink time, effort, and money into a company that is a “Motherfuck” pun.

I am an adult and I will not carry water for this delusion. Most women won’t. Many people of color won’t. Many Mormons won’t — so right there the Utah market is out.

They could have named this company anything, and they chose a pun for Silly Boys. I’m sure Reddit users ThunderCuntAU and My DixonsCider will be big, big fans of MotherDuck.

MotherDuck can’t be the antidote to Databricks and Snowflake overspend because it’s backed by the same financiers who have billions riding on taking Databricks public or to an acquisition and it’s backed by the same financiers who own billions in Snowflake stock. Why would they place a serious bet against their much, much, much more significant positions? They won’t. Not now, not soon, and not in the foreseeable future. So MotherDuck can’t actually take meaningful workflows away from Databricks and Snowflake.

The DuckMan cometh bearing tales of small data, with financial backing from the exact same troop of venture capital baboons running the exact same influencer-led, bottoms-up playbook as the products and paradigms the DuckMan pledgeth to replace.

You can be sure that hedge funds and the FTC will not be far behind if a commercially available product is ever released.

Don’t get fooled again.

I’ll tip my hat to the new Constitution
Take a bow for the new revolution
Smile and grin at the change all around
Pick up my guitar and play
Just like yesterday
Then I’ll get on my knees and pray
We don’t get fooled again



Lauren Balik

Owner, Upright Analytics. Data wrangler, advisor, investor. lauren [at] uprightanalytics [dot] com