Love this and it's a super important take right now. I'm completely onboard with "less numbers" and FWIW I believe there's a real case to be made here:
Exact numbers are the way to optimize machine decisions in ML. As you point out, human decisions aren't number-based. I'd wager they're story-based (good gossip is a good story). To survive / thrive, analytics has to become the story, meaning-making apparatus. Less dashboards, more engaging content. A clear narrative is more important than precise numbers.
This is the path to deliver on our promises without magic or messiah. Good stories create alignment, clarity, confidence. Let go of the pretense of empiricism and take responsibility for qualitative data (customer interviews) for good measure.
1. Similar to your point around the bet imagine you take analytics engineers and make them CEOs. How would they organize the company? Would they keep them the same way or do something different? How different?
2. A while back I listened to this podcast around data at Ramp (https://roundup.getdbt.com/p/ep-47-ramps-8-billion-data-strategy) and it resonated. The big point is to embed data into product teams (Product + Engineering + Design -> Product + Engineering + Design + Data) where you're thinking of the data you're capturing, how you're going to capture, what you're going to do with it, etc at the start of the project.
Both of these are getting to the idea that data/analytics is too far downstream and are often too reactive. There's value in the data and the analysis but it needs to be an actual part of the business vs a supporting function.
Very provocative. I do think data teams need to focus less on data and more on narratives, and collective sense making. That’s more about humans, and communication than tech and data.
Or to put it another way, I have seen analytics-driven decisions & companies that are systematically good at this. The type of model is also tied with elite consultancies, but also in day-to-day processes like Industrial Engineering & FP&A. It is part of the secret sauce of certain firms.
But I really don't know how to review your approach. As in, if analytics is a sham, then all of business is a game of blind-luck, suggesting that there is no "goal". There are tasks stupidly decided that win on sheer luck.
So I kind of love the idea of adversarial analytics. It's like red teaming your decision making process. If the CEO directs the data team to lie to marketing about their numbers for the month, what would the marketing team do? Do they freak out and go panic mode and it triggers a ton of politics? Or do they fall back on what they control, assess their inputs, get curious about why, and figure it out together.
This reminds me of the chaos monkey from Netflix https://netflix.github.io/chaosmonkey/ Is the ideal for any team in an organization to be resilient to bad data and analytics? That would be a good thing right? Everyone wouldn't immediately trust the charts and they'd dive in and take a breath before going crazy...
This also reminds me that a good way to cut random spend in an org is to stochastically cancel corporate credits to see who and what complains!
Thanks Benn, I always love your thoughts. In this case, and I'm just spitballing here, you could focus on creating a mature analytics function within your organization that actually makes a difference. We created Leading in Analytics to help you do just that. Go ahead and search for it. . . Scroll through the sponsored ads, of course.
THIS 👉 "Because after flirting with a life of crime, we might need to start keeping our backstory a secret, and learn to blend in with everyone else."
I have spent time as an undercover data agent in marketing, operations, logistics, product... Now that I'm spending most of my time (for the first time) leading with just the "data" play... I'm seeing the gaps more clearly.
Specifically in SaaS I would be love to see more startups start with the use-cases first. Seems obvious but most people don't seem to start there...
Love your articles, usually read them twice, something unusual in a world drowning in content.
Would the following classification make sense:
operational data work: real-time (or near real-time) automated systems based on data. Usually containing some ML component but I would include a simple counter as well. Here we see a lot of value (ads etc).
tactical data work: human-facing presentation of metrics (in e.g. dashboards or reports) in a slower cadence. Basically checking the "known unknowns". I have seen a lot of value in catching and root-causing regressions or just aligning people around KRs.
strategic data work: diving into the "unknown unknowns" by coming up with "insights" that fundamentally change the whole business model. This is hard and it is almost impossible to predict any outcome. Therefore, the value density per working hour is pretty low.
All three pillars are usually covered by the same team, mostly because they share some tooling and maybe also thinking. My understanding is that everybody wants to work on important "strategic" stuff, and therefore the third pillar is raising a lot of expectations. At some point I have been researching on any strategic "insight" stories with real impact published online but could not find much, just a lot of corporate noise.
LLMs have mastered the craft of creating SQL queries, Pandas scripts, and graphing analyses.
I work at a company where Data plays a service role. In that capacity, they’re far too distant from the problem to come up with unique angles nor ideas on what to dig into. PMs, Ops, and Revenue can now take on 90% of those responsibilities with a little additional effort.
In the near future, this skillset should be a tablestakes for any position hired in a technology company. Data will be relegated to a cost center like IT where they conduct tasks like setting up experiments, sharing reports, etc. To prevent that, BA needs to be the closest to the problems to ask the right questions.
I've been toying with the idea of an analytics sous-vide model for a bit and I wonder what you would think about it. A largely distributed model with a 3ish month rotation into the "hub" / COE so you can stay up to date on larger company context / methods and hopefully break down some of the tribalism that I feel like builds up. Maybe the scale you need to achieve for this to work makes it impossible, but I definitely agree that treating this like an independent function and not a good-to-have skill is aging poorly.
20 some odd years of working in data, and it has been only in the last several where I've extricated myself from the church of "data is everything and all things" to realise that at least 90% of everything in this world comes down to the messiness and complexity of people. (not an exact figure, but that's the point?) I think this is something that a few voices in the field, or data scene, or whatever we want to call this collective have touched upon, but doesn't get a loud enough voice. On the one hand, I think it would save ourselves a lot of trouble as there's no longer the need to quibble around ridiculous levels of precision, but on the other it's having to chip away and break free from decades of data rhetoric we've been taught to believe.
The bigger Ponzi scheme seems to be the data platforms that are making millions ( billions?) by enabling all these analytics teams, no?
Wonderful insights presented in a wonderful way, as always. I think a lot of companies centralize advanced analytics for too long. "Draining" their centers of excellence can unlock a lot of value. The International Institute for Analytics wrote about this 3 years ago: https://iianalytics.com/community/blog/aspire-to-decrease-the-size-of-your-central-analytics-org
Love this and it's a super important take right now. I'm completely onboard with "less numbers" and FWIW I believe there's a real case to be made here:
Exact numbers are the way to optimize machine decisions in ML. As you point out, human decisions aren't number-based. I'd wager they're story-based (good gossip is a good story). To survive / thrive, analytics has to become the story, meaning-making apparatus. Less dashboards, more engaging content. A clear narrative is more important than precise numbers.
This is the path to deliver on our promises without magic or messiah. Good stories create alignment, clarity, confidence. Let go of the pretense of empiricism and take responsibility for qualitative data (customer interviews) for good measure.
Well I just feel depressed now
Two thoughts while reading this:
1. Similar to your point around the bet imagine you take analytics engineers and make them CEOs. How would they organize the company? Would they keep them the same way or do something different? How different?
2. A while back I listened to this podcast around data at Ramp (https://roundup.getdbt.com/p/ep-47-ramps-8-billion-data-strategy) and it resonated. The big point is to embed data into product teams (Product + Engineering + Design -> Product + Engineering + Design + Data) where you're thinking of the data you're capturing, how you're going to capture, what you're going to do with it, etc at the start of the project.
Both of these are getting to the idea that data/analytics is too far downstream and are often too reactive. There's value in the data and the analysis but it needs to be an actual part of the business vs a supporting function.
Very provocative. I do think data teams need to focus less on data and more on narratives, and collective sense making. That’s more about humans, and communication than tech and data.
Honestly, I don't know.
Or to put it another way, I have seen analytics-driven decisions & companies that are systematically good at this. The type of model is also tied with elite consultancies, but also in day-to-day processes like Industrial Engineering & FP&A. It is part of the secret sauce of certain firms.
But I really don't know how to review your approach. As in, if analytics is a sham, then all of business is a game of blind-luck, suggesting that there is no "goal". There are tasks stupidly decided that win on sheer luck.
So I kind of love the idea of adversarial analytics. It's like red teaming your decision making process. If the CEO directs the data team to lie to marketing about their numbers for the month, what would the marketing team do? Do they freak out and go panic mode and it triggers a ton of politics? Or do they fall back on what they control, assess their inputs, get curious about why, and figure it out together.
This reminds me of the chaos monkey from Netflix https://netflix.github.io/chaosmonkey/ Is the ideal for any team in an organization to be resilient to bad data and analytics? That would be a good thing right? Everyone wouldn't immediately trust the charts and they'd dive in and take a breath before going crazy...
This also reminds me that a good way to cut random spend in an org is to stochastically cancel corporate credits to see who and what complains!
For 20 years I have thought data quality as a solved problem is right over that next hill. Rinse and repeat ♻️. Great post.
Thanks Benn, I always love your thoughts. In this case, and I'm just spitballing here, you could focus on creating a mature analytics function within your organization that actually makes a difference. We created Leading in Analytics to help you do just that. Go ahead and search for it. . . Scroll through the sponsored ads, of course.
Great writing ✍️ 🙌
THIS 👉 "Because after flirting with a life of crime, we might need to start keeping our backstory a secret, and learn to blend in with everyone else."
I have spent time as an undercover data agent in marketing, operations, logistics, product... Now that I'm spending most of my time (for the first time) leading with just the "data" play... I'm seeing the gaps more clearly.
Specifically in SaaS I would be love to see more startups start with the use-cases first. Seems obvious but most people don't seem to start there...
Love your articles, usually read them twice, something unusual in a world drowning in content.
Would the following classification make sense:
operational data work: real-time (or near real-time) automated systems based on data. Usually containing some ML component but I would include a simple counter as well. Here we see a lot of value (ads etc).
tactical data work: human-facing presentation of metrics (in e.g. dashboards or reports) in a slower cadence. Basically checking the "known unknowns". I have seen a lot of value in catching and root-causing regressions or just aligning people around KRs.
strategic data work: diving into the "unknown unknowns" by coming up with "insights" that fundamentally change the whole business model. This is hard and it is almost impossible to predict any outcome. Therefore, the value density per working hour is pretty low.
All three pillars are usually covered by the same team, mostly because they share some tooling and maybe also thinking. My understanding is that everybody wants to work on important "strategic" stuff, and therefore the third pillar is raising a lot of expectations. At some point I have been researching on any strategic "insight" stories with real impact published online but could not find much, just a lot of corporate noise.
LLMs have mastered the craft of creating SQL queries, Pandas scripts, and graphing analyses.
I work at a company where Data plays a service role. In that capacity, they’re far too distant from the problem to come up with unique angles nor ideas on what to dig into. PMs, Ops, and Revenue can now take on 90% of those responsibilities with a little additional effort.
In the near future, this skillset should be a tablestakes for any position hired in a technology company. Data will be relegated to a cost center like IT where they conduct tasks like setting up experiments, sharing reports, etc. To prevent that, BA needs to be the closest to the problems to ask the right questions.
I've been toying with the idea of an analytics sous-vide model for a bit and I wonder what you would think about it. A largely distributed model with a 3ish month rotation into the "hub" / COE so you can stay up to date on larger company context / methods and hopefully break down some of the tribalism that I feel like builds up. Maybe the scale you need to achieve for this to work makes it impossible, but I definitely agree that treating this like an independent function and not a good-to-have skill is aging poorly.
20 some odd years of working in data, and it has been only in the last several where I've extricated myself from the church of "data is everything and all things" to realise that at least 90% of everything in this world comes down to the messiness and complexity of people. (not an exact figure, but that's the point?) I think this is something that a few voices in the field, or data scene, or whatever we want to call this collective have touched upon, but doesn't get a loud enough voice. On the one hand, I think it would save ourselves a lot of trouble as there's no longer the need to quibble around ridiculous levels of precision, but on the other it's having to chip away and break free from decades of data rhetoric we've been taught to believe.