Defining Data Intuition(blog.harterrt.com)
blog.harterrt.com
Defining Data Intuition
https://blog.harterrt.com/data_intuition.html
4 comments
I agree. Having the experience to spot common pitfalls or 'weak' looking stats is key. Like any craft, there is no easy way to learn this other than experience.
Whenever I'm advising rising data analysts/scientists, I tell them to understand three areas of background knowledge that will multiply their ability to pull out an insight. They'll help them connect disparate ideas together.
Stats is easy but connecting ideas is hard. That's where the real magic happens. Plus, computers have a hard time automating this, for now.
1) Product Knowledge - How well do you know the product? This is easy for a simple app, but for large enterprise apps there are many features to keep track of. If you don't know the full context of your product, how could you frame your analysis in a larger picture?
2) Stakeholder Empathy - Whether you like it or not, as a data person you're advancing a business cause/mission. This means you need to fully understand where the business has been and where it is going. The basic question is - what are your stakeholders priorities? Why do they matter?
3) Customer Empathy - Arguable the most important of them all, how well do you know the customer? I encourage data scientists to get away from the computer and in front of the customer. Hear user research calls and ask questions. Drive as a Lyft driver, deliver food [1], etc.
Unfortunately these three areas are soft skills and you won't know you've improved until you find yourself reciting a fact. Usually you'll think "well duh, because the customer thinks this." It'll seem obvious, but it is only because you went through the trenches to learn that fact.
[1] Tony Xu (DoorDash) delivering pizzas to understand product and customer - https://www.listennotes.com/podcasts/how-i-built-this/doorda...
Whenever I'm advising rising data analysts/scientists, I tell them to understand three areas of background knowledge that will multiply their ability to pull out an insight. They'll help them connect disparate ideas together.
Stats is easy but connecting ideas is hard. That's where the real magic happens. Plus, computers have a hard time automating this, for now.
1) Product Knowledge - How well do you know the product? This is easy for a simple app, but for large enterprise apps there are many features to keep track of. If you don't know the full context of your product, how could you frame your analysis in a larger picture?
2) Stakeholder Empathy - Whether you like it or not, as a data person you're advancing a business cause/mission. This means you need to fully understand where the business has been and where it is going. The basic question is - what are your stakeholders priorities? Why do they matter?
3) Customer Empathy - Arguable the most important of them all, how well do you know the customer? I encourage data scientists to get away from the computer and in front of the customer. Hear user research calls and ask questions. Drive as a Lyft driver, deliver food [1], etc.
Unfortunately these three areas are soft skills and you won't know you've improved until you find yourself reciting a fact. Usually you'll think "well duh, because the customer thinks this." It'll seem obvious, but it is only because you went through the trenches to learn that fact.
[1] Tony Xu (DoorDash) delivering pizzas to understand product and customer - https://www.listennotes.com/podcasts/how-i-built-this/doorda...
Original author here. Super excited to see this on HN!
That's a great breakdown. I hadn't put my finger on stakeholder or customer empathy before, but I agree they're critical skills.
> Unfortunately these three areas are soft skills and you won't know you've improved until you find yourself reciting a fact. Usually you'll think "well duh, because the customer thinks this." It'll seem obvious, but it is only because you went through the trenches to learn that fact.
Definitely. This reminds me of Siver's "Obvious to you, Amazing to others" (https://sive.rs/obvious).
I'm trying to spend the rest of the year documenting as much of this soft-knowledge as I can. A lot of the data science hype over-focuses on hard skills and misses these soft skills.
That's a great breakdown. I hadn't put my finger on stakeholder or customer empathy before, but I agree they're critical skills.
> Unfortunately these three areas are soft skills and you won't know you've improved until you find yourself reciting a fact. Usually you'll think "well duh, because the customer thinks this." It'll seem obvious, but it is only because you went through the trenches to learn that fact.
Definitely. This reminds me of Siver's "Obvious to you, Amazing to others" (https://sive.rs/obvious).
I'm trying to spend the rest of the year documenting as much of this soft-knowledge as I can. A lot of the data science hype over-focuses on hard skills and misses these soft skills.
Nice, the Siver's concept is elegant. If you want to chat more about the soft skills behind data I'd be happy to. My user name at gmail
Love (3). The data is only an abstracted sliver of the reality. Pursue reality first, then go to the data!
Owing to hacker news denizens contrarian and independent nature, you may have found the one way to get people to not correct you (by asking for them to :) )
lol, dang. Reverse psychology!
I think this definition of "data intuition is a resilience to misleading data and analyses" is an elegant attempt to describe a notoriously fuzzy concept. Well done to author on this one.
As a trained teacher, I believe this is one of those soft skills (or aspects of tacit knowledge) that is notoriously difficult to teach or train someone.
I often say to people that the difference between an average person and an expert is that an expert knows which shortcuts/compromises you can make that will only have a small effect on the outcome, and which shortcuts will come back and to shoot you in the foot (and thus should be avoided from the outset). I like that the author's concept of data intuition neatly covers this scenario.
One might say that the author's definition is a negative definition, inasmuch as it is the ability to identify/avoid mistakes, but I think an equally important component of data intuition is the (positive) ability to identify implicit strengths and insights within the data (or data processes) that is presented.
I believe this skill is also very closely related to the ability in identifying which paths of data/statistical/scientific exploration are more likely to produce results than others. And as other commenters have said, this in turn is related to the ability of 'second-order thinking'. That is, connecting the dots where the there is no obvious or explicit connection.
As the saying goes, "sophistication is subtle".
I often say to people that the difference between an average person and an expert is that an expert knows which shortcuts/compromises you can make that will only have a small effect on the outcome, and which shortcuts will come back and to shoot you in the foot (and thus should be avoided from the outset). I like that the author's concept of data intuition neatly covers this scenario.
One might say that the author's definition is a negative definition, inasmuch as it is the ability to identify/avoid mistakes, but I think an equally important component of data intuition is the (positive) ability to identify implicit strengths and insights within the data (or data processes) that is presented.
I believe this skill is also very closely related to the ability in identifying which paths of data/statistical/scientific exploration are more likely to produce results than others. And as other commenters have said, this in turn is related to the ability of 'second-order thinking'. That is, connecting the dots where the there is no obvious or explicit connection.
As the saying goes, "sophistication is subtle".
Better data intuition refers to a strong mapping between the two - you can sense when they diverge. Poor data intuition is an exclusive focus on the quantitative with little understanding of the underlying reality.
You don't understand the actual patterns in reality, therefore suspect data movements do not surprise you (and make you suspicious). You don't understand the actual patterns in reality, therefore you do not explore potentially fruitful avenues for data analysis.
Expanding one's data intuition is about using data to enrich one's understanding of reality, as opposed to ignoring reality in favour of the data.