Get Out of the Building!

Producer: (00:01) Hello and welcome to Data Radicals. In today's episode, Satyen sits down with Tricia Wang. Tricia is a pioneer in thick data, which captures what numbers cannot. Her TED Talk, The Human Insights Missing from Big Data, has been viewed nearly two million times. Today, she helps major companies like Netflix learn from thick data to improve their business. In this episode, Tricia reveals the power of thick data, the value of digital personhood, and why quantification bias is dangerous to growth.

Producer: (00:34) This podcast is brought to you by Alation. The role of chief data officer (CDO) is more vital and challenging than ever before. Alation offers a vision for building a strong data culture that empowers people to find, use, and trust data. Download The CDO's Toolbox: Seven Tips for Building a Successful and Sustainable Data Culture, a white paper available at alation.com/cdo-tools.

Satyen Sangani: (01:12) Tricia Wang is a technology ethnographer, obsessed with designing equity into systems. A data geek, designer, and community organizer, she believes technology must serve humanity. As the co-founder of consultancy Sudden Compass, Tricia has helped companies like Netflix, Spotify, and Google to embrace qualitative data for business growth. Tricia, welcome to Data Radicals.

Tricia Wang: (01:32) Hello! So excited to be on.

What’s a tech ethnographer?

Satyen Sangani: (01:35) You have a pretty unique title with this notion of being a tech ethnographer, and that's not something that I've ever heard anybody call themselves. Can you describe what that is and how that came to exist?

Tricia Wang: (01:46) Yes. I made it up completely, so that's why you've never heard of it, because there's only one tech ethnographer, even though I wish people would take the job title. But I made this up, because when I finished grad school, even though I had been working in the industry and in research labs with engineers and with people who are in charge of data, I couldn't find a job. And I know I was a bit naïve, but I had this idea that as a sociologist I'm trained in quant and qual, I've worked in industry in the private sector, so I really wanted to head up the department that would be in charge of all data, qualitative and quantitative.

And it was naïve, because what I really found out was that inside companies, these departments are highly siloed and data is very politicized, and you have a department that is in charge of data, but it's actually quantitative data.

So chief data officers are not in charge of all data. It is only data that has been quantified. And I was like, well, that's ridiculous, because quantified data only tells you half the story, and especially if businesses want to grow and they want to be ready for the unknown, you always want to be prepared for the thing that your competitor doesn't know about. So you need to be ahead of the trends and ahead of any things that are actually already showing up on the market. So you really want to understand the un-quantified data, the ethnographically derived data.

Tricia Wang: (03:00) So ethnography is literally just the science of observing and studying people, of society. And so I was like, “I want a job that's in charge of all of it, the qualitative and the quantitative, and surely I can find a business leader or a set of executives or boardroom leaders who will give me this job — to oversee all data.”

And, wow, was I really naïve! I could not convince anyone to give me that job. Everyone was like, "You can be the head of qualitative data, of qualitative research." And I was like, "Why would I want that? I would only then have half of the information. I want to be able to, in an agile way, work with qualitative and quantitative data very quickly to tell the whole story." And people just laughed me out.

I had recruiters talking to me, and they were honest. They were like, "You will never find that job." And so then I was like, you know what, I guess I'm jobless. I'll just start writing about what I'm seeing, and I'll start becoming more public about the perils of only focusing on numbers. And, mind you, this was in the mid-2010s, so big data had just become a thing and people were obsessed with big data. So it's like I was that weird person who was seen as a data hater, but I wasn't.

Tricia Wang: (04:11) I was just like, I'm a sociologist. First and foremost, I'm trained in numbers and also deriving stories. They are not... Data that's not quantified. So why wouldn't you have both? So I just started writing about what I experienced at Nokia, and I was like, “What do I call myself?” — because you need to call yourself something in this world; otherwise, people don't recognize your skills. So I was like, I'm an ethnographer, I study people. But also I study what people are doing with technology, and I do this globally. So I kind of just made up the title and somehow it stuck. I don't know why, because no one can say the word “ethnographer.” And I even get tongue-twisted at times, but somehow the title stuck and I can't even get rid of it now. So now I'm like, okay, whatever, people will call me a global tech ethnographer. It is what I do, and it makes people ask me what it is and what my point of view is on it. So that's how I got the title — I couldn't find a job.

Satyen Sangani: (05:01) Yeah, and I think it's by having a unique title, everybody asks you what you do, and it's a way of catching people's interest and curiosity, which is pretty cool.

Tricia Wang: (05:08) Yeah, exactly.

Satyen Sangani: (05:09) But put simply, it's the study of people and how they use technology.

Tricia Wang: (05:13) Yeah. Exactly, quite simple.

Satyen Sangani: (05:15) What's funny to me about how you described your work is that it sounds a lot like entrepreneurship. If you think about an entrepreneur who's building a company — or even my own experience in building a company — a lot of what you identify is pre-macro trend. There's something small going on that a couple of people are doing...

Tricia Wang: (05:34) Exactly.

Satyen Sangani: (05:35) And you discover it, you find it, and you have stories that you can tie yourself to. But if it was measurable, then somebody else would already be doing it. So you don't really do that, you're focused on this much smaller, bleeding-edge cohort that ultimately becomes something larger. And so my guess is that a lot of this work that in the Valley we call customer discovery — there's a guy named Steve Blank that talks a lot about that — is very similar to what you do. And you're really at the cutting edge of trying to identify trends that maybe people aren't always seeing in the numbers, which is super cool. Has that parallel ever come to you, or is that something that anybody's ever brought up?

Tricia Wang: (06:10) Yeah, and I'm in the area of customer trend discovery; that's one way to refer to what the formal field is in the private sector. But the trend discovery work is often seen as R&D or a bit far from the product or far from the business, and the reason why I think it's really critical that this kind of work cannot be seen as separate from the strategy of the company. So you actually need a direct line to the chief growth officer or the CEO or the boardroom, and oftentimes these roles are relegated to some junior marketing role, some analyst role, where you have people who aren't really connected to saying, "Hey, this is what we've learned. How do we correlate this and scale these insights with quantitative data?"

So that was a skill set that we, Sudden Compass, my firm, has been teaching, working with chief data officers and CTOs and CIOs, and chief marketing officers; depending on who brings us in, we say this kind of skill set of understanding the full picture, which means you have to be able to go back and forth between the quant and the qual and then you have to be able to ladder that all up to strategy.

Tricia Wang: (07:15) And then execution, that's tons of organizational work that you have to do, and a lot of politicking, because you have to be able to map the organization, understand all the politics, all the people's fears. And you're really pushing the boundaries, because you're saying, “It's not business as usual.” So it's an incredibly hard job. So I would say it's partially the definition, but I will say customer discovery work is often not seen as strategic and is more like, "Oh, that's nice to have, but not critical to the business or strategic enough to be in conversation with actual strategy."

Satyen Sangani: (07:47) To make it tangible, can you give some examples about some projects that sort of are doing this kind of work, so people can understand what you mean by mixing quant and qual and where it leads to discovery.

Helping Spotify grow with qualitative insights

Tricia Wang: (07:57) Yeah, a really great example is, let's just say the Data Radicals podcast. You are all on Spotify now. Well, Spotify was one of our clients, and if you recall when Spotify first launched, it was not into podcasts at all. It was a music platform, music discovery platform, music streaming platform. And then it moved into music discovery, helping you discover other artists; they would use their machine learning to help you create personalized recommendations.

But one of the things that they had asked us to do is, they said, "We really need to understand what's coming next. What area should Spotify grow into, and how are we at risk of being disrupted?" And one of the key things that we did when we worked with their executives, is that we said, "Let's really understand what are people's relationship to music." And in an agile way, we actually said, "That is not even the question to ask." We worked with their machine learning team — they have some of the best machine learning — and we said, "Look, let's understand how people click through their music, and let's actually understand all the behaviors. But at the same time, let's talk to people, let's live with people, let's understand how people integrate music into their lives."

Tricia Wang: (09:06) And one of our first insights to them, a week into the work, was we were like, "Listen, we don't even think music is what you should be focusing on for Spotify. You really need to look at what's taking up all of the audio time and visual time. It's really about the attention economy. What is capturing people's attention? And then seeing Spotify within that attention economy." And so we worked with them to map out everyone's attention and spread of attention between Netflix — from all the platforms, right? — Netflix, Amazon, Hulu, to podcasts. And we actually said, "Your biggest competitor is all of these other things that are not music." Because the more options that people have, the less time they have for music, the less time they come back to the music.

Tricia Wang: (09:51) And what we helped them understand was that to move into a whole new vertical, which is that they were like, "Oh, maybe we're not just a music streaming platform. Maybe we are a platform to help people discover all other kinds of audio experiences. It's not just music artists." So they actually redefined the business that they're in, which is they are not just a music streaming platform, but they're actually there to help you discover all kinds of audio experiences. And that insight was something that came from our research.

Already there were hunches by executives that we should maybe go into other directions, but they didn't really have a strong angle on that, but our research helped with that, and then they did the quantitative studies to really work with that, and then we did experiments and worked with their data science team. So all of these things we had to correlate back and forth. We had to work closely. We were the bridge between the data science team and also the market research team that really did that. The goal was really to elevate the role of the market researchers so that they could have the skills and be seen as the partners of the machine learning team.

Satyen Sangani: (10:50) Yeah. It's really interesting, because it's sort of the micro of a macro term that we identified on a former podcast. So we had a guy named Paul Leonardi, who is a professor at Santa Barbara, and he basically talked a lot about how technologies are often not used in the way the makers intend, and so only 20% of the time is the tech actually used in the way that people had actually intended for it to be built. And so it's super interesting to get these profound observations at this massive level of scale for products like Spotify, where you'd think they'd known exactly what was going on with their platform.

So you mentioned you're a sociologist, and you mentioned that you sort of made up this title for yourself; how did you then turn this into consultancy or a business? What was the evolution to making this something that other people would recognize?

What’s a tech ethnographer?

Tricia Wang: (11:35) So I have a really weird background, in that I've never tried to have a career path, and it's really odd. It looks like I had a career path when you read my bio. For some people, it's like, "Oh, you worked with so many organizations." But first and foremost, I started out, in my early days in New York, as a filmmaker. I used to work for NASA, for Sally Ride, and I used to also be a community organizer working in low-income communities, making sure people had access to media and technology.

So my work is always about building equity, but I was led into the corporate world because I realized that that is where many decisions are made and they affect great numbers of people; that corporations, what they do well is they scale. And I took all my storytelling abilities from being a filmmaker and learned how to piece together stories and tell stories about science, because that's what NASA is, like some of the best scientists in the world. And I realized that that was an area where, if society is obsessed with data, and data is going to structure so much of our lives and determine many of the ways that we are human, that I have to work with the private sector.

Tricia Wang: (12:40) And, eventually, even though I couldn't get a job internally, I had many people email me, reach out to me, from Fortune 500 companies, just saying, "Hey, we can't give you a job internally, because there's no way you're just gonna all of a sudden be in charge of this team that doesn't even exist, but could you help us get these teams to talk better to each other inside the organization?"

The ask would come in two forms; either it was like, "Could you help us elevate qual, because I really think that's the missing thing to help us grow our business, and we don't know how to get these new insights, or business is stale, or we're not seeing the growth we want." And it would be a champion who would be like, "I know, I've seen it time and time again, that every time our business has had massive levels of growth, when our executives or some team has some kind of qualitative on-the-ground insight and then is able to get the whole team to execute." So they'd be like, "We need your help, because we've gone too far to the quant side, where we only are making decisions on quant and we're completely paralyzed."

Or it would be coming from, the other ask would say, "Well, could you actually help our quantitative team?" They wouldn't even think qual was part of the answer, they would just be like, "Oh, we have a group of data scientists."

Tricia Wang: (13:44) And it was a very forward-looking chief data scientist who would say, "I wanna make sure I'm really proving the value of our work to the business." And they knew they had a timeline. They were like, "Right now I've got a budget, I've only got a couple of years, though, to really demonstrate the value of my team, our headcount, our budget, and I want it to grow. I wanna make sure we're useful to the business. And you talked about that data can help business grow, and you talked about this thing called qualitative ethnography. Just come over and help us elevate the value of our work to the rest of the business, help us integrate and help us show that we can be of service."

And so depending on who brings us in, whether it's the chief data officer, the tech side, versus the chief marketing officer oftentimes or maybe just the CEO, we would find ways to essentially be that bridge in the company. And so eventually I just had so many clients that I was like, “I guess we have a consulting firm.” It was like a total accident all because I couldn't get a job. And there weren't leaders like you then; it was a field that was just beginning and the big data wave, people were obsessed.

Tricia Wang: (14:42) And so, I think now, if I were going on to the job market, because I think I've created the world, I think I have been an influential role in this space of helping chief data officers in the field of data, that at least a portion of people I've been able to influence to understand importance of qualitative. And that's why I invented the word “thick data,” because I was like, I have to communicate, I have to rebrand ethnography and qualitative data, and like no one thinks it's sexy. Everyone thinks big data is the sexiest thing. So I have to rebrand it and figure out a way to make it look just as important as big data. And so I've been one of the contributors to this world, and I think I would have a much easier time now if I were on the job market looking to do what I wanna do.

Satyen Sangani: (15:19) Yeah. And you called this kind of qualitative data “thick data” in a TED talk that you delivered. Tell us a little bit more about that concept. And what is thick data and how does it work?

What is thick data?

Tricia Wang: (15:29) Thick data is essentially any form of data that has not been quantified or is difficult to quantify. So oftentimes the things that we see in big data, any forms of numbers, has always started out as a human phenomenon, as a social phenomenon, so it first emerges as a thing that has yet to be quantified, and then humans figure out ways to capture that as numbers. So it's like a smile is not quantified, but using measurements and pixels and everything, you can be like, “Oh, the lips went out this much so we can quantify that,” and then we can measure that, the curve of the face, and so using facial recognition, and then somehow someone makes an interpretation to say, “That is a smile and therefore a smile means happiness,” or whatever.

Tricia Wang: (16:13) However, you don't know that that smile means happiness, because maybe someone is smiling because they're anxious, maybe because they are in a fearful position. Like if you are a Black person or a darker-skinned person and a cop has stopped you and you're smiling to get them to try and create safety and make sure that they don't perceive you as dangerous. So there's all these assumptions that we make with measurement that are not true, right? But we had to quantify that. So all thick data is, is the stuff that has yet to be quantified or is difficult to quantify, and that you need the human experience, a social interaction, to observe that. Think about your childhood, imagine capturing your childhood as a spreadsheet, as a database. If you had a pretty great childhood, if you had parents that hugged you or told you that you were valuable or loved and you did something important.

Tricia Wang: (16:57) You can count all the times that you slept at a home with actual shelter. But that doesn't really capture your childhood, right? That's not the full experience. It captures a part of the story. So I always tell people to try and put your childhood in a spreadsheet versus then tell me about your childhood. Both experiences, both representations of your childhood, are valid. I'm not saying the spreadsheet version is not valid, but that tells a part of the story, and the qualitative — like you telling me about your childhood — those are your memories, they may not be accurate, but that was how you perceive it, that's also one representation. So imagine if you put them both together. I don't know how many people wanna quantify their childhood, maybe they can now, but in a business setting, I'm saying that you need to have both. You have to have the quantification of your customers or whatever the product is you're selling, the interactions, but you need to always be open to the unknown, and those are things that have not yet been quantified. And then once you surface the unknown in the form of thick data, and if you want to validate it at scale, if you're like, Well, 6 people told us this, or I observed 1 person doing this, but I wonder if there's more people.

Tricia Wang: (18:02) You may want to launch a bigger study, and then eventually you may want to collect more quantitative data, or it may make you look at your existing data, your quantitative data in a whole new light. It may enable you to ask new questions or realize, "Oh, we have a product that we have yet to even develop that our customers are asking for."

Satyen Sangani: (18:18) So where do you start a project with the intent to gather thick data? If you have obviously all of this quantitative richness, you're measuring things, and now you're going into a world where you're actually trying to get the unmeasured, almost definitionally. How does one start this process? Give us a little bit of a sense of the texture of the work. When you start a project, how do you know what your domain is? How do you start to develop a set of questions? Who do you go to? How do you choose that?

Tricia Wang: (18:43) This is the question that people ask me all the time, just like, "How do I start?" And I'm like, "You always start where the pain is in the business," because you should never start a thick data project just for the fun of it, unless you have tons of time and you have some kind of lab, but usually most businesses, there’s stuff to be done, there are OKRs, there are objectives and people are busy, and so I always say, if you really want to make sure that you're not missing anything, and I say thick data is always needed, but where is the pain of the business?

How P&G used thick data to enter a new market

Tricia Wang: (19:11) And so, as an example, one of the stories I always tell is with Procter & Gamble, when I started working with them, their business was doing great, they had no pain, but someone smart enough reached out to me and said, "We're going to be launching one of our products into an urban area, and I know we don't know anything about the urban area because our products are sold and bought in suburban areas and homes that are humongous and with multi-rooms and big families and big cars and garages and stuff.”

Tricia Wang: (19:41) So thinking about Procter & Gamble, where they are a consumer product goods company — they make Tide, they make cleaning products — but they're in the consumer products space, so that was the specific business question they had and they said, "Look, we can collect all the 1st-party and 3rd-party data we want about urban areas. We've hired all the consulting firms, the big 4, to do an analysis on our go-to-market, how we should enter, and what's the first market," and all the data to justify them entering into a new market, to justify that budget.

Tricia Wang: (20:10) But they had said, "I'm really worried that we literally just don't even know who the customer is. Who is the urban customer? And here we have all these assumptions." And so I was like, "Yeah, then that's where the pain was at.” Every business is different, but it really has to be: Start with a question. And it usually has something to do with growth, so it's like if you wanna grow more, if you're looking for the way to grow, then start there. And if at that point they’re like, “Our main agency of record is about to launch a campaign in urban areas, they're telling us to do this thing. But we wanna confirm that their campaign is going to really help us sell the product and enter the market in the right way.”

Tricia Wang: (20:48) And what was crazy is that they could have just trusted the agency, they could have just said, the agency knows what they're doing right, this is their agency of record that's always worked with them and made them successful, but they were like, "We just want to double check and we wanna feel confident."

So I was like, "You know what, this is such a big entrance, this is such a big new thing you're doing, why don't we get all your executives of this product, why don't you all just travel to New York. Let's pick an urban area." And it was Tide, that was the product. And I said, "Why don't you just live in New York for two days? And why don't you bring your laundry, just bring your laundry and see what happens and try to do your laundry." And that completely changed their minds about the ad campaign, how they're gonna launch, how they even spend their money, because they realized that the advertising campaign that they had treated doing laundry as a really terrible thing that New Yorkers they don't wanna do. And it is terrible! Who wants to do their laundry in a public space with millions of other people, right?

Tricia Wang: (21:43) However, when you have to do something terrible, you find a way to make do, and to find it to be a joyful thing — or maybe you find a way to at least make it worth your while. And so what I had them do was, I was like, "Just spend time in laundry places where you do actually just go and spend your time there doing your laundry in these semi-public spaces and see what happens." And what we learned together was that people would go... And who would do the launch, they would treat that as a time that they can call their parents, they would read a novel. It was a way outside of their small apartment away from their roommates, and it was a way off the computer. Or people would get dressed up.

Tricia Wang: (22:17) And it was like a Tinder in real life where they would meet other people and check other people out, and ask people on dates. The place of doing laundry became this very rich social space that's a part of the New York City fabric, and there was no way they would have known that. And they came back and they told their advertising agency to scrap it and come up with a new campaign. Their executives had confidence, and of course, they tested out this campaign on making sure that they have the numbers to actually demonstrate the effectiveness of the new campaign, but that's a very specific story of how thick data was launched.

Tricia Wang: (22:49) But other times in other clients we've had, launching of thick data is not like launching a product, but it's actually just about trying to help people understand, maybe with HR, what are we missing with our employee retention? So it really depends on what is the business case and the business question.

To understand customers – get out of the building!

Satyen Sangani: (23:06) I think the immersion theme from my perspective is — and bridging back to some of the entrepreneurship comments — is just this idea of getting out of the building. Often you are in your analytical space, you think the data that you have is all the data that describes everything that's happening in the world, and of course, the world is much richer than can be stored in a database, however large it might be. That fundamental recognition of getting people to sort of “get out of the building” often doesn't play well with people who might be comfortable with their numbers or I might not wanna do that. Is that a struggle in your work, do you find that you have to evangelize this idea of just getting people out of their chairs?

Tricia Wang: (23:44) Yeah, it is a struggle in some cases, especially where there is no pressure or the data team doesn't feel pressure to deliver more tangible results. A lot of times, data teams are not properly leveraged. One of your past guests, Caroline Carruthers, who wrote The Chief Data Officer's Playbook, one of the things she talks a lot about was like, just take people out to lunch in the organization and understand their problems, and I would say you need to do the same of customers, like you have to spend time with your customers and even non-customers!

Tricia Wang: (24:16) Depending on how business-oriented and tied to the product the data team is, then it comes down to the leadership. If your chief data officer actually feels the need to be close to the business and really make sure that everyone understands the customers, then those are the easiest people for us to work with, because they immediately are like, "Yeah, we get what you're saying, and we can implement this new way of doing work," which is getting people out of the office and spending time with their customers or just being curious.

And the more difficult part is when it's clear that the chief data officer, they don’t feel the pressure to really prove to the company their value, and oftentimes data is seen as a vanity department and they're not being fully leveraged or it's more like they need to have it because, of course, every modern organization, every Fortune 500 company, has a data department.

Tricia Wang: (25:07) But it really depends, it really comes down to leadership. But you're right that there's a culture amongst programmers and people who use computers a lot — like analysts, even – that their value depends on how much time they sit in front of the computer, and so if their bosses and managers don't see them at their desk, then they're not delivering value.

At Sudden Compass much of my work is in coaching and not just leaders but middle managers to be like, "You have to give your workers the freedom and really just outline clear deliverables and clear objectives, and give people the freedom to get there and the tools to get there. One of those tools is spending time with your customers. Another tool is working with the qualitative people in your company, which means they're oftentimes not in your department. They're the marketing department, they might be the design department, they might be the customer service people." People need the freedom to talk to other people in the organization.

Tricia Wang: (26:04) But it's so crazy that these large fiefdoms, these kingdoms, the departments within are so siloed and it gets so... It's like crazy. I think the craziest thing that I always get the reaction to when I talk to data people is they're... I'm like, "Pick up the phone and talk to your customer service agent." They're like, "What?" "The customers... Like the people in India or Philippines or whoever we've outsourced our customer service work to or whatever, maybe it's someone in Kentucky.”

Because oftentimes that is seen as a lower value and they're not being paid as much, but I'm like, "Those are your most valuable people." Or if you run a store, actually go talk to the people who stock the shelves and interact with customers all day, and that to me, that's the hardest thing to get people to do, really. They're just like, "How would I even do that?" "Just pick up the phone, email... "I'm like, "You figure it out, you're in the same company!" But that's the hardest.

Satyen Sangani: (26:52) That is hard. And what do you even start to ask these people like, what is your job? What's hard about your job? Are there certain easy questions to ask that are the start for that conversation?

Learn from those close to the customer

Tricia Wang: (27:02) Yeah, just like treating them as a human being. If you're leading with your curiosity, and you know, if you know that somehow, even before you go in, you have to actually at least believe that maybe they know something you don't know about the customers because they're the ones who hear from the customers. The email support people who reply on Zendesk, they are not paid nearly as much as you, but they sure as hell know a lot more about their customers than you do. You may have access to all the quantitative data, but they can tell you why you're getting some kind of trend or behavioral-whatever statistic you're seeing. They can tell you why.

And so if you go in with that curiosity of like, "Hey, I'm trying to understand something," because surely there's no way you know why you're getting the data points back that you're getting. You may know what the data points are: “Half a percentage of people do this, or the drop-off rate is this, or the retention rate is this, or people sign up more at this time or stay on longer during this time, or this country region.”

Tricia Wang: (28:00) But you don't necessarily know why, and you may even think you know why, but you want to maybe confirm that with the people who are closest to the customers. So I would say, just talk to people and be like, "What do you do? What do people talk to you about? What do you see customers doing? What do they come to you for?"

Or "I'm seeing this trend. Here's this data point: 80% of people drop off in month 3 after they have the free trial, but they happen to be ones who are an employee and the companies in our size, 1 to 10." I'm just making that up, right? And then you can be like, "Why do you think that is?" And they might have some ideas and you just keep talking to people and try and piece the story together so that you can tell a whole story. So I would say, lead with your curiosity, and you can even straight-up be like, "This is a data point I'm seeing. Do you have any sense of what's going on? Let's talk about that."

Satyen Sangani: (28:48) It’s funny because I relate to a lot of what you're speaking about. One of the things that I try to do is with our employees and even with many of our customers, just do 1-on-1’s to just figure out like what's going on, and I try to keep them very broad-based, but it helps me get texture in a world where as the company has grown, you don't necessarily have sort of ground-level truth, and so you've got numbers and you've got people's representation of the truth, but you don't necessarily have what is happening day-to-day on the ground, and I find those as some of the most valuable meetings that I can have overall, and it helps me learn and frankly it's a good reminder for me to do them more often.

Tricia Wang: (29:23) Yeah, and do you do them across the organization at all levels?

Satyen Sangani: (29:27) Actually, mostly I try to do them at the lowest possible level, if I can. Obviously, I'll let them be open, but I want it to be not so much management or middle management, because I think those are people that I talk to anyway. I think it's mostly the people who are doing the work, whether they're line sales people or engineers or customer support folks, and that's obviously internally. Then externally, you want to do it with a broad variety of customers, both people who are doing the work day-to-day with the tool, but also the people who are sponsoring in the account.

Tricia Wang: (29:52) Yeah, that totally makes sense.

Satyen Sangani: (29:54) I think it's also fair to say that I find that yes, of those meetings are not very useful, some of them, you have to actually kind of wade through a whole bunch of stuff before you start to get to trends, so there's a little bit of patience that I find that's required in the work. Is that what your experience is?

Tricia Wang: (30:07) Yeah, because it's a thick data point, it's like you get really deep on one person's experience, but you don't know if that's the same as another person. So this is why for ethnographers, we develop tools like creating archetypes, cause you kinda have to start figuring out, “Well, there's a pattern here and there's a pattern here,” because your whole goal — it's to surface patterns — is essentially the qualitative version of artificial intelligence, which is like you're looking for the patterns and you're trying to pattern match, you're trying to sort out what are the buckets of stuff, so it's not easy.

Tricia Wang: (30:40) There’s no obvious thing. Because you can't talk to one person and just be like, "Oh, I got it." You actually have talked to multiple people, and to really get down to the nugget of the insight that will be actionable for your business context, because you might be like, “Oh, that was a cool insight or cool pattern,” but it may not even be relevant for your business, or it may not be relevant for the strategy of the moment that you are trying to execute, or it may challenge your strategy.

And in particular, I can only imagine how hard it is in your world right now, especially where customer buying cycles are longer, right. There is a Gartner research recently that... And I gave a talk with them last year where they released the report where it's like there's now longer buying cycles and there's lower trust in vendors, and customers don't know how to measure success.

Tricia Wang: (31:22) And so it's really hard to get people to pull the trigger on a buy, and especially for enterprise technology right now. It’s just more complicated, and then you're throwing in then... Now, we don't have as many conference gatherings where people would come together to build trust in person with the sales team, and so there's a whole new way of the digital salesroom now where a lot of companies have to reinvent themselves and a lot of data companies, enterprise software companies, are really communication companies. They have to develop all this new skill set, which is not just building a great tool to use, but to build a whole new way to translate and make their tool relevant to potential buyers and to maintain the existing buyers. So I think what you're doing is so key, and every CEO, every C-level executive at any kind of data company, should be doing what you're doing, which is being out there and talking to your own team also.

Satyen Sangani: (32:17) Really any company, right? You always gotta talk to your employees and you’re always gonna talk to your customers, and I think that's a practice to develop, and it's obviously something to remind ourselves of. You talk about this idea of “quantification bias” and tell us more about that. But what is that, and I know you've kind of touched on it, but I think it's a great term to have in people's arsenal.

What is quantification bias? How did it harm Nokia?

Tricia Wang: (32:36) The quantification bias is something that I observe. I didn't have a word for it, but I first observed it when I was at Nokia, where I saw that teams, leaders, everyone was so unable to see and validate and value and interrogate any form of data unless it came in the form of numbers. And they weren't even really willing to interrogate their numbers that much. They would just take the numbers at face value, and what I observed was that this really hurt companies and it put them in a very risky position because it meant that they weren't open to things that weren't quantified, and it's the unknown that helps you grow and helps you figure out if you're gonna be disrupted.

Tricia Wang: (33:15) And I saw this so closely at Nokia — I experienced it with my own eyes — how is it that the world's largest cell phone company that was predicted to dominate forever, and how is that they could just disappear, and why is it that we're holding these iPhones now and not these Nokia smartphones? And I was a witness to that. So that was such an important and formative part of my career that I was like, “How could such smart leaders, smart executives, just refuse to see the value of anything that is not already in a chart or in a spreadsheet or in some kind of database?”

I put a name to it because I saw that as a trend among so many companies that I was interviewing, trying to get a job at, where people were like, "Well, we already have numbers, why would we need to not know anything that isn’t quantified?" It shocked me that such smart people would say this, and so that's why I was like, “Oh, clearly, this is a new emergent 21st century bias that is a bias of the big data age.” Is this bias against anything that has not already been represented in the form of numbers? And my whole point is that this actually is incredibly dangerous for companies and particular companies that want to maintain their growth or continue growing.

Satyen Sangani: (34:26) Yeah, because the companies are essentially, as you pointed out, built to scale things, and that scale is a system and the system is built to replicate itself and optimize itself, and all of that is measurable. So if you get to a place where there's just something orthogonal to the company or to the system, then people are just like, "Ah! Not my thing."

Tricia Wang: (34:44) Yeah. It's like organ rejection.

Satyen Sangani: (34:46) Yeah. Totally. And I think their incentive compensation structures obviously align to all of that, so it makes you almost willfully blind.

Tricia Wang: (34:53) Yes. I always say that my job would threaten executives — my job as the person coming in to help companies better use their data, to advise companies on that — it would threaten their houses on Lake Como. And what I mean by that is that when you have reached such a high level in your career, you have already bought your summer home and your retirement home on Lake Como.

You don't want anything. Anything new could threaten that, and that is exactly what I experienced in Nokia — and I've seen it time and time again — is that you have to take that kind of psychology into account when you are trying to do any kind of change management, and this is why digital transformation is so hard.

This is why I have a job. But why we have so many clients is because when you get to a certain level, you don't want to change, because if you have enough assets and things that are at stake, you're depending on this value of your stock and a certain kind of package, trying something new that sounds out of left field, that the culture or the business environment doesn't value, that is not seen as du jour of the moment, because big data and AI is everything, and here I am talking about “Talk to humans!” when I'm not selling them a data product, they're very shocked, even though I'm saying you need the quantitative data, but it's as if they can't hear it, and it puts a lot of that what they've invested their lives in at risk.

Tricia Wang: (36:10) And so there's a psychology to a lot of this work, which is why I think the best data officers and best data leaders, let's just say, are ones who really understand that their work, it's all about communication, that coming up with really good data and understanding your data structure and lakes and warehouses and all that stuff, and getting the right vendors and partners, that's half of the job, and the other half is getting everyone on board to use it and to feel not threatened by it, so part of your job is to really make sure people feel comfortable with what you're introducing them to.

Web3 and the promise of decentralization

Satyen Sangani: (36:44) Well, I'll bridge to something totally different, but I think an area of your interest in terms of threatening technologies, which is the world of Web3. You've obviously done a lot of work in this area. Lots of people don't understand it, and lots of people think it's a trend and a fad.

Why is this an area of interest for you? And can you tell us a little bit about your work and how you think it's applicable in the Web3 domain?

Tricia Wang: (37:05) I'm still trying to understand it, but I have always been fascinated by the idea around decentralization as another way of operating. So much of our world right now is top-down, it's centralized. And I'm not saying that decentralization is good or bad, and that centralization is some kind of bad thing. I'm just fascinated by another organizing structure.

Tricia Wang: (37:25) And so what Web3 is — there's many definitions of it — but my definition, the way I see it is just that it is about developing new forms of getting stuff done, and that is more about distributed networks and decentralization and just essentially non-hierarchical forms of getting stuff done, whether it's with currency or ownership of real estate. Instead of 1 person owning it all, maybe you fractionalize it and make it easy for multiple people to own. So there's a lot of discussion around Web3, kind of bring more equitable forms of access to whatever it is.

And so I was really lost, though, and fascinated at the same time, because I was like... Everyone was like, "Blockchain will solve everything." [chuckle] And I've been around the block long enough to know that that is not the case, and that there was a lot of ideology around it, so I didn't fall prey to it.

Tricia Wang: (38:13) I remember early days of the internet, I was definitely like, "Oh my God, the Internet's so cool! Once we're all online, then everyone will have a job and it'll be so easy to find information." And of course, over time, I was like, "That's ridiculous, Tricia, because no technology can just solve everything. And once you introduce something new, it always creates a new cascade of other problems to solve."

I saw the same rhetoric with the internet. I experienced it myself, and then I saw the same rhetoric with social media: "Once everyone's connected on social media, we're all gonna be great." And it's like, "No, we have lots of wars right now and misinformation. It has not necessarily made society better." And then I saw the same thing with VR and AI. Same thing, which is like, "Here's a new, shiny tool." I'm like, "It's gonna solve all these issues, and no more world hunger." I was like, I really do wanna get past the idealism and all the naivety around what blockchain could do, and for me to really understand what I can talk to my clients about. And then when I run a company like, “How do I really understand my data stack when I have a team?” I need to be able to really understand that.

Tricia Wang: (39:16) At Sudden Compass, we have worked with other companies to launch labs. What we did with this is we teamed up with the World Economic Forum and said, "Let's work with the consortium and with several blockchain companies to fund some open-source research to really understand where Web3 is at. What is the value of it? And let's get past all the ideological speak and just say practically, 'What can we do?' And to strip it of all of its hype talk, of all the hype that's in the industry."

Tricia Wang: (39:46) I co-founded the Crypto Research and Design Lab. And for the last year, we've been on the ground, researching what kinds of use cases are best for blockchain-enabled technology. So that's what I've been doing the last year. And one of my own personal takeaways is that in a world where we're becoming increasingly digital and we need to bring assets into the market — like new assets that we need to create, and create a market around it — it is valuable to have a tool that shows the provenance of that asset and to scale that asset.

And so the asset that I'm talking about, that I am most interested in right now, is the biological asset of carbon. One of the reasons why carbon markets haven't worked out is because there's been a lack of transparency around them. And carbon is the one thing we know has proven that most scientists can agree on that if we draw down more carbon, because we're releasing tons of carbon, right? Essentially, we're all... Everything we're doing in modern life just releases tons of carbon, from mono agriculture, industrial agriculture to cars, planes, everything. It just releases tons of carbon in the air. And so that's warming up the earth, and that's wreaking havoc, and climate change, and some places are getting older, some are warmer, it's more dangerous. And so we know we need to draw down carbon. The best thing to do to drop down carbon is through regenerative agriculture. But we don't know how to show the transparency of where carbon is from, and to track the life cycle of that carbon.

Tricia Wang: (41:09) I'm really interested in a blockchain being applied to these kinds of situations, where we need to bring a new kinda asset onto the market. There's many applications of the blockchain. I just spoke to a bunch of business leaders about the implementation of blockchain in enterprise settings. I think blockchain can be a misleading word because oftentimes in business settings, you don't need a permission list, meaning it opened blockchain. You can have something that is more like a distributor ledger technology, where it's something that is open within a defined number of players because the most neutral definition of blockchain is something that's usually open. But you don't necessarily want everything to be open, so that's why I'm interested in blockchain as it applies to creating new kinds of markets for assets that we genuinely need for the survival of our humanity.

Satyen Sangani: (41:53) Yeah, let's dive into the examples. Who would be the participants in this carbon market? And how would they leverage blockchain in that use case?

Tricia Wang: (41:53) Right now, the only people who can participate in the carbon markets, because there's no lack of transparency and it's very top-down controlled of who verifies, it's only companies that... And it's large companies that can afford spending hundreds of thousand dollars to be verified by a legacy carbon verifier.

So one of the companies I advise is ReSeed.farm. And they enable small shareholder farmers, so farmers with less than 500 acres, to participate. And these farmers, they generate 80% of our food supply system. The way they can participate is that they're using AI, they're using Google satellites, they're using IoT to track how a farmer manages their parcel of land so they can verify what they're doing, what kind of species they're growing that generates carbon that they're storing on their land. And what ReSeed does is they bring this carbon to the market, and they're able to sell this carbon to companies that want the carbon, but they also get a lot of data about that farmer and their whole entire supply chain system.

Tricia Wang: (43:02) So it is an incredible purchase for a company because not only are they able to say, "We've contributed to lowering our carbon footprint and helping the earth," but what they get along with it is this carbon that's wrapped in data about their own supply chain system.

That is the most important thing for any company, where you have a supply chain that involves farmers, which means a lot of consumer goods companies or food and beverage companies, is that they need to have stable supply chains. It's the heart of their business. You don't have any product without a stable supply chain. So if they can stabilize their supply chain, they can get data. Your farmers also get money from that drawing down and saving, the stewarding of that carbon in their earth, and this is done in a transparent way. So not all of the data needs to live on the blockchain, but what they're designing is a way for at least the provenance of that carbon cannot be tampered with, and you know the origins.

Tricia Wang: (43:53) If you buy carbon and then you're like, "Oh, I wanna offset something," you don't know if the carbon you purchase... Oftentimes, when you purchase something, you get to choose like, "I wanna offset my carbon $5 more," but you don't know if that carbon comes from a mining company that said, "Oh, we're carbon-neutral." But what they really did was they got rid of a bunch of villages, they bought up the land — they stole the land, not even bought. Oftentimes, they steal the land, clear those people off the land, and plant a bunch of trees, and they're like, "Okay, we have carbon."

And that is not an equitable or sustainable way. That's an exploitative way of bringing carbon to the market, but you don't know that. And so what I'm excited about is having these new kind of carbon platforms like ReSeed to be able to show the transparency of where something came from, and you can do that at scale. And what it does is it increases the price of carbon, and that is the one thing we know that we still have a 10-year window of time where we can stop and possibly even reverse climate change.

Digital personhood and the quest for a more equitable world

Satyen Sangani: (44:48) So you talked a little bit about this idea of exploitation, which I think bridges a little bit into this concept that you've come up with, or discovered, which is called personhood. And I think super interesting to understand this relationship between your digital personhood, the world of sort of crypto technologies, and the blockchain, and then how that might build a more equitable world. So maybe dive in a little bit to this concept of personhood and tell us a little bit more about that, and why you think it's important.

Tricia Wang: (45:14) So I will start with making the statement that I firmly believe in, that we are more digitally human than we've ever been in the history of our humanity. What I mean by that is that we have more bits of ourselves because of all... We are interacting in more digital ways. We're getting more things done digitally. When we buy food, our transportation, everything that we do almost involves some kind of digital interaction. Those are bits of data of us that live somewhere.

That means that we are now more represented, increasingly, day-by-day, represented as a digital representation out there, a version of us, right? And there are data brokers that collect our data, our representations, and they are sold. Or there are companies that have representations of us and they make decisions based off of the data they have, the first-party data. We exist as first-party data, we exist as third-party data, and there's this whole world out there. I think it's becoming increasingly unmanageable, and I think people are now facing this question around like, "What does it mean when so much of me is out there?" And we need these services in the same way that we need our arm.

Tricia Wang: (46:23) You know, if you were to cut off my access to any of my social media and my email, I would literally be unable to be human, in the same way that if you were to cut off a piece of my body part. You can't just do that because we are so dependent on these services. What my question is: What does it mean to be digitally human? How do we find a sense of humanity in this space? How do we operate as humans?

I think this all comes down to the question of: What are companies... What are institutions wanna do with our personal data? What is that social contract that we're gonna have so that we can all trust interacting with these digital platforms? I think increasingly, what the implications are for businesses, is that it really is about trust. It's about developing that trusting relationship that I am, in exchange for this interaction, you get a service, but I'm going to be handling your personal data, and this is what I'm gonna do with it. And then you have to engender trust that you're gonna do what you say, that you're not just gonna do X, Y, Z, right?

Tricia Wang: (47:28) I'm really interested in the new forms of exchange and interactions that are gonna come from this. One of the big problems that I see right now that's preventing us from moving forward is that we are totally stuck in the era of talking about privacy. It's really controversial — what I'm going to say, this is the topic of the book I'm writing — which is that I think we're barking up the wrong tree when we are trying to fight for privacy. You have a lot of advocacy groups and policy, state by state at the federal level, to really help people wrangle in and protect their privacy.

It's not that I'm against privacy. I think privacy is important, but I think we're missing the bigger thing that we should be fighting for: It's not privacy. It is our representation of ourselves. We should be fighting to have some control of the representations of ourselves out there. Because privacy is very much about saying, "This is all of mine, and it implies that I can't give this up.” But it's too late. In order for us to even interact online, you have to give up parts of yourself, you have to give up data. There's no way.

Tricia Wang: (48:32) And so how this intersects with Web3 is that... You know, we're just in the beginning days, I don't think there's any clear solutions yet. But one of the questions that Web3 is exploring is, "How do we give people more control of their personal data? How do we store data in a more distributed, decentralized way?" Or "How do we enable people to store their own data and give it up, or be able to decide when you have access to it?” And so I'm really interested in figuring out, seeing different experiments. I don't think it's only Web3.

Tricia Wang: (49:03) I think a really good example of this kind of personal data exchange where it's not stuck in the privacy world, but actually, is in the representation world, the Spotify, when at the end of the year, you get a representation, right? They say, "This is like what you listened to the most this year," and like, "These are your genres," and "This is the kind of music you like." Well, that is really engendering trust because that's saying, "You generate the data by using our platform and we generate data about you, and so we have all of that data. But now, we're gonna reflect it back to you, our representation of you. We're gonna show you what our machines see when we're collecting data about you. And we're gonna show it to you in a way where you're not scared about it. It's going to create more trust, it's gonna make it feel more social. You could share and screenshot, like show your friends."

You know, no one says, "This is Spotify's data representation of me by their data scientists or machine learning team." They don't say that, but it's just more like, "Hey, cool! Look at my top five genres, or top artists! Oh, my God! I had no idea I was such a big Beyoncé fan!" But essentially, what that person is saying, "This is a representation of me, and wow, cool. How amazing this company did that."

Tricia Wang: (50:06) And I think increasingly, the companies that will know how to do that well, which means you have to work more with your comms team and your design team, which is why I talk about, as a designer, I bring that. That is my value-add of like, when you work with data, you have to have a design mindset when we wanna make it useful to everyone, whether it's customers or other business partners, is that design is about making choices. Design is not just like, "Pretty color here." Design is really at heart about making choices and how do you make transparent how you make those choices because everything is a choice, everything is a design. There's no such thing that's neutral.

Tricia Wang: (50:43) How that relates back to personhood and representation is that I think personhood is the feeling of agency, that you still have self-determination. Right now, I think we're at an existential crisis where a lot of people feel that they don't have agency, that they've lost their personhood, not just with the internet, but they may feel that the government controls their personhood, right? Or that they don't have agency because the economy is so limiting. Or they may have a 4-year degree, but they're not able to self-determine the kind of career track because it feels like our economy is falling apart, and so is our banking system. There's all these things in modern life where many people feel that they've lost a sense of their personhood.

Tricia Wang: (51:24) Personhood is not automatic. At one point, Black people were not considered people. They had to fight for the right to be seen as a person. And so my question is: How can we see people as people? How can we be seen as a person by the machines? How do the machines and how do companies help people feel that as you are generating more data about yourself and using these services and participating in modern life, how can you still retain a sense of agency?

Tricia Wang: (51:52) I had a friend who said they stopped using Facebook because they realized that the algorithm wasn't giving them information. It wasn't ever surfacing the friends that they hadn't talked to in a long time. And they are like, those are the friends who were probably going through a hard time the most. I don't mean... She was like, "I don't even know. My friend could have been suicidal, or like going through a hard time. But I was only interacting with my feed, with the loudest people who are posting the most."

And she was like, "I got off Facebook because I realized I wasn't staying in touch with half of my friends." So in a way, she was talking about a feeling of loss of control with how the machine was surfacing back to her her social network, right? These are the kind of questions that people are grappling with. If we want people to use digital tools, our services, we need people to feel they trust the machines that we're asking them to use.

Satyen Sangani: (52:38) Yeah. But there's obviously inherent conflict there because a company like Spotify might reflect back to you what you want, or it might reflect back to you what's most likely to get listened to, which might be different from what you want, but it might actually influence you. On some level, these algorithms are designed to influence you in some particular way, and that may or may not align with your self-image. Your self-image may not align in reality with what you actually do. There are some really tricky issues to deal with in order to get to some reflection of what my true personal reality is.

Tricia Wang: (53:10) I get it. I think you're right, I'm like... I remember when my Spotify came back and I was like, "They're saying I love ‘Little Black Sheep’” or whatever. I don't know, I was like, "No, I just played that a lot because I was with my nephew a lot that year, and we had to play that song to get him to sleep," you know? I'm like, "That's not a representation of me," but you're right, there has to be a serious conversation about how these algorithms control us because we're in an attention economy, and these algorithms do control our attention, and you see the kinda words that are, you know, fake information and all this stuff that they are leading to.

So I think part of it really comes down to us feeling that we also understand how these algorithms work if... This is why on my LinkedIn, I have, "Half of the information is fake," because I don't want the machine to fully feel like they know me, so I have on there that I was like the CEO of a penguin colonizing company. I put fake information out there, half of it, because I'm trying to also understand how the machines see me so that they're not fully influencing me. So I think we're gonna find these kinda acts of resistance.

Satyen Sangani: (54:10) If anybody wants to read the world's best LinkedIn profile, go to Tricia's LinkedIn page.

Tricia Wang: (54:14) Yes. [chuckle]

Advice for CDOs: Embrace the “soft Cs”

Satyen Sangani: (54:15) You work with these CDOs and obviously, you've advocated for them to work against quantification bias, towards thick data. What do you see as the sort of leading edge, bleeding edge, of where these folks are needing to invest and build skill sets within their companies? How do you think about advising them around what they should be really thinking about that they're not thinking about right now?

Tricia Wang: (54:36) The biggest thing that I think a leading data science leader should be investing in is not in the common Cs of categorization and cleaning data. It's in the other Cs, which is culture, communication, and customers, and collaboration. So these are the seas that we really work with, and it is actually genuinely hard to do. And your job as a chief data officer or a data leader in the company is, like I said before, data is only part of your job, generating the quantification to reflect back to the company. The other half is the bleeding edges around communication and helping the rest of your business, your business counterparts, to understand the value of this in a way that isn't scary and where they can see that it actually is gonna improve their business.

Tricia Wang: (55:27) What's crazy is I think the hardest part is still saying that you've gotta spend time with your customers. And this is a big topic in your podcast, and many of your guests have talked about this, which is: Data is what you do, but you don't need to talk about it. AI could be what you have, is a tool you have. Those are tools. Using machine learning, data governance are tools. But those are really big terms that are not only scary, but they might not be immediately applicable because you need to talk about the outcomes that these tools lead to. And so it's really learning to become a better communicator and collaborator across the business and to say, "You know, we are not in a silo."

Tricia Wang: (56:06) But that takes a really brave kind of leader to work that way because it's not just about having the light shine on you, but it's about you making others in your company successful. And it's also about courage, about raising the big questions of what could be missing. Again, do you want to risk your house on Lake Como, or whatever it is? It takes courage to actually say to someone, "Hey, the path that we're going on might not lead us to where you want us to go, or what the company has defined as the outcome, or may not be the best outcome. It may work for shareholders, but I see this, potentially, as a problem for our customers or for society."

It really takes a lot of courage to communicate and to collaborate. And I think in a time where our economy is going into a recession, what's called the “rich session,” it will take even more courage, especially for people in these kinds of roles to think more expansively, but you really are at the heart of the business. It's really an asset that you are responsible for making even more valuable.

Satyen Sangani: (57:07) Well, I have nothing to add on top of that, that was phenomenal. Tricia, thank you for taking the time to speak with us today.

Tricia Wang: (57:14) Thank you, Satyen.

Satyen Sangani: (57:22) Often, the most important truths aren't in the data, but out in the world that the data tries to represent. You can't quantify stories, emotions, or interactions, yet these forces drive what the data just can't represent.

As a tech ethnographer, Tricia is pushing cutting-edge businesses to learn from this kind of information, which she calls thick data: Information that's just not easily quantified.

So, as much time as we're spending building models and moving around data, here's another exercise to make your team more radical: Get your data folks out of the building! Push them to visit the world of your customer. Encourage them to understand what makes their customers tick, what makes their pain points painful — what does the data fail to tell us?

Thank you for listening to this episode, and thank you, Tricia, for joining. I'm your host, Satyen Sangani, CEO of Alation. And data radicals, stay the course, keep learning and sharing. Until next time.

Producer: (58:10) This podcast is brought to you by Alation. Does data governance get a bad rap at your business? Today, Alation customers wield governance to drive business value, increase efficiencies, and reduce risk. Learn how to reposition data governance as a business enabler, not a roadblock. Download the white paper Data Governance Is Valuable. Move into an offensive strategy at alation.com/offense.