← Embracing Digital Transformation
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- [00:00]
A
Welcome to Embracing Digital Transformation. Before we dive in, I wanted to personally thank you for listening. Many of the ideas we discuss on this show inspired my new book, AI Augmented Teams. If you're looking for practical ways to combine human expertise and AI to achieve better outcomes, I think you'll find it valuable. Learn more at Paydar AI Books. That is P A I D A R AI Books. Now let's get started with the show.
- [00:30]
B
Right now there are some folks out there that are trying to reboot like a trifecta. Like there are a lot of companies out there trying to work on the next revolution of this, which is how do we make it so that anybody without really intrinsic data knowledge can make use of the data, unlock more of that data potential in their day to day work.
- [00:52]
C
Welcome to Embracing Digital Transformation where we explore how people process policy and technology drive effective change. This is Dr. Darren, Chief Enterprise architect, educator, author, and most importantly, your host.
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A
On this episode, a history lesson in AI and data unleashed in Silicon Valley with Wei Jiang, pioneer of data management and chief product officer at Conductor.
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C
Okay, Wei, welcome to the show.
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B
Thank you for having me. Darren, excited to be here?
- [01:29]
C
Yeah, I'm actually really excited. I in our pre we I always talk to my guests beforehand, like a couple weeks sometimes beforehand. And I wrote on my notes here that you're a good storyteller, so we'll find out for sure by you introducing yourself. But just so you know, on my show, I only have superheroes on the show and only every superhero has a background story. All right, so Wei, what's your background story?
- [01:59]
B
Yeah, love to. First of all, thanks for having me on the show. I'm excited to be here and I'll tell your listeners a little bit about me. So I was born and raised in Shanghai, China. I came to the United States when I was 14 years old. And I will tell maybe another story at some different point about how I got into computer science. But it has a lot to do with my family, has a lot to do with what my father actually studied when he was in college. But one way or another, I ended up being here in the Bay Area. I've been living in San Francisco Bay Area for my entire life basically. And I got my start by attending University of California, Berkeley. And I had to pick a major I didn't really know. And I was born in at the time when just as I was about to enter university, it was the initial dot com boom.
- [02:53]
C
Oh, the 90s.
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B
The 90s, yeah. And this is before Google, before Netflix, before any of these Amazon eBay. This is before all of that. And I just remember getting mesmerized by these gigantic shiny disc that was sent to my inbox or in my mail. Not the email inbox, but the physical snail snail mail Inbox.
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C
Snail mailbox. Yeah.
- [03:18]
B
It's called a company called aol, which is American Online. And that was my first, first exposure to the, to the Internet, I suppose, because when I, when I went to Berkeley, we all got, you know, on the first day of school we all got very excited about having an email address and getting up our dial up modem. For those listeners on your show that's a little bit younger, you probably don't know what a dial up modem sound like, US Robotics, but it's like this, it is like unmistakable sound that you're like on the Internet. Right. So I think I was lucky in the sense that like right at the time when I went to school is the beginning of this huge Internet phenomenon, the dot com boom. And if I had gone to school just a few years later, if I had gone to school a little bit earlier, I don't know if I would have ended up choosing the major of computer science that I chose. And that's really what led me down the path of working technology pretty much for the last three decades, two and a half decade almost at this point. Yeah. So that's how I got my start. Now one other story I will tell and we'll get into what I currently do. But one big victim of the dot com boom at the time was a company called webvan.
- [04:35]
C
Oh yeah, I knew Webvan very well.
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B
Yeah. And I still have their color crates in my garage. I have the yellow and the green crates and they're actually worth hundreds of dollars on ebay right now because they're considered like an Internet memor.
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C
Oh yeah, absolutely.
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B
But I like the web and story because it's a perfect example of something that was just a little bit too ahead of its times. Right. If you think about what we do like today, you, you wouldn't even bat an eye. Like I get my grocery delivered from, you know, Safeway, Doordash, Whole Foods, whatever. I go on Amazon Fresh. I can within an hour get anything and everything I wanted. And this was actually a concept that was there, you know, in the 1990s, like the early 2000s. There was these brilliant ideas that actually had a lot of value. But the market, the technology, the go to market model, like the pricing strategy and the cost model did not allow those businesses to survive. But so this is just one of the early lessons I learned from the dot com boom and bust, which is you can have absolutely the right idea, but it's not the idea itself. And any one of these pieces is not sufficient to make a successful, like thriving business. Right.
- [05:53]
C
You know, I'm glad you brought that up way because it brought back memories of. Because I was ra. I start. I did a startup in Silicon valley in the 90s and I stood at one of those VC breakfasts that they always had and you got a lottery and you got 30 seconds for your elevator to a pitch. Yeah. And then they hold up the red and green signs. And I remember the first one I went to webvan pitched.
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B
Yeah.
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C
In that very first one. And they got greens across the board. They raised $250 million. I did not get picked on the first one, but the third time I went to one of those, I got picked.
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B
Wow.
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C
Red all the way across the board. I was like, oh, my ego got blown up. I was like, I thought I knew what I was doing. I was young, energetic. No, they said, yeah, that's a disaster.
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B
Yeah. But you know, you never know. Converse is also true. Just because an idea was terrible or not so good at the time, it could be very good now. Right. It just depends on.
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C
Yeah. Timing is we ended up selling. We ended up shoe strap or what's. Yeah. Strap bootstrapping that ourselves ended up selling it to Rational Software at the time. Time.
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B
Oh, I know Rational very well.
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C
Yeah. Turned out to be.
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B
We used to create those object model diagram. There was all ab.
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C
Absolutely. So I mean it turned out okay in the end, but that was an unusual time and I'm glad you brought that up because there's so much parallels to what happened in the 90s to what's going on today.
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B
AI right now. Yeah, yeah, 100%.
- [07:29]
A
Hey, sorry to interrupt the show, but I have to tell you about my new book, AI Augmented Teams. Over the past few years, I've worked with leaders across government, industry and higher education who are all asking the same question. How do we use AI to move faster without losing trust, quality or accountability? That's exactly why I wrote this book. AI Augmented Teams gives you practical frameworks and proven workflows to help your team deliver reliable, defensible results. With AI, it's not about replacing people, it's about helping people and AI work better together. If you're leading a team, driving transformation, or simply trying to stay ahead, I'd love for you to check it out. Visit Payar AI Books to learn more and get your copy today. That's P A I d A R AI books, 100%.
- [08:26]
B
There was. Definitely a bubble, but in that bubble, so much technology and so much innovation and so much entrepreneurship happened. Right? This was the beginning. Like I know everybody talk about Steve Jobs and talk about like the even one generation before me a little bit, but the Internet boom is really my first hands on experience of seeing. And because I was going to school at the time in CS as well, I saw the movie End to end of how. And that's also kind of how I got my entrepreneurial bug, of how I enter product as a discipline. Because after graduating from from Berkeley, you know, CS major, you become an engineer. Right? That's just like the standard prototypical thing. But for me, I was an engineer for about two years in the early 2000s and I quickly just again like the entrepreneurship and seeing all my colleagues and friends starting to create or start businesses, whether that's Internet, you know, Internet like store kind of businesses, or web creation or web PA creation businesses. It made me hungry for not just how technology works, but what to build and what kind of problem does it really solve and what value does it bring to the businesses. And I became a product manager before I even knew that I wanted to become a product manager because someone else have recognized a little bit of my talent. They're like, well, you're really good at creating software, but what you're really good at is translating business speak to technology speak and bridging the gap between business and technology. And that's basically what I've been doing since then. You know, that's how I started my product management career.
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C
You know what, that's a sweet spot to be in right now because AI can enhance that superpower that you have 100%.
- [10:18]
B
I mean, gosh, I wish I had AI. I mean, I can't even think of what if I had AI back then. But a lot of the work that I did was basically taking and then speaking a little bit about how I got into the data business. Right. Because later on we talk about what I do now with Conductor, even though it's a very specific vertical. But before joining Conductor, my current company, I worked almost for two decade in what we call these horizontal data infrastructure, data platform companies. Right, right. And I got into the data business because actually it's funny story, speaking of stories, I always measured like my mom and dad would call me up and ask me like, what do you do? So I told them that I joined this company called Informatica. This is my first B2B enterprise software. And I said mom, I'm doing something called ETL which is extract, transform, load. And she's like what the heck does that even mean? So I remember once upon a time I couldn't really explain to people why data is actually the underpinning of all nice things. Basically and this is during like early 2000s or maybe like 2002, 2003 time frame. And I always consider working data kind of like this janitorial non appreciative like nobody knows that when you go to the ATM for example, you withdraw money. What actually happens behind the scenes to settle your transaction? When you go to the doctor's office and you see this very ancient looking window, there's a system called EPIC that's running behind the scenes. But it is a data reconciliation system of like electronic medical record which is a way to take patient data. Munch that with all the hospital like all the central repository with all where the information goes. So pretty much every single experience you experience digitally, I knew back then that it always went to the underpinnings of that is some type of data infrastructure that's going to hold everything together. Right. So at that time data was not sexy, it was not glamorous. Nobody really knew what ETL was. I couldn't explain to my parents what the heck I actually did for a living. But I'm glad that you know, by sheer luck, like really by sheer luck and maybe like also some curiosity that I ended up being the data business in kind of this sort of non glamorous at the time this like very like munging data was seen as kind of a behind the scenes type of
- [12:49]
C
a. Oh yeah, you're not a good enough programmer. So you ended up, you end up
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B
doing the data stuff. Otherwise you want to create all these amazing, you know, visual experiences. Web pages.
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C
Exactly.
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B
Shopping cart apps. You know, why don't you work on one of those things that you could explain to people? But, but yeah, but I think you know, looking back that that was the foundation honestly to my passion for everything that I do now about how do we unlock that data. And speaking of parallels, I mean I can't begin to tell you Darren, like everything that was a parallel with kind of the, I would say the big data revolution. Right. That makes of snowflake databricks and all these company that are very, very successful. We're seeing a re almost like a renaissance of how that is all being played out right now with AI and data as well.
- [13:39]
C
Well, well, because AI is data hungry. It'll take all the data and, and I think we all know, know the stats on this, right. Companies, about 90 of companies. Data is not being used but just stored swamp. Yep. Yeah, it's a big, it's just there and, and no one knows what to do with it all. I think AI can unlock a lot of that and curate a lot of that data for us.
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B
Yeah, 100%.
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C
And then feed itself.
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A
Right.
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C
It's like our AI can actually cook and eat, which is really interesting.
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B
Yeah, yeah. I think if you look at my trajectory, like after Informatica. Informatica was a technical product. In order to use Informatica you have to have some level of data, data domain knowledge. Right. You probably should know what a star schema is and what a Snowflake schema. A lot of people don't know. Why is Snowflake named Snowflake? Snowflake is actually the name of a type of data modeling that you do where you find out, you know, the specific dimensions and all of that. But I will say if you look at the trajectory of where the software has been is like increasingly almost like as I get older and as I change my career from one, you know, one job to the next job, every stage of that I have gone one more level up to meeting where I think the general population and the business users. Right. Informatica was not a piece of software that could be used by the business user at the time, but slowly, surely over the course of the, you know, a decade, a decade plus. I think the after Informatica went to a company called Trifecta and the company was actually started as a mission to democratize data for non technical users or for not as technical users as those that are data engineers. And I still sometimes chat with my colleagues or ex colleagues at Trifecta to say if we had the power of AI back then, what type of product we would have created. And I think right now there are some folks out there that are trying to reboot like a Trifecta. Like there are a lot of companies out there trying to work on the next revolution of this, which is how do we make it so that anybody without really intrinsic data knowledge can make use of the data, unlock more of that data potential in their day to day work. So it's been very rewarding to see that.
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C
Very, very powerful and I see those kinds of companies need to exist. So there's some good investment for some VCs right there.
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B
Absolutely, absolutely.
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C
All right. So Wade, with all of this craziness going on, data is, they've been saying for, for Years data is the new gold or the oil.
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B
Oil. The new oil.
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C
Yeah, yeah, yeah. But I haven't seen much action. I see a lot of talking but not a lot of action in corporations on actually managing their data.
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B
Yeah.
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C
Why is that? Well, is it still just not sexy enough for people to.
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B
No, no, I think people now, I think business now, definitely after two decade of marketing of the data as a new oil, I think people foundationally understand that data is valuable. Right. But actually this is a good segue to talk a little bit about how I end up at a conductor, because now I'm at Conductor and I've never worked before Conductor. I've actually never worked for a company that's kind in the vertical space. Right. I created software that acted as horizontal technologies that whether you're working, uses to
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C
build stuff on top of.
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B
Yeah. Whether you're working marketing, whether you're working legal, finance or whatever, you could use technology that I worked on to make applications and things for business users to consume. The reason that data unlocking data is so challenging one is technical reasons. Right. You have disparate data systems. I remember something that Sohei Babasi, Sohei Babasi was the CEO of Informatic for the number of years that when I was at Informatica, he came from Oracle, a very big database company. And he used to say that for as long as that there are separate silos of data, there will always be a job or a company for someone that's the job of them to come and wrangle and munge all the data into something that's meaningful and consumable. He called that data integration. Right. The word is data integration. Well, I think he's absolutely right. He's wise. But you know, he's one of the smartest person that I'm, I've gotten to know in my Life. And he's 100% right. So some of the difficulty with data has nothing to do with the business challenges. It has to do with the technical infrastructure. A lot of the data, for whatever reason over the last two and three decades have been locked away in disparate silos. Different units have like their own systems for storing that information. Right. Even though people want to centralize all the information, but for one way or another, separate spreadsheet, separate data marts, separate data warehouses sits out there. But the other challenge is even if you have a great technology now, like for example, if you do deploy databricks or Snowflake or Amazon Restaurant, there's all these technology to help you harness these data from different silos and do the integration and combo of the data is that in order to actually leverage the data, you have to have decent domain knowledge of what the data actually means. This is the other big challenge with the business use cases of data. Now in the old school days, people will come up with metadata systems. It's like you have your data systems and then you will have your metadata systems to describe what do you actually have in the data systems. So if the data system has a table that's called client info 26712, you have a business glossary or some kind of business metadata catalog that says when you see technical identifier of X in this business, it actually translate into Y right. Now again, this is very challenging because business rules, business descriptions, they don't match to the technical identifier. And the way the technical system gets integrated together one to one, often you have to translate the business logic into data manipulation logic that you can then apply to the actual data. Now, even though now it's 2026, we have so much more with AI. AI is the ultimate like powerhouse for solving what the heck does the data really mean if you have some kind of metadata catalog to help them identify that information. But the challenge is the number of people in an organization, any organization that can somehow simultaneously speak the language of the technical infrastructure of the data and the business requirements and meaning. It's very small. It's a teeny, teeny population. These people are probably your most valued folks. These people are your domain SMEs, but not any domain SME. You need a domain SME that actually can speak the language of technology basically. And we struggle with this at Conductor even today, which is I have a whole team, I have a very large data science and data engineering team and I have a very, very powerhouse customer success and professional services which was I call our go to market, business facing, client facing team. But to get them to work together and create, even with assistance of AI, to create really meaningful insights is not as easy as one would think. And that's why a lot of the
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C
data, why is that the case way? Why, why, why is that so difficult to have someone that can, you know, span the fence or well, sit, sit across those two worlds?
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B
That's a really great question. I actually think that this might be a little bit of a hot take, but I think it has a lot to do with how people get educated and get brought up in their career tracks. Right? Typically if you think about the person, that's typically like a marketing, like say a digital marketer like I'm working at a doctor with digital company. The people that work in digital marketing, they need to learn so much about their domain, like content, you know, brand. What does brand marketing practice look like? Growth marketing. They have a lot of like day to day things that they need to learn about how to do the practice of, of marketing. But they probably did not in their career or in their experience. They've always had someone else that they go to and say oh so and so why don't you help me prepare a report, why don't you help me create a dashboard? They always pass the work that they needed to do the technical work to somebody else, right?
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C
Yeah.
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B
But if you want to tap into AI right now, I think AI is like the greatest compressor on some level, which is everyday people without a lot of technical skills can like vibe code or can tell AI what they're actually trying to do. I think AI bridges this gap which is for the business users who doesn't understand how to write SQL or how to build a star schema or snowflake schema or how to build a data store. They can express their requirements via natural language and have AI assist them by creating the technical artifacts for them or at least as a bridge or shorter the time between them and collaborating with engineering and as well as a technical user can use AI and be like, hey, what does this data mean? Like how would this data be helpful? What kind of problem can it solve for X, Y, Z? Right.
- [23:40]
C
Well wait, you just described a big huge potential problem. Because before, because I was one of those people, I sat in the middle, I sat between developers and test engineers or developers and build engineers early in my career. So I understood the development side, I understood the process side in, in big huge systems I was invaluable.
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B
Yeah, right.
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Because I, I, I was speak the
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B
language of both parties.
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C
Yeah. But basically with AI, I don't need that person anymore. Or I, I, I, I really do. But I don't see it's not painful that he's not there anymore. Does that make sense?
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B
Yeah, it makes sense. Although you know, I think everybody kind of have, I mean still, even till today, I think there is a school of thought that hey, AI is replacing or taking the place of a lot of the jobs or a lot of the work that humans used to perform. Right. You mentioned that you were the translator between business.
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C
I was the translator, yeah, I was.
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B
But now with AI you don't need as much of the translator job. Well, I think of this slightly differently. I just came back from Adobe Summit. And on the keynote, this question was asked of Jensen at Nvidia, right? And he gave an example, this example about in the beginning when AI came along, everybody thought that all the radiologists. He was talking about an example of the radiology person, right? He said that people's prediction is that radiologists, all of a sudden there will be no demand for radiologists because one of their key part of their job was trying to look at the image of the things and trying to like figure out what the image is trying to tell them, right? But as it turns out with AI there are actually more radiologists and radiologists are more in demand than ever before. Because people understood that just because you understand what the data says doesn't mean that you are actually able to build a strategy, act on this data, make the data actually useful. There is a lot of untapped potential. I want to say, right? People are evaluating what their job and the value of their job based on what has been the art of possible based on today. But if you were to think about what the art of possible is for AI, if the pie keeps getting bigger, then your value should not be locked based on what was feasible before. So I think if I were to apply that to your question about being the translator between business and technology. If AI can do a lot of the requirement, clarification, gathering of information, bridge the knowledge gap between engineers and business users, then all the things that you never dreamed about before, you can now actually get to go work on those things. Like, okay, I do understand my model, I do understand this is where my gaps are in my content. But that understanding how that doesn't actually get you going on, then creating the content, make sure the website is healthy, publishing it, seeing the input of that and then being able to iterate on that faster, right? These are the things that you never got to before AI came along because
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C
you were so unlocked. It truly augments, augments our teams, augments us as individuals. I'm glad you brought that up because it fits right with a book that I have coming out in May this year called AI Augmented Teams. It talks exactly about what you just said way which is, look, there's going to be more jobs, we're going to be moving up the value stack. That's how I see it. I'm not going to be sitting there. Early in my career, I ran the nightly builds, right? So I, I would drive home from work, I'd start the build at like 8 o' clock when all the rest of the engineers were done checking in and Then I'd wake up at two in the morning and check on it, make sure it was still running and then fix any problems that were there, like I ran out of disk space or whatever and then kick off the build again and have it ready for everyone the next morning. That job, I really shouldn't have had to wake up at 2 in the morning.
- [27:45]
B
You shouldn't have to do that. I, I think.
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C
And I could have been doing more valuable things.
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B
Absolutely. I think right now we're literally witnessing probably like two years, three years from now when we look back at what's going on right now. This is a, such a seminal time. This is a time where we're basically rewriting the job descriptions of a lot of the jobs that currently are being performed. Like what is the job of a product?
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C
Totally agree.
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B
What is the job of a data analyst? What the task that used to define that job probably isn't what it's going to be like two years, three years or even now. That's not the job anymore. The job is not to translate meeting calls down to notes, synthesize the notes and present a summary and come up with a report. That's probably not the job anymore. That job still need to be done. But that job now is being done by AI. So now the job is how do you leverage that data now to activate a strategy? So there, you know, I don't want to be a person that says, oh, all jobs are going to be safe. There are going to be some jobs. If they're really at the lowest level, there will be some automation. Just like Industrial Revolution have automated away a lot of the assembly line and like a lot of the physical like labor. I think they're in the digital world. There is going to be some job that is going to get eliminated. But those, the people that used to do those job based on their own curiosity and their capability, they are now able to tackle I think more strategic and higher level things that they're unable to do before. And I think that's exciting.
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C
You know there's a prime example of that in the 60s, 70s and even in the 80s there were typing pools.
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B
Yeah.
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C
There were professional typists. And when I say this to like my Gen Z and millennial listeners, they're like what? Someone had a job that was a professional typistry. Yeah. Or data entry was the another term for it. But there used to be rooms full of just people just doing that job. They're gone. So where did they go? They're now project managers, program managers, administrative assistants, VPs there. I mean, their value shifted and people shifted and they become more valuable to the company. So I see the same thing happening.
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B
I do, too. I do, too. I'm 100% with you. I think I've always been a bit more of a technology optimist. I see the potential being endless. I see that we do need guardrails. We do need governance, we do need security. I am as concerned as many others about the current state of, like, complete wild, wild west when it comes to AI adoption and AI. There's very little AI regulation. That might be topic for a different day, but. But I 100% believe in the power that AI unlocks, especially for general population. Even if you didn't grow up with a computer science degree, even if you don't have the exact technical training, you now have, like superpower in your hands, then if you learn how to use it, you can go very far with it. You can go very far with it.
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C
Yeah. No, you hit it right on those. Hey, Wade. This has been. I didn't even talk about what I wanted to talk about, but we're. This has been a great. A great episode. Thank you for coming on.
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B
Yeah.
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C
And we're happy to. Which.
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B
Okay. Didn't even really get to talk about Conductor too much, but so let's.
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C
Let's talk about Conductor a little bit because, I mean, this is where your journey got to. So what is Conductor? What does it do?
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B
And yeah, I can tell your listener a little bit about Conductor. So Conductor is an enterprise AEO and GEO platform. Now, for those your listener don't know, AEO stands for Answer Engine Optimization or GEO Answers for Generative Engine Optimization. The industry can't quite decide on an acronym for what it's worth. So it's always aeo, geo. But the history of the company, the company's been around for more than a decade. It got its root in SEO. I think SEO is probably something that most people will know. It's search engine optimization. So I think the company builds a very comprehensive platform. And the reason that I got brought in that I joined the company is because of all the things I actually talked about in this episode with you, which is the marketing technology industry, the Martech industry, has been suffering from these really, really impenetrable data silos for years and years. People want to bring attribution and holistic marketing insight together. But because of the technology debt, because of the hurdles that people have had, it's been very difficult for digital marketers to really just like, sit at their desk and Be able to pull data in from search, performance, website performance, campaign performance, and tie it all together end to end. And then not only tying it together to understand the insight, but actually be able to, like, formulate a strategy and be like, what should I do as a brand to where should I invest in order to get found? It used to be getting found on Google Search, but now it's about how do you get found in ChatGPT and how do you get founder or Gemini? So that's what Conductor does. And I'm the Chief Product Officer there. I manage our product strategy, our engineering team, and our UX team. And it's been a great journey for me because I was able to take 20 years of my domain expertise in working in enterprise data and bringing that to Conductor so that we can then solve some of these really big technical hurdles. And with AI right now, I think we're finally reaping the rewards of this investment that we made to bring all the data together in a unified system. And now we're off and running with digital marketers able to use our data, you know, every day in, in the AI surfaces that they're most familiar with.
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C
So I absolutely have to play around with Conductor because I, I want my show to grow.
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B
Yeah, you want to be found in AI.
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C
I want to be found.
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B
Right, exactly.
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C
Even though, even though my audience is pretty good, we get, we have about a million downloads a month.
- [34:08]
B
That's amazing.
- [34:09]
C
Which is great. So thank you. And share the word. Right. Get it out there. I'm going to start taking a look at conductors, see how it can help me out.
- [34:17]
B
Absolutely, yeah. Go to our website. We have a trial. Anybody can get started. And we just released something called Agent Stack, which is if you're using Claude, you're using ChatGPT, you're using Microsoft Copilot, you can directly plug Conductor into any one of those surfaces and you can be supercharged with AEO intelligence.
- [34:35]
C
Coming from Conductor, that is super awesome. What a great pitch. Wei, thank you for coming on the show. I think we need to have you back. We, we most definitely need to have you back. You do such a great job explaining really hard concepts. So thank you so much for coming on.
- [34:53]
B
Yeah, thank you so much, Darren, for having me. I'm, I'm happy to come back, especially if there's interest from your audience. And thank you so much for the opportunity.
- [35:04]
C
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