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Show Notes

Carl Lewis:

Welcome to The Connected Enterprise podcast. I’m Carl Lewis, your host from Vision33, and my guest is Jonathan Chin of Facteus. Jonathan is one of the rare people I've interviewed who lives in my home city, Portland, Oregon. Welcome, Jonathan. Tell us about yourself and Facteus.

Jonathan Chin:

Thanks for having me, Carl. I’m the co-founder of Facteus. We’re a data company that specializes in consumer transaction data. We have a panel of consumer credit and debit card holders, and we can mine their data to create insight products, reports, and data products in general. We’re in an emerging market called data as a service, or DaaS.

Carl Lewis:

We have SaaS and now DaaS. What is it exactly?

Jonathan Chin:

It stands for data as a service. I have a love/hate relationship with the acronym. It explains that we're a technology service company specializing in data, but it doesn’t explain that we operate differently than normal software companies.

What separates us from normal software companies is that we sell data. Whether it’s data feeds, dashboards, or GPT insight products, our value proposition is giving data to our customers that they didn’t have access to before purchasing our products.

Carl Lewis:

You merge data from other sources, too, not just credit and debit card transactions, right? Do you see new data sources on the horizon?

Jonathan Chin:

As the digitization of everything and the internet continue to expand, yes—more data will be more available. The obvious thing we specialize in is how people spend money. That’s grocery loyalty cards, credit and debit cards, payments like PayPal, Venmo, Affirm, and buy now, pay later. How people spend money is changing as we speak, so more data from that ecosystem will be valuable.

There’s more data every day. There's some stat that most of the data we see was created in the last two years because it continues to grow at an infinite exponential rate. Search data was a big thing before. Now I'm interested in how people are using generative AIs and what story that tells about what people are working on, thinking about, aspiring to be, shopping for, or just where society is going. There will always be new data.

Carl Lewis:

Definitely. When I first looked at ChatGPT, it was version three. Now it’s version four, and it will keep morphing. Part of that is the data, right? That there's more data periodically, so you can update it with more data—but what role will DaaS play in fueling the growth of ChatGPT and its large language model competitors?

Jonathan Chin:

There's a lot of competition, and we’ll soon be where—between the Google Bard model and the open AI Microsoft ChatGPT model—there will be some sameness and averageness. Saying which one’s better will be splitting hairs. The differentiation will be their ability to understand/synthesize data and train on data the other doesn't have. That's where DaaS can become an important industry in pushing AI forward.

Overall, these have become APIs. The language models have become integral and are the next wave of startups being funded in the venture capital world. In those use-case-based language models, data will be the only thing that differentiates them. Because without some unique proprietary data, Google and Microsoft will just beat whatever startup that tries building on top of them.

It will also help expand it because, from a DaaS perspective as a data vendor, one of the biggest hurdles to expanding the market or getting customers is educating customers about the domain knowledge of data. Because data is still an emerging space. If I sold you data, Carl, I'd have to explain how to look at it to make it useful for your business—how it could help you grow revenue, expand customers, understand competitors, etc. And that domain knowledge can be baked into these large language models to create more data users or expand the market.

Carl Lewis:

You have a lot of data, but it's fairly general. Do you see specialties getting created in your industry?

Jonathan Chin:

There's room for specialty, yes. As a DaaS company, the challenge in specializing too narrowly is that you need a large enough customer base or total available market (TAM) to build a business. There's a high cost of building a data business, but we believe there's high profitability at a certain threshold/critical mass. We pursue generalization of spending because any specific category—for example, coffee shop spending—is too small for us to warrant a staff of X number of data engineers, data scientists, and business leaders. The future can be anything, though, so as markets and data grow, specialization might be required.

Carl Lewis:

So as much data as there is about coffee shop expenditures, it's not enough for somebody to build a data business around.

Jonathan Chin:

In our experience, yes. There aren’t enough customers who would pay for that depth of knowledge.

Carl Lewis:

Understood. Some industry experts are saying the DaaS industry is positioned for tremendous growth. Why do they think that?

Jonathan Chin:

We're in this interesting time that has coalesced with technology and how people run businesses and AI technologies emerging. Big data technology is ubiquitous now. Even if firms aren’t using big data, they're aware of it, and they understand the players.

Marketing companies have done a good job—in a scary way—of positioning how data can create more efficiencies despite the creepiness and potentially unlawful or immoral ways marketers are using it. We need to put some privacy and restrictions on how people use it. Also, AI has created a huge intake in people’s ability to synthesize data and let the AI create all the contextualization, and that training AI is also as important on data.

This is coalescing into a huge opportunity for the DaaS industry because we're unlocking data. The analogy is that we're miners finding valuable nuggets that are hard to get to. Not everyone can do it, but once the data’s unearthed, people see tremendous value in it. Plus, there are more applications for it now.

Carl Lewis:

We discussed people who start businesses. Many small businesses are started by the seat of our pants or a gut feeling. And that’s the reason many fail—they started without much market information. So this could be a boon for somebody who wants to, for example, put a restaurant on XYZ corner. They could find out if people in this area go out to eat. What's the available market you're competing against in terms of “Are there customers out here, or are there only 85-year-olds who are too tired to go out to eat?”

How else are people using data? What questions are your customers looking for answers to?

Jonathan Chin:

You hit the nail on the head. Before, if a small business owner wanted to start a business like a restaurant, a consultant—likely expensive—was their only means to get data like that, via surveys or something else that isn't that accurate or empirical. Now, you're 100% right. Data is at your fingertips, and companies like Facteus make that data available to entrepreneurs.

One of the big markets we operate in is the investment market. Active stock traders use our data to understand trends about companies. For them, timeliness matters. They see a lot of data sets, but understanding how Target and Walmart are doing from a sales perspective year over year is something they'd pay for. Same-store sales are a big number quoted in those quarterly financial reports. They could use our data to get a sense of what those reports will look like before they come out.

Market share is a big one retailers look to Facteus for because that data's not easy to get. They have their own data but not their competitors' data. The big thing recently in the restaurant category is delivery services. We can see from where and how often people are utilizing delivery services. Is there a winner and a loser? Uber Eats or Grubhub? Does one offer better rates for a restaurant? We can see all that data from a preferential perspective.

Same with grocery delivery. That's been a big uptick; people are trying to understand not just grocers but also the CPG companies that want placements on shelves. Brands want that type of data. So, our consumer data has a lot of depth from a behavioral perspective. During the pandemic, many spending behaviors changed, and now, post-pandemic, some of those changes have stuck.

Carl Lewis:

Definitely. My family occasionally had a meal delivered before the pandemic when everybody was tired and didn’t want to cook. I'd say that's tenfold what it used to be. An awful lot of buying habits have changed.

Jonathan Chin:

Grocery delivery has been a big change in our family. We like to cook, so we don't eat out much, but we use maybe ten times as much grocery delivery, especially Instacart and Costco. Between parking, the lines, and the people, sometimes you're like, "You know what? Delivery is just easier.” And you pay a little more.

Carl Lewis:

If you go, the cart is full, but if you shop online, you can get what you planned.

Jonathan Chin:

You won't be tempted by the wine aisle.

Carl Lewis:

How does this technology affect individual consumers or small businesses? All technology eventually rolls downhill to individuals. I'm a charter member of the Gadget Club, so eventually this will mean something to me regularly, but I can't quite see that now. Maybe you have an idea.

Jonathan Chin:

You mean these AI technologies and how data will unfold in them?

Carl Lewis:

Yes.

Jonathan Chin:

At the individual level, there's going to be skill empowerment. Individuals who adopt this will accelerate their ability to be productive with quality. I use ChatGPT every day for my work, and it saves time. It can’t do all the work for me from an automation perspective, but it 100% saves me hours of work.

Some of my data engineers and UI engineers save time researching code. Nowadays, languages are so large, from a technical perspective, that people can’t know everything about them all. Still, they need to know the basics, and researching how to do something has always been part of the development cycle. With AI, our engineers say, "I just ask it for a function that does this and this and this. It writes it for me, and I fix it." And businesses can use it how professional individuals like us are using it: to be more efficient.

A small business can stay smaller longer with these technologies. I'm on the growth side, so I know marketing is an expensive function in a business. As you buy real estate to get eyeballs, it's very time-consuming to create content or ads. ChatGPT is a great way to augment a single employee dedicated to marketing and make them four times more efficient. It won't reduce marketing costs, but it might help you explore more opportunities while staying lean and reactive.

Carl Lewis:

But it's access to this data that gives it the power to do that for you.

Jonathan Chin:

Absolutely.

Carl Lewis:

You mentioned there's personal information, like the charges people make, and people worry everybody knows everything they’re doing and has access to all their data. But there are ways to keep people's private information private. Can you explain that?

Jonathan Chin:

That's a great point for data stewardship. At Facteus, we utilize a technology called synthetic data. It’s something we've developed as an application. It's not a concept or an industry we've created, but we have our own way of doing it. When we gather data from our partners—usually banks, processors, and credit card companies—the data's obfuscated. That means we've injected mathematical noise into the data, so transactions won’t look like the transactions on your bank statement. Dollar amounts, dates, times, etc. might be changed, so nothing could identify you or be reverse engineered.

The laws have never caught up to the technology, but they do incrementally grow and change and make progress. As far as DaaS, data privacy and data stewardship must catch up. So, we took the initiative to do that when we started the company because we didn't want to put ourselves in jeopardy.

We’ve seen other companies rise and fall, especially where cell phone location-tracking companies used data nefariously and inappropriately. That was something we never wanted to get caught in. I also think that excluded us by, because we added noise from different markets where people were monetizing data before us, we have just chosen that, even though those are big markets, mainly targeted marketing and advertising, there are more opportunities growing in what we're seeing today, and those are catching up.

Carl Lewis:

It’s great that you started doing that from day one. Everybody has their own angst about the data that's gathered, so I know people appreciate it. Many of us are worried about an Alexa in every room and that sort of thing. That's part of the technology picture we're still drawing.

Well, Jonathan, you’re part of a fascinating industry. Thanks for peeling the onion and explaining what you do at Facteus and why it’s relevant to us. As we look into the future, we'll understand a bit better having spoken with you today. So, thank you for joining me.

Jonathan Chin:

Thanks for having me. It's always great talking with you, Carl.

Carl Lewis:

You’re welcome. And thank you, everyone, for tuning in today, and we'll see you again on the next episode of The Connected Enterprise podcast.