Ep. 17: Exploring Human-Centered Design with Yasir Drabu.

In this episode of The Elusive Consumer, we explore the intersection of technology, data, and human-centered design with Yasir Drabu, a visionary tech leader and immigrant entrepreneur. From his journey starting at the foothills of the Himalayas to building a successful software company in America, Yasir shares insights about creating software that truly serves humanity. We discover how data-driven strategies can be balanced with ethical considerations, the future of AI in business, and why empathy should be at the core of technical innovation.

Transcript

[00:00:00] Ellie Tehrani: welcome so much. Yes, sir. To our podcast, the elusive consumer today, we’re going to talk a little bit about the challenges that businesses face in reaching various consumer groups and also how data driven strategies can address those challenges.

[00:00:27] We’re also going to hear about companies like Taza and how they empower organizations to gain a competitive edge through these software solutions that you provide. But before all of that, We’d love to hear about your personal journey and how you got into this line of work,

[00:00:49] Yasir Drabu: I’ve been, you know been a computer geek all my life. So all the way to finishing my grads, the grad studies in computer science. So towards finishing grad school, many people

[00:00:59] would [00:01:00] ask for help. Yeah, six, my computer, our CD drive. I’m like, I’m not that person, but, but many people ask for help with software and it kind of, you know, it’s a, it’s a, it’s a, Was accidental.

[00:01:10] I was on my journey to become a professor, but found it more fun to actually build stuff. And then one thing led to another and got a couple of software contracts with a couple of companies to help them build what they were looking to build, like, Software platforms and things like that. And it kind of grew organically and after I finished grad school, I was like, okay, this is serious.

[00:01:34] Should I go find a corporate job or should I do this more seriously? And I, I took the latter route you know, new to America and started you know Like a typical immigrant and we kind of started with a couple of people and we’ve been growing ever since. It’s I haven’t looked back. It’s been fun because if you love what you do, you know, that’s the same, the same, which is very common.

[00:01:56] Ellie Tehrani: Right, right. And I, I love [00:02:00] your personal journey as well in terms of growing up at the foothills of the Himalayas embarking on this journey to the us. And as you sell, said yourself as an immigrant being, you know, you have to be resilient and you have to find. New ways of thinking and forming bonds with various different like minded people.

[00:02:22] How did you find that journey?

[00:02:25] Yasir Drabu: think you know for all that said about America in the current environment, it’s still an amazing

[00:02:31] place to, you know, Kind of common, complete, you know, there’s no,

[00:02:38] you know, there’s no what is that term nepotism and those kind of things that I got my first deal on a phone call and they liked what I had to say.

[00:02:45] And they said, alright, let’s do it. That’s very unlike. That’s very unlike something in India where you have to know somebody to get a deal sort of thing. So the journey was interesting. And I, you know, Kudos to the entrepreneurial spirit of the people [00:03:00] here. It’s a, it gave me a place where, even though my name is different and my accent is also a little off than your typical American, but it did.

[00:03:08] I don’t think that ever that’s ever been a impediment. I’ve had the pleasure of meeting some amazing people along the way who’ve helped me you know, build this company as well as you know, provide guidance, mentorship and open doors when needed. And without expecting anything much in return.

[00:03:26] So, so it’s, it’s, it’s been, there have been challenges, obviously, from as a, to grow a business, but in general, it’s been, it’s been amazing to come to a new country and meet so many people from so many different backgrounds and be able to work with them to build something.

[00:03:44] Ellie Tehrani: Right. And going back to that growing a business, you mentioned you’ve always been a computer geek, but how do you transition from being that enthusiast to starting your own company?

[00:03:56] Yasir Drabu: yeah, that’s I think the I think [00:04:00] the neck is to kind of translate. Right? So I think in computer science itself, you know, you have bits and bytes and hardware. So you’re always looking at what I call higher level abstraction. Right? So you’re trying to abstract away complexity with either analogies or you know, mental models and things like that that help you you know, mask that complexity.

[00:04:21] But if you take that a couple of notches up, which was I was lucky to make that transition, you’re able to take a business problem. Which you can then translate back to these levels, these abstractions and then, you know, when you can see how a keystroke can actually help a business on the other end, and you’re actually able to make that whole end to end connection you can then help play the translator between a business problem and a technology solution idea and kind of put together.

[00:04:49] So I think that was the 1st step of getting in that. I remember very interesting. Like in grad school, I, I joined as a computer science [00:05:00] person. Help build a program,

[00:05:01] which was pretty messy. I actually help them. I understood the problem and then help create a solution, which was used years later.

[00:05:09] Even after I left school, it was something I did in grad school, but it was helping them connect all their student data with admissions data. So they could open doors in the dorms and things like that. So they created, like, the centralized system which was pretty cool. So, I think that. Understanding of translating one idea into next is I think very vital to kind of help you make that leap from a computer geek to you know, a business business person.

[00:05:35] It’s, it’s been, it’s been challenging. I mean, you have to get off, let go of wanting to do it all yourself. So the delegation management you know, all those things take time. And I think. You, you end up learning a lot from other people about everything beyond just the technical abilities is, you know, how do you develop your emotional intelligence and connect with connect with people that, [00:06:00] you know, away from the screen actually talking to talking to people as they’re humans. And that’s, that’s what helped us build our company’s culture stack as well. So.

[00:06:09] Ellie Tehrani: Right. We’re going to touch upon the culture and growth mindset in a bit, but before that, you’ve said before that the thinking behind your company and Tessa was that humans deserve better software. What do you mean by that?

[00:06:26] Yasir Drabu: Yeah, so I think software has definitely gotten better, but when I started my journey, there was a lot of software, and there still is, which people kind of glue together with duct tape and a band-aid, try to make it work. Right? So, for example, you’re using the Riverside studio, but if you were a podcaster In the early days, you would, you would use some tools to record and then you would upload it somewhere.

[00:06:47] Then you would, you would have to do a lot of editing. It production was a very big challenge, but then, as, as things progressed, you know, and it became a mainstream it became an industry of thoughts for [00:07:00] creators tools like this kind of help do that. So the same thing you know, how do you how do you take away unnecessary.

[00:07:08] Pain to get things done, right? So one very common example, which I still very dislike is when you go to a doctor’s office. I’m sure you’v had this experience. They’re talking less to you and typing more into the computer. Right? So that’s a problem. I think we are very close to solving not me personally, but with the help of AI and some of the you know speech to text conversion tools and things like that will make that conversation so that the computer and the user interface kind of hides away.

[00:07:37] And it doesn’t impede the relationship. You’re trying to build with the, with the patient, right? That’s a very common use case where the UI becomes almost is taken away and the interaction is directly with your patient. So Similar philosophy in business and specific use cases where we, we are trying to see where, where there are pain points, where there’s a software, like we’re doing a lot of work in [00:08:00] FinTech, EdTech and other, other spaces where we get to work with some really amazing entrepreneurs.

[00:08:05] We partner with some of them and we see the problem they’re trying to solve and like, interesting. Nobody has solved it before and that’s what I meant by, you know, getting better at making it easier to do things. So you can kind of focus on the most creative aspect of being a human and removing all the things that are mundane and making it better so that you can every week and kind of reach their best potential.

[00:08:27] So our goal always has been to software has to make somebody’s life better.

[00:08:36] Ellie Tehrani: Of course, and better technology, allowing humans to be better humans and do better work themselves. As you said going back to the UI and the user experience, you also mentioned on your website that Taza has high empathy design. Talk us through that and what that means,

[00:08:57] Yasir Drabu: sure. So I think you, you, [00:09:00] you, you, I don’t know whether you’ve heard this or at least in, in, in engineering cycles, it’s like, it’s the user.

[00:09:07] Ellie Tehrani: right? Right.

[00:09:11] Yasir Drabu: I didn’t, I don’t know whether that needs to be edited out, but that’s usually, usually Usually, and I’ve seen a lot of engineers who are really smart, kind of roll their eyes when somebody’s struggling with something.

[00:09:24] They’re like,

[00:09:24] this is so straightforward. Why don’t they get it? I’m

[00:09:26] like, you need to take a step back and

[00:09:28] empathize with their needs and understand their user journey. Right? So I think,

[00:09:33] Even within

[00:09:34] The, within the company, we always say lead with empathy. You know, if other engineers are struggling, you kind of try to understand their point of view.

[00:09:40] They may become we have global

[00:09:42] teams. So they come from various cultures. You may not be able to express themselves in a certain language and stuff like that. So always lead with empathy. The same thing translates into UI and user user experience. Designers understand where the user’s pain point is. You know, you can’t simply say.

[00:09:57] Well, my software is the best. Why, why, why [00:10:00] don’t people know how to use it? It’s actually kind of work with them. Listen to them here. Their user journey. Some of these are actually X techniques, but and then use those powers of observation to see, okay, how do I make this easier for them? Where are they getting struggling?

[00:10:16] Sometimes it can be as simple thing as a prompt or, you know, what’s being said that it throws them off. Right? Versus sometimes just the screen is too busy or. Or the kind of interaction that you want to do is a little bit different. So, really having that walking in their shoe kind of approach is very important to,

[00:10:34] as the 1st foundational principle of building good software.

[00:10:37] So

[00:10:38] it all

[00:10:38] starts there.

[00:10:39] Ellie Tehrani: Of course, without the seamless user experience, how do you engage with the consumers? Ultimately? So you mentioned earlier also that you’ve worked with various different verticals from fintech to real estate, for instance, are there any commonalities that you’ve observed in terms of optimizing and also prioritizing what features to use?

[00:10:59] To [00:11:00] deliver maximum value to their customers.

[00:11:06] Yasir Drabu: Yeah there are, I mean, from an engineering standpoint, there definitely are like, you know, there’s some things that are common across, you know, how do you authenticate a user without confusing them to how do you send out email messages that are meaningful and transactional emails? How are they done properly?

[00:11:22] But and then you know, you also. I think what’s common, apart from the engineering aspect is how do you, how do you kind of approach that? How do you have that? What we call our design system approach where you, how do you understand what, what are the kind of questions that you’re asking? What are the kind of interactions that you’re doing with with your potential and customers?

[00:11:44] You truly need to understand who’s actually. your software, you know, sometimes you build it for the other CEOs, but they’re not the end users. Right? So some, their end users

[00:11:55] may be so far away from the CEO may think

[00:11:57] this is the best piece of software. And this is how

[00:11:59] [00:12:00] it should work. But then you actually go to validate

[00:12:02] that and you see it’s not true at all.

[00:12:04] Right? So so I think the commonality is to really start that journey from, from that End user, and then you will see there are some common patterns that emerge in terms of best practices. You know, how much, how you want the UI to be organized? How do you use color palettes? And how do you use a font and you know, typography and those kind of things they all have.

[00:12:30] Some commonality across the project, but the domains we work with are very specific and unique. For example, in ed tech, we’re doing with some amazing entrepreneurs in Austin. We’re working on a math manipulative. It’s completely a different user interface for kids who can take two coins and add and say Two, 10 cents make 20 cents.

[00:12:50] It’s a totally different user experience. And then on the other end, we’re working

[00:12:53] with a billing company that has this complex claim system, which, which, which is dealing with a huge data [00:13:00] set. So how do we show lots of data without confusing them? So it’s unique in that sense, but you know, the commonality is to understand the end user.

[00:13:08] And then from an engineering pattern standpoint, there are certain things that definitely are common. Hopefully that

[00:13:14] answers the question.

[00:13:15] Ellie Tehrani: No, it definitely does. But then the followup question to that is how do you understand the end user? How do you collect that data from them to get that insights?

[00:13:25] Yasir Drabu: Yeah. I think we 1 of the things that I when, when we were smaller, I used to do a lot of that myself. And what I, what I realized was to scale that we started building what internally what we call the product mindset. Right? And the product mindset is we are not your typical order takers. We, we actually don’t do work for really large companies, right?

[00:13:46] We don’t we don’t go after example, we are not doing maintenance on you know, somebody’s oracle database and stuff like that. We are actually the ones who help build products and a lot of startups or innovation teams [00:14:00] within large companies that want to do something innovative. So so to answer your you know, sorry, I lost my train of

[00:14:07] thought there.

[00:14:08] Ellie Tehrani: You were saying initially you collected that data and you gathered that insight yourself, and

[00:14:13] then as you

[00:14:14] scaled yeah,

[00:14:15] Yasir Drabu: we will we build the product mindset

[00:14:17] and then the, the product mindset

[00:14:20] eventually get to us creating a full vertical within the company where we bring in product managers. We coach them on that. And sometimes we bring in there there’s, you know, subject matter experts and then the user experience.

[00:14:34] People, so we started with empathy led design and product people kind of asking the right questions. What is the kind of questions you want to ask? So, before we even touch any software, which earlier on, we used to do very quickly, we have this whole thought process laid out on how do we discover what’s important and what’s not so that whole practice.

[00:14:52] Of that product mindset, there’s some amazing books, like inspired love and others written written about product management that guide. Some of [00:15:00] those practices internally, you know, we use some of those techniques. They’re not techniques. They’re more like guidelines on how to, how to be good at what you, you know and the, the, I think building that at scale is what we’re trying to do at any given time.

[00:15:14] We are, we are building. 10 to 20 different products, so we have various product teams doing that. And each 1 has its own kind of product ownership product mindset team that needs that. So

[00:15:28] did that answer your

[00:15:29] Ellie Tehrani: It does, yes, it does. And I, I wanna move on to the, in terms of how you shape your products and services further, how do you incorporate a data driven approach within your products and services to understand customers and help businesses make better decisions? Mm-Hmm.

[00:15:46] Yasir Drabu: I mean, you always start with a hypothesis, right? So you’re always starting with a guess. So initially, you don’t know, because you don’t have enough data. You don’t have enough users. So you say, okay, based on our initial preliminary market research, [00:16:00] we don’t get into analysis paralysis early on. We more believe in iterative development and constant feedback, because if you get in, we’ve done some analysis paralysis kind of projects where we keep trying to find more and more data, doing more market research.

[00:16:13] And one year later or two years later. We’ve not started on the product, but the market has moved on. Right? So the best thing to do is to not best thing. At least our approach is that you know, you, you iteratively make a hypothesis, run an experiment, right? So the hypothesis is okay, the user, there’s a problem in this space.

[00:16:33] There’s this gap in the market here, our market inefficiency. And then we build a product. We release it. We, we try to Talk to people and try to and immediately we start seeing whether is that a

[00:16:45] product market fit? Right? That’s I think all product. Everybody lives by. Is there a product market fit?

[00:16:51] Right? So and so once you find the product market fit by iterating and say, oh, this is what we talk to them and say, hey, they’re like, we don’t like this. This [00:17:00] doesn’t make sense to tweak it, tweak it, tweak it. And then we try to We let them once the product market fit starts to kind of take shape, that’s when we start collecting more and more data.

[00:17:11] Truly, you know, initially, you may have like early adopters helping you tweak that product market fit. Once that’s done, not really done as an, as Starts to shape better, we have more users and now we are looking at a lot of data. You know, we, we use Microsoft clarity. We use you know, various other tools to see how people are interacting with the software.

[00:17:31] We create heat maps. We have. People doing follow on surveys. Hey, did you like this feature? What did this confuse you? We have some of our business analysts, our product managers, reaching out to some of these end users, doing short surveys of questions. What did you like? What you did not like. So that’s direct feedback.

[00:17:47] We actually do demos or when we also take a lot of the support tickets that we get in from some of the products and we analyze them, Hey, and we get it. Once you give [00:18:00] you know, the. Platform to the end user, they’re not shy and sharing their opinions. So, so I’m sure, you know, a little bit for that, but and and they’re actually able to give us very concrete, meaningful feedback.

[00:18:16] And we’re like, wow, why didn’t we think of this? Even though we are in the product day and night. Some things are like subtle, but very, very nice improvements. And then what happens is you keep making these subtle improvements in the product. And then it reaches a tipping point where it becomes like, it almost fits like a glove or what, whatever the expression is.

[00:18:36] So you, you use all, you take all this Google analytics data, you, you know, click paths heat maps user surveys, your own you know, Exception logging on the application, there’s several different techniques that the team uses to collect data and then they try to analyze it and sit through it and then ultimately the direct

[00:18:59] feedback [00:19:00] during usage we get.

[00:19:01] So, we take all of that and say,

[00:19:03] okay. Here’s the things we need to fix and that keeps us so we have some teams doing long term features, which may take months to build and some teams doing rapid features in smaller cycles. That may take few days to do so the people who, you know, and users love that. If they give feedback on Monday and on Thursday, the new features out, they’re blown away.

[00:19:24] So, so, so keeping, keeping iteratively building the product market fit. By using all this data through, through its own journey, right? Each software has its own journey from hypothesis

[00:19:36] to early prototyping, to minimal viable product, to, you know, early product market fit, then refining it to meet and you know, meet the end user’s needs.

[00:19:46] And during all this, I think the discipline is equally important, right? You can’t, you can’t make everybody happy. You really need to focus on your core group of users. Who you can who you need to [00:20:00] build a product that’s a hundred percent fit. It may fit adjacent industries to a certain degree, but if you try to satisfy everybody, the product will ultimately dilute.

[00:20:09] So trying to keep that discipline of 10 good features versus a hundred average features is a very, very

[00:20:15] delicate,

[00:20:16] Ellie Tehrani: Yeah. Yeah.

[00:20:17] Yasir Drabu: Yeah.

[00:20:18] Ellie Tehrani: Absolutely. I can definitely relate. In terms of the core users then of Teza, is there any way you could describe them?

[00:20:29] Yasir Drabu: yes, I, I think we have a pretty good what we call our ideal customer profile or and what we, what we like to work with and what we don’t. So, our 1st, foremost is people who have an entrepreneurial

[00:20:43] spirit, whether that’s within a corporate or even whether it’s a startup, typically a funded startup, because obviously it’s very difficult to work with no budget.

[00:20:53] We try to be very. Capital efficient as a company, but it’s, you still need some capital to build you know, build what you’re trying to do. [00:21:00] So there’s a very clear I, you know,

[00:21:03] Picture of an end

[00:21:04] customer that we want to build. Ideally, it’s a second time

[00:21:06] entrepreneur who may have already had his first

[00:21:09] successful exit, and they know what it takes to build something because there’s that perception software building a product and a business around

[00:21:16] that product is, you know, it should all take like 10 years.

[00:21:19] 1 day or 2 days, we’ve, we’ve seen, we’ve heard all the stories. So I think the ideal customer is an entrepreneurial customer within a corporate, or who wants to either build something that is somewhat cutting edge, maybe not bleeding edge, or, or even taking an existing industry and trying to disrupt it by identifying either market gaps, market efficient inefficiencies in certain places.

[00:21:43] And we work with them to solve that problem. Problem and see if there’s a large enough addressable market that would make it, it would have a meaningful impact. Obviously, we always try when we build our 1st product for 20 years by 20 people. Now, we have products that are used by hundreds of thousands of people and [00:22:00] millions.

[00:22:00] I mean, the gold standard, obviously, is a Google, which billions of people use, but in terms of the impact they have on the world, our goal is to kind of at least help as many as we can along the way in a meaningful

[00:22:14] way. So.

[00:22:15] Ellie Tehrani: Okay. And in terms of the, ethics behind an organization that gathers such a large amount of data. I want to talk a little bit about that. We know that a lot of companies face this issue. How do you at Taza ensure ethical use of all that consumer data that you gather? And as the CTO, what, what sort of measures do you implement to respect user privacies while also maximizing the best use of that data?

[00:22:48] Yasir Drabu: I mean, it starts with a pretty strong you know information security policy and things like that. So we do understand GDPR and, you know, other. So, for example, in our [00:23:00] health care product, we are very, very strict. Right? So it, it, it has to comply to HIPAA. We have some HIPAA training within the within the company.

[00:23:09] We, you know, we limit access to certain areas. And if we have to analyze data, we are, we, as a company, don’t touch any of this data primarily because we are not, this is not of our interest. Right? So anything related to some PHI or PII, we actually don’t touch We are not an ad company, so we don’t actually want this data.

[00:23:28] And we, we try, we treat it as nuclear and we always try to anonymize it as much as possible. So our goal is not to, we are not interested in you know, selling ads to people are trying to build a profile around what they’ll what we’re trying to do is just focus on how they’re using the software and how can we make that experience better.

[00:23:49] So it gives us already a level of. Advantage that we don’t have to ethically. We don’t need this data. I mean, we don’t need it for business. So it’s an easier ethic to [00:24:00] follow, right? I think if we, if I were in the ad business, it would definitely be a much bigger challenge, right? So you want to build a more stronger profile and try to build the most personalized.

[00:24:10] Targeted ad so that you can get the maximum number of advertisers and stuff. In our case, thankfully, so far, we haven’t, we haven’t we are not in the advertising space and intentionally. So, to some degree, we, we really want to focus on areas where it, you know, it goes back to a basic principle that humans deserve better software.

[00:24:30] Ellie Tehrani: Mm hmm.

[00:24:32] Yasir Drabu: I, I know ad companies argue that, hey, we can do a better personalized ad so you can avoid the noise, but it comes with a lot of ethical dilemma of how much of that profile you’re building. So, in our case, if it’s not, you know, I, I think ads are important to connect businesses to users. So we never discount that.

[00:24:49] But from our point of view, that’s an area

[00:24:51] that we want to stay away from in general,

[00:24:54] because, you know,

[00:24:56] It doesn’t make somebody’s life better probably does, but, [00:25:00]

[00:25:00] you know, not an area

[00:25:01] of interest for us. So that that way, we can get that way. We can keep

[00:25:04] away from that. So so I think strong security policies, privacy policies coaching our team on how to respect.

[00:25:12] Even, even if we accidentally have people look at data, they, they know how to not deal with it just from an ethical training standpoint, right? It’s not only about software controls or locking up servers. And the key, the point should be if we leave everything open, nobody should touch anything.

[00:25:26] It’s like a safe neighborhood kind of, that’s, that’s the premise, but we also put controls in place. So that’s that way we end and we do work in areas where it doesn’t challenge our core business. So that way it makes the decision making easy. So so 1 of the 1 of my employees who’s been with me for almost 5, 6 years and he’s he always says this.

[00:25:48] He’s like, the reason I continue to work. It does is what was said on day 1 is still true today. I have never had to compromise my personal ethics to make any decisions if we have to do something for [00:26:00] something, we always do right by the customer. I have to never choose between doing what’s right for what’s right for the customer because we always first do right by the customer.

[00:26:08] And we believe everything else follows.

[00:26:09] So.

[00:26:10] Ellie Tehrani: That’s a brilliant culture to have at any organization. And further on that, having a beneficial aspect to humanity, I want to talk a little bit about the double bottom line products that Taza creates. Can you explain what that means and share some examples? Of

[00:26:31] Yasir Drabu: Sure, so I think the double bottom line is like our 1st, you know, if we, if we had to choose between 2 projects, if something has a double bottom line, 1 is sure it’s profitable, but it also helps a whole community to do something better. We would pick that versus, let’s say, something that’s fun to do, but may not.

[00:26:49] Not have that much of an impact. I, I, I don’t wanna give specific examples in this category because you, you, you need to think through, well, there may be benefits to everything, but generally [00:27:00] speaking you know, for example, the EdTech product, you know it, it provides access to simplified tools to kids younger children to really engage in mathematics in a way that,

[00:27:12] You know.

[00:27:13] It may be harder to

[00:27:14] do you know, because they don’t have the access to, like, a Montessori education or they, where they cannot.

[00:27:21] So it gives that kind of visual learning

[00:27:23] tools. So they do make money. They’re a profitable

[00:27:26] business. We help, we love building the technology

[00:27:29] and it makes. A lot of kids get

[00:27:32] ready for STEM kind of education.

[00:27:34] So that’s really a double bottom

[00:27:36] line experience, right? So same thing with our real

[00:27:40] estate tech product. It may seem very innocuous, but a lot of people are coming out of

[00:27:46] college and they don’t have a credit history. So we actually built a tool. If you pay your rent, we’ll automatically send that information, only the good parts.

[00:27:54] And if you have challenges, you can, you can work with your landlord so that you can continue building. Your credit [00:28:00] history. So as you get out of college, or you’re, you know, moving into the next phase, you already have, you start having a stronger credit history that may help you get loans, car loans, and actually get into society.

[00:28:11] And it’s a very small thing. I’m not saying that’s the only way they have. Sure. They have other ways to do it, but anything we can do to help, you know, has that positive impact. We, we love to do and That’s another example. There are a few others, but, you know there are some in health care and others, but I can keep going.

[00:28:28] It’s a, it’s a, it’s a thing that I can talk at

[00:28:31] length for.

[00:28:31] So,

[00:28:32] Ellie Tehrani: no, absolutely. It’s always great to hear about how companies, particularly in tech, are using data and their skill sets and expertise to try to do good, and it’s not necessarily the vertical that’s associated with trying to do good lately. So it’s, it’s very important, I think, for our listeners to hear these examples. So thank you for sharing those. I want to continue talking about. Consumer data and the [00:29:00] aspect of the future of what that might look like. In your opinion, what do you believe is the future of data and how will it impact businesses specifically for tech companies such as yours?

[00:29:14] Yasir Drabu: so I think you, you need to qualify what that data is. Right? So I think there’s parts of data that I personally think what’s what’s happened. Yeah. Again, I’m not in the direct consumer space, but what’s happened is there’s a lot of Consumer data that they have traded off for conveniences. Right? So we, we, we know the addictive and predictive habits of YouTube and tick tock and all of those.

[00:29:39] Right? So I, I personally think I actually was hoping, I mean, this is more of a, Tech idealist talking was hoping that at some point people take ownership of their own data, and they actually give permission to who they want to write. So you want to reverse that right now. All our data and our clicks and our behaviors is all stored on centralized [00:30:00] servers of let’s say, Google.

[00:30:01] I’m not picking on them. Everybody does that. It’s necessary as a How technology evolved and if you flip that and actually use something like crypto or you know, actually build, build what you call your, you know, data waltz and actually have everybody has their own personal data vault of all their behaviors and they share it on a permission basis.

[00:30:23] I mean, in a. I don’t know whether it’s in the near future, and I don’t know what competing interests will prevent that. But in an ideal world, as we get into into a world where fake data and other challenges are going to be very it’s best to. Protect protect that data in, in some sort of cryptographic form, whether it’s on a blockchain or something like that and give permissions only consciously.

[00:30:50] Otherwise, it’s not accessible. I mean, will it happen? I don’t know. But there are a lot of people pushing for those kind of initiatives, and there’s a lot of smart people working on it. I’m [00:31:00] sure somebody will figure it

[00:31:01] out. But I think in the future,

[00:31:03] I believe that’s the direction.

[00:31:07] hope that it goes and a user data

[00:31:09] isn’t, you know, freely available for a

[00:31:12] couple of bucks at one of the darker sites or, you know, those kinds of things. So there’s a lot to be

[00:31:18] done. There’s a lot of work to be done in general to protect customer data. I think it’s too, there’s a privacy policy on a website and that’s not enough to protect it. So I think a lot more needs to be done to protect it, especially with, you know, young adults and people who are getting acclimated to.

[00:31:38] You know, Internet and other things. I mean, it’s, it’s very destructive. What we, what we’re seeing happening with kids

[00:31:45] and everybody’s down on their phones and they’re, they’re, they’re been getting healthy doses of dopamine. But this is all based on the data that’s been, uh, gleaned off their behavior clicks and what they scroll, what they like, what they watch. [00:32:00] I think either regulation or technology and ideally a combination thereof should be put in place. To protect it. Sorry. It’s a long winded answer, but there’s a lot, a lot of work to be done because a lot of this data is being

[00:32:13] abused,

[00:32:14] Even though there seems to be some level of customer production, but it’s not, it’s not enough.

[00:32:21] Ellie Tehrani: on the flip side of that, then if you are handing the ownership sort of in an ideal world back to the consumers themselves, how do you think that would impact the companies and the way they’re doing business? So your customers in effect, and what would you need to do to help them better?

[00:32:41] Yasir Drabu: Yeah, that’s that’s you know, that’s, that’s something we’ve kind of actually had a couple of discussions on. I don’t think we have a deep enough or a perfect enough answer because it hasn’t happened yet. And usually when something is a future risk, you try to hedge. All right. So, but. I, but I think it will impact certain industries a lot more [00:33:00] than ours, right?

[00:33:00] In our case, as I said, we are not really looking at individual data. So, if, if something is somebody’s personal data, whether it’s their health records, it’s their, you know, online usage behavior, or how they’re interacting and with their friends and social circles, those are the areas where they have the weakest.

[00:33:19] And then you have, you know, Commerce behavior on things like Amazon and Shopify, which gets aggregated. So, you know, there’s how what they’re buying patterns in terms of we, we do some work in those spaces, but we are not targeting those data sets, but it will affect you know, those companies significantly because then they can make the right recommendations.

[00:33:36] There’s a, there’s a lot of limitations that it will pose and. The question then becomes is how, how does that, how does that get addressed in our case? I think seeking explicit user permissions. With clear benefits trade offs is the best way. I think as we move into that future, right? [00:34:00] Most of the world will be

[00:34:03] digitally savvy, right? Right now. We are still

[00:34:05] transitioning. We have people who have never had Internet and, you know, whether

[00:34:09] it’s our parents, grandparents, they don’t know. They keep.

[00:34:13] They keep sharing stuff, which they shouldn’t and they don’t know where the boundaries are and things like that. So we keep coaching them.

[00:34:19] I’m sure you may have had that experience with your grandparents or something. But but

[00:34:24] as we get into the newer generation, like the kids, like my son, he’s, he’s been on it Since he was 12 or 13, and he knows he actually doesn’t let me put my credit card in. It makes him worry. So it’s interesting.

[00:34:37] So they are, they’re a lot more aware. Right? So I’m, I’m assuming by the time these protections get stronger, most of the world will have strong digital literacy and understanding of what their data means versus, you know, what’s available. I think that will help shape some of the policies in the future because the newer generations will be digitally more.[00:35:00]

[00:35:00] Savvy than some of us who kind of, you know, came from a village and didn’t have computers till, till they went to, you know high school or something. So we look at things very differently compared to newer newer generation. So I think between the change of mindset in the future, there will be different paradigms that come into play is my, is my hope.

[00:35:21] Ellie Tehrani: Right. And your outlook on the future, talking about the future generations and them being more tech savvy and more aware and more cautious, it leads me to my follow up questions about all the latest buzzword that everyone’s talking about in terms of A. I. And machine learning. How do you envision A. I.

[00:35:42] Impacting the understanding of consumer behavior of business behavior?

[00:35:48] Yasir Drabu: So, I think 1 thing is for sure, there’s going to be a, there’s going to be a huge transformation of abundance in in the initial phases. Right? So, [00:36:00] there’s going to be nefarious elements in human nature can’t help it, but the general majority, assuming we still believe in humanity is there’s going to be the, the removal of Mundane tasks, right?

[00:36:12] There are a lot of things like we do in our daily life. I’m sure as a broadcaster, you’re like, I wish somebody could just take care of this mundane stuff. And I could focus on talking to guests or doing what, where your best and most productive and those things will be the first things that will get automated away.

[00:36:30] And that gives you more free time to be. More creative have be more present, hopefully, and the goal that that brings us to a phase of flourishing. So a lot of mundane tasks get automated and that does have some impact on jobs. It’s I think humanity will still manage to adapt and be able to transform.

[00:36:51] That’s that’s my optimistic view, but there, there is a darker side to that is, you know, lots of purpose and job impact on [00:37:00] on the later stages. I think. Mixing of this AI, like, right now you have large language models, like, obviously, being the most popular one, but there are Bard and many others that are out there.

[00:37:13] Mixing that with customer data is a really dangerous mix. Because Google has access to a lot of personal data and whether they like it or not, I think they know more about me than I do sometimes. Now, if they take BARD and give it access to that data, I think that barrier needs to be protected. I don’t know how, how that could get misused, right?

[00:37:34] We know, I’m sure they’re aware of it and they’ve been. Really good stewards of it and so, but there was in this race, somebody is

[00:37:44] going to make a mistake and taking personal data, like what they have and mixing it with AI without some checks and balances could lead to some very you know. It will lead to some [00:38:00] abuse of data is my, is my major concern.

[00:38:02] So AI in general will, it’s the genie’s out of the bottle, so to speak. And it’s going to do what it’s going to do, which I believe it’s, it’s going to help people free up from mundane things and get better and, you know, Protecting individual data from getting used by these models in ways that we cannot predict is definitely something that everybody should be advocating for.

[00:38:29] I think Sam Altman was right that we do need regulations, especially around and I think everybody, I mean, on, the, elon Musk, I think, is on the other side of paranoia, saying that, you know, it’s going to kill us all, but I think there’s a, there’s a fine balance there. I think protecting certain domains of personal information is very important or at least somehow making that vault and using it

[00:38:53] within the confines of that vault is equally important.

[00:38:58] That’s just me

[00:38:59] [00:39:00] speculating if we don’t know what 5 years look like.

[00:39:02] Ellie Tehrani: Absolutely. It’s so hard to tell. In terms of Taza, are you leveraging machine learning techniques at all at the moment?

[00:39:10] Yasir Drabu: Yes. absolutely. I mean we, we’ve been lucky to

[00:39:14] partner with some innovative customers. We’re starting with some very interesting use cases. And we’re trying to help several customers with areas that. Are not what they term as nonproductive mundane work, right? There’s transcription summarization to a few other ideas like that, where we can help automate some workflows using AI.

[00:39:34] And internally, we are using AI and guiding our code quality code analysis and things like that. We’ve already started using that almost. This was pre GDP. We’ve been doing it almost for nine months now. So so between between Between improving work efficiencies, and then helping some customers I think it’s going to be, it’s going to be an interesting few years.

[00:39:54] I have a lot of a lot of we’ve seen some really, interesting use cases is we don’t know where [00:40:00] it all kind of, it’s anybody’s guess if somebody says they know it. But I, I think everybody’s trying to kind of figure it out.

[00:40:07] Ellie Tehrani: Right. I want to go back to the topic of culture. You mentioned earlier in our conversation that you run your business in a very empathy first, empathy led manner. But I want to talk about having a data driven culture. How important do you think it is to have a data driven culture in a tech startup, for instance, and as a leader, how do you foster that mindset within your organization?

[00:40:35] Yasir Drabu: I think empathy lettuce. It is how humans behave. Right? But. Decisions cannot be made on that alone, so we have a huge initiative internally for the lack of creativity. We call it Jarvis, but but, but it essentially aggregates all our all our data, right? Whether it’s who’s working on what and why, and how long it takes.

[00:40:55] token, what’s the code quality. We have a lot of metrics on [00:41:00] engineering, on efficiencies and all that, that really helps us understand where we need to make course corrections in the business. You can not in this day and age run without data, right? Especially as I said, it’s the same thing like in product in early stages.

[00:41:14] We were just a 20 people company. We could manage as you kind of continue growing. You really, it’s really hard to. Keep an unbiased opinion of things, because, you know, I may know Ellie and I know she’s good at X and then I say, okay, all all the good work goes here that that creates nepotism that creates other organizational problems.

[00:41:37] So, we, we, we actually. Full day data and factual data as much as possible from people’s work activities. We, we run some agents to collect data. I, I run a monthly pulse survey through my chat, but asking random questions of people just to get a sense of what they’re working on, what they’re liking, what they’re not liking.

[00:41:58] Sometimes it’s [00:42:00] situational, sometimes it’s a long running trend, but over a period you see a trend, right? If you can’t constantly say, Hey, We feel emotionally burnt out, or we always feel overburdened. Then we know we had to do some course correction. Like in our last survey, we did, we did find a lot of feedback on work life balance.

[00:42:17] So I said we’re trying to basically tell people to stop sending emails after work. It, it, it almost creates this narrative that you have to work. After hours, which we don’t want, right? So if you, even if you want to send an email or you already have typed it up, schedule it to go out in the next morning.

[00:42:33] So that’s a small tweak we can make based on this kind of feedback, right? So you’re taking data, understanding the sentiment of your team, and then trying to make

[00:42:42] decisions that help help them cope with

[00:42:46] that. Right? We look at individual cases, and we look at macro trends within the organizations to see how we can then use that.

[00:42:54] So there was a lot of. Post covid, we start this how work from home and remote. It’s it’s [00:43:00] a challenge everywhere. Right? So we, we, instead of forcing and mandating, we said, alright, let’s, let’s start with something that gets them excited to come and interact with people. So we did a lot of you know.

[00:43:11] Competitions, you know, everything from in India, cricket here, golf, everything to kind of blend that in so that people can realize that it’s important to come together in person for, especially when you’re working on something as complex as software. You, you can work remotely some days, but you can’t work remotely forever.

[00:43:32] So we, we use data to help guide us and how we could kind of bring people together a little bit. So data is. Very important, it actually helps enhance the empathy roadmap as well, because you have the data to be find the right, you know right reasons to be empathetic. And you can see that. Okay, somebody is actually struggling somewhere and you use that data point to actually help them get through that difficult situation, for [00:44:00] example.

[00:44:00] So.

[00:44:01] Ellie Tehrani: Absolutely.

[00:44:02] Yasir Drabu: important.

[00:44:03] So

[00:44:03] Ellie Tehrani: Yes. And I can tell you’re incredibly data driven individual, both personally and professionally. But what advice would you give companies that haven’t yet leveraged data and insights to understand and engage with their customers and consumers? What steps should they take to start this data journey?

[00:44:23] Yasir Drabu: I would say always just like everything iteratively starts small, right? Ask, ask the first basic question that you really need an answer to, right? What is it? What did, what’s your first blind spot and get an answer to that? Don’t think of the most complex you know, dashboards and metrics and KPIs.

[00:44:42] Think of the most simple things. If, if you’re a small, I think for example, a small services company, it’s how many. What’s the utilization level or, you know, how many hours am I paying for versus how much am I getting paid? How many hours am I getting paid for? You should know that answer. Like, [00:45:00] you should not have to dig, you should always have it in front of you.

[00:45:02] So, you know, day by day, week by week, where that, where you need to make course corrections or micro corrections. It’s easier to, if you know it daily, you can correct it 365 times. Whereas, if you do it weekly, at least you have 50, if you do it monthly, you only have 12 corrections to make. So it’s too slow.

[00:45:20] So you need to find a way to start with the smallest, most important. Metric, and once you get, you understand what that’s doing to your business, you automatically build an appetite right for more. And, you know, if, if, if that’s something that’s you know not technically keep that technical capability does not exist within the company.

[00:45:41] Then I think there are a lot of data companies that can help you know We do some of that, but generally, I think there are companies that are more focused on just helping you understand your

[00:45:50] organizational metrics and draw them out. So you

[00:45:53] can kind of make meaningful decisions. So I would say start small and iteratively.

[00:45:58] Keep adding. Once you get [00:46:00] comfortable with some metrics, then add a few more at a few more, then you’ll kind of say oh, yeah. Is it utilization? Is it billing rate? Is it this? You know, then you can actually question. Am I with the right customer? Is this my right customer profile? Sometimes the customer outgrows you.

[00:46:15] Sometimes you outgrow the customer. So any kind of use data and other. Sometimes there are other software indicators, like number of escalations, you know, this customer, it needs a lot of handholding and they’re only like half a percent of our revenue. And we’re spending 10 percent of our resources on them, just using a very extreme example, but you can use data to make those decisions.

[00:46:38] Okay. We need to find them a better home where they find a better, you know, on the other end, you know, you may have a very important customer and you may not be paying enough attention to them and you, you may miss out on a growing that account data again, helps you, you know, are there between meetings and having customer success, people record the right kind of feedback.

[00:46:59] It’s, it’s, [00:47:00] it is, you can keep making the metrics more sophisticated to help you better drive you know, outcomes and decisions for the company.

[00:47:08] So.

[00:47:09] Ellie Tehrani: Absolutely. Thank you so much for that, Isia. We’re running out of time, but before we wrap up, is there anything that you’d like to bring up that we haven’t discussed? Mm

[00:47:20] Yasir Drabu: No, you’ve been, you’ve been terrible. So I think you know for there needs to be like

[00:47:26] since this is about data, you know, for the elusive customer ends based on how you use data strategies. I think it’s

[00:47:31] important to be a good steward of that data. you know, it’s a, a, no software can teach you, you know, you

[00:47:39] can put all the controls you want in place, but if the,

[00:47:42] you know, if the. If the. leadership team you know, doesn’t in, you know, cultivate that, as I said, you know, you should, you should be able to have the data lying around and nobody should look at it as the kind of culture. If it’s not, not [00:48:00] that I’m saying do that, but I’m saying the culture should be there and the leadership.

[00:48:02] So being a good steward of the data, having a very high ethical standards for dealing with that data and understanding, you know, if you, the more, you know, the, you know, it’s just like the more powerful you are, the more rich you are, the more responsibility you have same thing with the same thing with data.

[00:48:18] The more data you have, the more controls, responsibilities and coaching. I think that if we, if we stay on that track, then the business leaders can ensure that we don’t, we don’t we don’t create, ethical quagmires and, you know, all the, all the things that data gets abused for is at least kept to a minimum.

[00:48:42] Ellie Tehrani: Absolutely. No, I, I totally agree. And consumers can see through that and they will stay loyal to the companies that are in fact, ethical with their data. So it’s important to mention that. Thank you so much for your time today. It’s been brilliant. It’s been educational, and I [00:49:00] hope to learn more about you in the future and your professional and personal journey.

[00:49:05] Thank you again for giving us this time.

[00:49:08] Yasir Drabu: Thank you. Thank you so much for talking today.

[00:49:11]

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