This week, Jessica sat down with Tendü Yogurtçu, the Chief Technology Officer of Precisely, which powers data integrity for more than 90 of the Fortune 100.
They discuss Tendü’s non-traditional perspective as a CTO: her leadership blends deep technical knowledge with a human-first mindset, and her approach to innovation is rooted in clarity, connection, and culture. Tendü also explains how to align technical transformation with business goals, how to create momentum in remote environments, and why innovation is only sustainable when it’s grounded in empathy.
She also shares her views on AI governance, inclusion in data design, and what it takes to move fast without breaking trust. Plus, how she’s building software – and also mentoring rising talent, closing equity gaps, and creating cultures where teams thrive.
About Tendü Yogurtçu
Tendü Yoğurtçu, Ph.D., is Precisely’s Chief Technology Officer (CTO). She has 25+ years of software industry experience, including extensive Big Data and Cloud knowledge. As CTO, Tendu directs the company’s technology strategy and innovation, leading all product research and development programs.
Tendü is a member of the Forbes Technology Council. She is also an advocate for STEM education for women and diversity, who was recognized as CTO of the Year at the prestigious 2019 Women in IT Awards, and recognized as an Outstanding Executive in Technology for 2018 by Advancing Women in Technology (AWT).
LinkedIn: https://www.linkedin.com/in/tenduyogurtcu
Jessica Kriegel:
Website: https://www.jessicakriegel.com/
LinkedIn – https://www.linkedin.com/in/jessicakriegel
Instagram: https://www.instagram.com/jess_kriegel/
Culture Partners:
Website: https://culturepartners.com/
LinkedIn: https://www.linkedin.com/company/culturepartners/
TRANSCRIPT
Dr. Jessica Kriegel: This week on Culture Leaders, I sat down with Tendu ysu, the Chief Technology Officer of precisely a company, powering data integrity for more than 90 of the Fortune 100 tendu perspective as A CTO is anything but traditional. Her leadership blends deep technical knowledge with a human first mindset, and her approach to innovation is rooted in clarity, connection, and culture. We talk about how to align technical transformation with business goals, how to create momentum and remote environments, and why innovation is only sustainable when it’s grounded in empathy. She also shares her views on AI governance, inclusion in data design, and what it really takes to move fast without breaking trust. And yes, she’s building software, but she’s just as focused on mentoring rising talent, closing equity gaps, and creating cultures where teams thrive. This is a conversation about building technology that actually serves people. Welcome to the podcast, tendu Yasu. Tendu ysu, welcome. My first question, as always is what’s your why?
Tendü Yogurtçu: Jessica? It’s so great to be here. Thank you for having me. My why is a combination of curiosity, innovation, and impact. I love learning. As a child, I was very curious and very open to learning. And as an adult, I am still following the same path exploring how emerging technologies can solve real world problems. But what keeps me going is the human side. Helping people tap into their strengths, guiding the organizational shifts that make innovation sustainable and finding new, sometimes unexpected paths forward at the end of the day to drive progress through people, through ideas and through meaningful solutions. And part of my why also comes from the nonprofit work I do, mentoring and advocating for women and sponsoring underrepresented groups in STEM and supporting education in underserved communities who gets to participate in the future we are building.
Dr. Jessica Kriegel: That’s interesting. So you don’t often hear chief technology officers say that their why is people, I mean, I feel like there’s a stereotype at a bare minimum where chief technology officers are the technical side of the house and then they have to partner with and figure out how to get people at the organization on board with what they’re doing. And sometimes that can be the biggest challenge. Interestingly, that’s actually one of the biggest growth areas for our company right now is digital adoption efforts failing because people aren’t on board. So how do you get people to embrace change when it’s coming at such a rapid pace? So did the technical side of you come first or the people side of you come first? I mean the chicken and the egg question, I guess for you as a leader.
Tendü Yogurtçu: It came together. I think I was always into technology, math and topics around that, but I also was very people oriented, both as a leader and as a technologist. So it’s a combination and you are absolutely right. How do you drive culture of innovation? How do you drive digital transformation through cultural change? Those are all starting with people. If you don’t bring the people on board, none of those actually is going to succeed. So precisely one of the things that I am focused is especially with the remote first environment, how do you bring clarity, connection, and culture together with clarity around purpose and impact? I work closely with the strategic customers to bring their real world challenges into the team’s view so that the people who are solving the problem, the engineers, the data scientists, the technology teams understand the business context. I think this is very important.
At the end of the day, we are solving problems to serve someone that matters to someone. That connection feels innovation. And the second piece is, to your point, strategic innovation is a team sport. We focus on rapid prototyping, fast fail experiments, exploration of new markets and emerging technologies, especially when you are remote, this keeps the team energized because they can see they are future focused. And with cross-functional teams owning and iterating on capabilities, there’s a bit of ownership feeling as well, and they championship that change. And then the third piece, the recognition, how do you create recognition and in remote environments, especially celebrate contributions. So we built formal programs for invention recognition, technical excellence, and peer to peer recognition. We drive hackathons, idea pitches and spotlight sessions to help everyone stay visible, inspired and connected. It really connects everyone to our values, openness, determination, individuality and collaboration. And everybody embraces that across the board.
Dr. Jessica Kriegel: I would love to dig into an example. I think a lot of people don’t understand the business context of technological transformations and the importance of culture in driving strategic innovation the way you’re talking about. Could we walk through an example for those that are listening?
Tendü Yogurtçu: Yes, of course. First of all, precisely has embraced the flexibility in the work environment. So we have 97% of our employees are in remote or hybrid roles.
We support in-person collaboration by offering temporary locations for team meetings, town halls and other company events. I will give an example, we had an enterprise customer which was challenged and they had major, major data quality issues and moreover, very mundane manual ways of creating data quality rules to clean their data. So our first step was to understand the customer challenge and we bring the team so that they can hear the business challenge and the impact and what the solution should look like. And then we bring the team together on almost like a workshop manner. Some might be virtual, some might be in the same room to brainstorm and come up with different ways of approaching this problem. Before even any research prototyping work happens and then people cross-functional people including data scientists, engineers, product managers, and the field teams strategic services. They will be collaborating on the solution so we can make sure the customer problem is solved.
So we connect with the customer biweekly every other week so we can showcase how we are progressing and validate the solution that we are building. So it is not the build as a technology idea, it’s built to serve their business needs and we make sure that it is designed in a way that will serve many other customers business needs as well. But key attributes are there. First we bring that cross-functional perspective in addition to customer’s input. Second, we collaborate cross-functionally. So it’s not that technologists or the engineers solving the problem or the data scientists creating a model. And third, we iterate over quite often, both through virtual gatherings or sometimes in-person workshops. So we can bring to market in a successful way. If we need partners, sometimes we will also involve them.
Dr. Jessica Kriegel: So I mean you’re doing in those workshops, what we’ve been doing with system implementers, for example, so we were working with a company Florida Power and Light, and they had just implemented ServiceNow a few years before and they were having service outages every single day. The customer success reps would be getting calls from clients saying, I want to change something about my account or whatever, and the system would be down multiple times a day.
So the customer success rep was literally getting out paper and saying, tell me your name, tell me your phone number, tell me your problem. I’ll call you back when the system’s back up and then we can submit a ticket for whatever your issue is, right? Meanwhile, it was not getting any of this information because the customer success reps were so used to the system being down that they weren’t even reporting it anymore. It was just like the system doesn’t work. So IT and customer success, we got them in a room together and we consider ourselves the marriage counselors of digital adoption. That’s great. So we get them together and we’re like, so it what’s going on? And they’re like, oh, we’re putting it into routine maintenance, which in their minds is just routine maintenance. It’s not a problem. But because the data was so, what’s the word?
It was imprecisely input. What they think is routine maintenance is actually taking the whole system out. And customer success is so used to it being this way that no one’s communicating. Everyone’s frustrated and we just got them in a room together and said, here’s what’s going on. Let’s take ownership, let’s take accountability. And they went from having multiple outages a day to they have a clock on the wall. Now that measures how long it’s been since the last outage and when I checked a couple of weeks ago, is it like 90 days? So often these challenges just people not talking to each other.
Tendü Yogurtçu: That’s a great example. I am a big believer of cross-functional teams and bringing different perspectives for solving a problem. It also increases the engagement for the employees
Dr. Jessica Kriegel: And it increase, in this case, the customer experience and the NPS score, and therefore who knows what other business outcomes or impacts it affects. So I’m curious when it comes to big data, data being inaccurate or messy, the quality of data is really the biggest problem I would imagine most enterprise companies have. We got lots of tools, we got lots of processing power. The issue is that the data itself is unreliable in some way, and that’s usually because of people not putting it incorrectly or not using the system accurately, right? I mean, how do you fix that when you come into a client and you realize that’s the issue or how do you prevent that from happening in the first place?
Tendü Yogurtçu: So for those of the audience who are not familiar with precisely empowers organizations across the world, including 93 or Fortune 100 with the trusted data, and we define trusted data as accurate, consistent, as well as contextualized data. We call it data integrity. And we do this Jessica, with a combination of software for all data management you can imagine from data integration, quality observability, governance, to also 400 created data sets as well as strategic services. Now, if we look at one of the recent research that we have actually sponsored through Drexel Lebo College of Applied ai, the research shows that 60% of organizations state AI is a key influence, which probably is higher by now, right? Every three to six months the adoption rate is increasing, and however, only 12% report that their data is sufficient quality and accessible for AI initiatives. So we are seeing this big gap, and it’s not just our survey. Gartner survey also has similar data points. What becomes very important? One, you have to start with the right data. What’s the right data for that business problem? As you said, when we are working with customers, one of the biggest challenges is that their data is in silos. Silos across different lines of business silos, across acquisitions. They made over the years silos across so many different platforms, data platforms on-prem in the cloud, some legacy, some new. So you need to bring all relevant data for that business problem, relevant in terms of demographics of your customers, regions, customer segments so that you can minimize the blind spots and the bias.
Imagine if you are looking for retail across your demographics and miss a major demographic, then you will create that bias in the outcome. So relevant data, right? Data is important. And the second part is thinking proactively about data quality and governance without waiting for issues to surface downstream, which means monitoring the health of data as the data is ingested and investing in the quality early enough. And we have seen this over probably more than a decade, some of the quality or governance programs in these organizations, large enterprises going across many years. But you need to really become a little bit agile in that and don’t think about the perfect quality or perfect governance across all your data. What is my data quality and governance requirements for a marketing campaign versus something financial credit reporting might be different. So you need to think about quality and governance within that context of the business problem and use the tools that will create the transparency across that data pipeline.
And then third in the gen AI and AI context is everything. It’s not just about the organization’s internal data. You need to also think about additional attributes so that you can have higher accuracy and reliable outcomes. What does that mean? For example, for an insurance company? Environmental factors matter for tracking the path of a hurricane and thinking about the potential number of claims they may receive or the propensity of customers who are currently not insured. Environment factors, property boundaries, geospatial context matters. We are seeing retail spending a lot of money in behavior data for consumer insights. That’s how the organization can reduce the risk and improve the performance of the AI systems. So the data becomes a very, very key foundation for making AI systems successful.
Dr. Jessica Kriegel: So it’s almost like you’re not selling a product around a particular type of data or solution. You’re saying we help you solve business challenges, whatever that is. And if you got to get familiar with how to collect weather systems, large data sets and put them into something that corresponds with insurance and risk liability assessments and location, et cetera, then you learn how to do that and you consult on that. Is that right? You’re basically consulting management consultants with data and software to expertise basically.
Tendü Yogurtçu: So it’s a combination. We have the software offered as a data integrity suite of services. They are interoperable capabilities for data integration, quality governance, observability and so on. And then we have the created data sets which can be combined or can be subscribed on its own.
And then the consultancy services as you described to operationalize these data programs because it’s often that the customer comes and says, I don’t know where to start with quality or governance. Can you help me because I have this cultural change I have to drive and I don’t know how to define the ownership of data.
Dr. Jessica Kriegel: So I remember around 20 years ago when I was early in my career, I was working for Taleo, which was an applicant tracking system company. And they were talking about they were the tip of the spear on big data when it came to the employee experience, applicant tracking, but then also onboarding and performance management eventually. And I remember the CEO giving a speech about big data. It was the first time I heard the phrase big data. And he gave an example of, we’re going to understand all of your employee base. We’re going to understand where their performance has started to go down. We’re going to correlate it with information about when their shares are about to vest and identify predictively who’s going to leave so that you can jump ahead of it and either do a dive and save, offer them more money or get rid of them or hire before they leave, whatever. And I remember thinking in that time, that sounds really cool. It also is a little bit creepy, the amount of intelligence that can be gotten, so to speak, from big data sets and predictive analytics and all of that. So with the governance piece, I’m interested in the ethics conversations that are being had or can you give an example of where you have to thread the needle carefully when it comes to big data?
Tendü Yogurtçu: I think we briefly touched on the bias piece, right? If for whatever is the business context is if we are not bringing all representation and relevant data, we are creating biases and diversity impacts AI systems at every level, starting with the data. We like to think of data as being impartial. However, the truth is that the human biases create data biases too. With the growth of AI adoption, we are actually seeing there are some public cases that were shared creating real world issues from impaired facial recognition software that less accurately identifies women and people of color. There are actually many examples of that. Also, inequities in healthcare provision. Some examples on credit scoring for women versus men, gender specific bias. So we are seeing quite a lot of examples of this, which makes it really important both for training these models that we need to make sure that the full range of people, behaviors and contexts are included, but it goes beyond data. The teams designing, developing and deploying. These AI systems need to be diverse too. If everyone building the system thinks the same way or share the same background, then they will mis these and it’ll create risks and it’ll be missed opportunities. And we see the reverse being true as well because the AI systems influence the society. They shape what is recommended and who gets access to services, who gets credit, high scores for mortgage loans and how people are categorized. So that’s why inclusion becomes very important and oversight also becomes very strategic.
Dr. Jessica Kriegel: I’m wondering if you’re seeing a change in the risk tolerance that leaders have when it comes to AI and technology right now. For example, I was looking at the AI summit at the beginning of the year. It was JD Vance’s first public appearance global stage. It was in Paris, right? Emmanuel Macron hosted all the world leaders to talk about ai. And the message all week long was loud and clear, we need to deregulate, we need to go harder, we need to stop being so worried about ai. Whereas the previous year and the year before and the year before that was all about Wait, wait, wait. We need to be careful with ai. What’s the ethics of ai? What’s the risk of ai? Are they following the lead of the Fortune 100 leaders that you’re working with or are the leaders following the global political leaders leader? Is everyone just getting more comfortable with AI now so they’re less worried about the risk? What are you noticing in corporations?
Tendü Yogurtçu: It really varies. Some organizations are more risk tolerant than others. Highly regulated industries actually have started surprisingly ahead of some other industries because they have been governing their data better than others for a long time. They were able to actually embrace some of internal back office use cases for procurement audits, even agent AI use cases we are seeing in some of these back office functions, but the risk tolerance still varies. Every week we are getting some questionnaire from customers that actually asks, are you using AI in your products? Are you going to give me opt-in, opt out? This is why we had to make this explicit opt-in for our customers. And in the case of genai, we are actually offering them a choice to bring their own credentials for whichever models that they want to use so that they can have peace of mind and mitigate those risks.
So we are putting some of these guardrails across our products to support that varying risk tolerance. When it comes to your question about are they following these more geopolitical sites versus regulations, it’s a mix. EU AI safety act was actually more pragmatic than the GDPR, which we have seen that was quite kind of slowing down for people in their data initiatives. However, we didn’t see EU AI safety Act being embraced by all countries. Now, every country is coming up with their own regulations, even in United States, states are coming up with their own guidelines, which is going to cause more confusion. And that’s where we are likely to see innovation shifting geographically because of what regulations dictate, in my view.
Dr. Jessica Kriegel: Yeah, I mean, I remember when I was at Oracle, we used to do nine box grid evaluations of employees to identify top talent, and none of the German employees were ever considered top talent. Why is that? Because of GDPR, all of the regulations around what you could and couldn’t do with their data. And then suddenly the company didn’t say, well, because we have this strict regulation in this region, we’re going to slow down over here. They just said, okay, fine. Cut them out and keep going. I mean, that’s a whole other ethics consideration that these global organizations that you’re working with have to consider, which makes it even more complicated. I’m wondering what you think the future is for big data for ai. I mean, what is the thing that you’re not doing now with any of your clients that three years from now you will be doing?
Tendü Yogurtçu: I think data and AI is converging. People still see them as neighbors, but we will be talking about data and AI together in pretty much every context in my view. And right now we are seeing a big hype with agents to the point that every company is calling their chat bots or assistance agents. So there’s a agentic AI washing going on right now. I do however, belief that AI agents are going to change the way people interact with technology and with software, and we’ll almost enable people who are not as data and AI literate to be mastering the tools and the solutions. So I think two to three years, I think we are going to see agent AI changing how we use software and tools dramatically and how we manage data.
Dr. Jessica Kriegel: Yeah. I’m curious, this is an organizational question. In your role as chief technology officer, when you look across the table at the other executives at precisely, and I’m going to use hyperbolic language here, so just go with me on it. Who’s your best friend and who’s your worst enemy talking organizationally, right? When it comes to you accomplishing your objectives for the organization, where do you usually see roadblocks and where do you have a partner in driving success?
Tendü Yogurtçu: Best friend is the chief product officer, and I don’t have an enemy because I think because of the culture we created, we partner very closely. Even when we go into, for example, our weekly calls with the leadership calls, we always have partners from finance, hr. So we think as a team, we don’t think about CPO team, we don’t think about engineering team, we don’t think about CTO team. We think as a broader team that is inclusive of finance, hr, it so we can achieve our goals. So I wouldn’t call anyone enemy in that sense. They’re all partners and we share the common goal, and that’s the only way we can achieve that common goal. So we don’t think about my goal versus their goal. It’s our common goal that comes from CEO down, and we have to make sure that we are progressing towards that.
Dr. Jessica Kriegel: Yeah. Is it a techie company? I mean, it seems like a silly question, but there are techie companies that are not that techie, and then there are techie companies that are very techie. How would you describe the culture?
Tendü Yogurtçu: What do you mean by techie company?
Dr. Jessica Kriegel: I mean, at Oracle for example, I would argue that that’s a techie company because Larry Ellison is the chief technology officer. So as decisions are being made at the top level, it really all goes first and foremost through the technology lens as opposed to the sales and revenue lens or the human resources lens, where other organizations, maybe they’re in the tech space, but they’re driven by something other than the tech when it comes to their culture and their purpose.
Tendü Yogurtçu: We are a very product driven company, but sales and marketing, I would say will be equally balanced in that. It’s almost like technology, sales, marketing triangle led
Dr. Jessica Kriegel: The trifecta.
Tendü Yogurtçu: Yeah, trifecta. Exactly.
Dr. Jessica Kriegel: That’s great. Okay, so one last question I have for you that is my favorite question is what is something that you don’t get asked about very often in these types of interviews that you wish you were asked more often?
Tendü Yogurtçu: So Jessica, first of all, nobody asked What is your why in general? And you asked that already. Check. Yeah, check. I think one of the things, what’s the strength people often overlook in you. People don’t ask. And they often see me as a strategic or technical technology person who leads innovation, but what they don’t always notice is how much I invest in understanding people where their energy is, how they think, what they need to try. That’s often where real innovation starts and also is key to building high performing leaders for the organization. I think that’s one question people often don’t think about.
Dr. Jessica Kriegel: Yeah. Let me ask a terrible question as a follow up to that. I mean, do you think that being a woman in tech has something to do with that, that you’re not so black and white in your thinking? I mean, I’m totally stereotyping right now, but you do a lot of volunteer work in women in stem. I’m wondering if you’ve seen that emerge as a theme?
Tendü Yogurtçu: It might be related to, I was the co-founder for precisely in tech program. In fact, a couple of years ago I co-founded with another sales leader, and our goal was to have this network where we can also mentor and advocate for each other. We welcome men also, by the way, because men have to open the seat at the table often for women and also give some training tools, executive shadowing, et cetera for women in early their careers. And I think I have been always on the more empathy lead with empathy and understanding others and putting myself into their shoes so that we can come to table with curiosity and not with judgment, and that creates that inclusion and also bringing diverse perspective, which in my opinion, always ends with a better business outcome.
Dr. Jessica Kriegel: Well, what a beautiful way to end the podcast. Thank you so much for coming onto the show and sharing your expertise with me. You’re such an inspiration. I mean, really appreciate you sharing your insights with the listeners and thank you for coming.
Tendü Yogurtçu: Thank you, Jessica, and I enjoyed our conversation very much as well.
