AiThority Interview with Tendü Yogurtçu, CTO at Precisely

Tendü Yogurtçu, PhD, CTO at Precisely highlights key takeaways from the Gartner Data & Analytics Summit in London, innovations in data governance to meet generative AI challenges, common misconceptions about AI and more in this AiThority chat…
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Hi Tendu, welcome to our AiThority Interview Series. Please tell us about your journey so far.

I have always had an interest in computers, which led me to complete my undergrad in computer engineering and my masters in industrial engineering, focused mostly on object-oriented programming and analytics and design. I had my first two jobs in the field while still living in Turkey – at Apple and then creating a startup with two of my colleagues from undergrad.

I then moved to the US to pursue a PhD in computer science. While at university, I worked in an independent research institute, where I learned how to bring everyone from different disciplines together. As I finished my PhD program, I joined Precisely (originally founded as Syncsort back in 1968) as part of their new product development incubation team, focused on helping customers get their data out of mainframe environments and benefit from advanced analytics that next-generation big data and cloud platforms provide.

The company soon saw the opportunity to expand into big data and set about evolving the mainframe business into one of the broadest and deepest data management portfolios in the industry. Josh Rogers (CEO) and I presented a business plan to the board, which kicked off the company’s major growth and new products, and my career. I eventually moved into the CTO role and built Precisely labs for the incubation of new technologies, focusing on customers and partners, and supporting strategic acquisitions. Today, Precisely is the global leader in data integrity, responsible for equipping customers all around the world (including 99 of the Fortune 100!) with the accurate, consistent, and contextual data needed to make confident business decisions.

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You were a part of the Gartner Data & Analytics Summit in London. Share three key takeaways from it!

I had a great week at the Gartner Data & Analytics Summit in London – and particularly enjoyed having the opportunity to connect with our customers, partners, analysts, and colleagues in person!

There were a few key takeaways that stood out most to me. Firstly, how critical it is for data leaders to deeply understand their business’s specific use cases, prioritize them effectively, and be able to clearly communicate the value being delivered by their data initiatives. This ensures that data strategies are aligned with and help drive business objectives.

Next, all initiatives, including generative AI applications and new architectures like data fabric, require a solid foundation of data and metadata. High-quality, well-organized data is essential for the successful implementation and performance of these advanced technologies.

Lastly, leaders can gain business support by quickly demonstrating the tangible value of data initiatives. Achieving early successes, showcasing that their impact builds momentum, and continually reiterating these initiatives further strengthens business confidence and support.

Who are your customers and how do they leverage your products/services?

We help over 12,000 customers in more than 100 countries to make more confident decisions based on data that’s trusted to have optimal accuracy, consistency, and context – achieved through our unique combination of data integrity software, data enrichment, and data strategy services.

We pride ourselves on delivering continuous innovation fueled by the conversations we are having with our customers every day. We listen to organizations across industries to understand their business challenges so we can evolve right alongside them. As a result, we have customers across financial services, insurance, retail, telecommunications, government, and more who rely on our portfolio of data integration, data governance and quality, location intelligence and data enrichment solutions to build trusted data for AI, advanced analytics, and other critical business initiatives.

A great example is Generali Real Estate, a customer of ours who I recently had the pleasure of presenting alongside at the Gartner Data & Analytics Summit in London. As a leading global real estate asset manager, the team relies on Precisely data to enhance their City Forward platform, enabling real estate investment managers to make smarter decisions powered by AI-driven insights. Fueling AI/ML models with contextually rich data, such as information on business locations, leisure hot spots, and other geographic features, allows for highly accurate investment analysis and real estate valuation, including sustainability factors like carbon emissions and climate action.

Also Read: The Role of AI in Enhancing Digital Accessibility

Precisely has been recognized by CRN’s 2024 Big Data 100 list. How has AI helped to innovate, personalize, and drive sustainable returns in a complex business environment?

We have seen a shift in the last 12-18 months to the AI conversation now becoming a business-wide conversation. It is a discussion that is no longer limited to the data teams, data science teams, or technology teams – it has become a C-Suite and board-level priority. Organizations are increasingly seeing the value in how AI can fuel efficient and personalized chatbots, provide recommendations for tailored content creation, drive machine learning applications for faster and more accurate business processes, and so much more.

But while artificial intelligence is everywhere these days, the truth is, very few organizations have AI-ready data in place to power successful outcomes. This year, we expect organizations to continue implementing traditional AI to increase efficiency in both the back and front office, while exploring how generative AI can create new capabilities, products, and services. In the process, businesses will increasingly realize that trustworthy data is central to their AI success.

Think of it this way: the reliability of your AI outputs depends directly on the data that feeds them. To enhance the accuracy of AI outcomes and minimize bias, organizations should aim to integrate all relevant and critical on-premises and cloud data, including complex data on mainframes, into the datasets used to train their AI models.

Additionally, data needs to meet rigorous quality metrics; ensuring it is accurate, complete, validly structured, standardized, and free of duplicates. It must be timely, governed using a robust framework, and observed for changes and anomalies. Lastly, data that is augmented by trusted third-party data and spatial insights helps unlock rich context – ensuring more relevant and nuanced responses.

As generative AI continues to evolve, data integrity will play a pivotal role in elevating AI initiatives to new heights and delivering trustworthy and dependable results that propel businesses toward success.

How should data governance evolve to meet the generative AI challenge?

Today, organizations need a data governance program as part of a robust data integrity strategy that aligns people, processes, and technology, to help them understand their data with the goal of transforming it into an enterprise asset. A decade ago, data governance was fundamentally a technical undertaking centered on compliance. These functions were often performed by the IT department, and the primary purpose was to improve the quality of internal data for use by specifically identified teams. With the accelerated digital transformation and increased adoption of AI, data has become the most vital corporate asset. Therefore, it is essential that data governance becomes an enterprise-wide priority.

I see governance as a “reinvent yourself” state because we have seen a transformation in data quality compared to 10 years ago and now need to think about governance of data and AI models together. Data governance plays a key role because when you think about AI governance, you have to think about where that data is sourced from and what’s happening to that data before it is fed into your AI models. From ensuring quality and compliance of generative AI output to tracking the lineage of how these decisions are made, transparency of automation is critical for ensuring compliance and competitiveness.

What are the most common misconceptions about AI within your industry, and how do you address them?

Up to this point, there’s been no shortage of AI issues that have made headlines in the industry, including: AI hallucinations, “forgetting” the data it’s trained on, business chatbots recommending a competitor’s product, and AI-written briefs containing false information and fake citations. These are all real-world examples of the adverse impacts of poor data practices on your AI results, regardless of the use case or industry.

As AI technologies evolve, ensuring their success means committing to data integrity from the start. Organizations are in control of the proprietary data they feed into their AI models. This makes data integrity a priority to avoid the pitfalls of “garbage in, garbage out” and ensure AI solutions powerful and reliable. Regardless of your use case, investments in AI will only pay off if your AI systems are built on a foundation of trusted data.

For leading AI companies, women are under-represented throughout the industry. What are your comments?

The data doesn’t lie. Research has shown that during COVID-19, 50% of women were leaving roles in tech by the age of 35. These numbers are scary, but we’ve also seen amazing technological innovation emerge – the explosion of automation and AI, accelerated cloud adoption, and more remote technology jobs. These have created more opportunities for women in the post-pandemic workforce; we need to enable more women to take advantage of these opportunities by investing in digital skills.

My advice to women and anyone who is looking to progress their career in technology is to keep an eye on the emerging trends. There’s currently a skills shortage in many areas, including AI/ML, data science, data engineering, cybersecurity, and cloud. STEM education, specifically technology education, is becoming critical to help women to stay in the workforce. Businesses and educational institutions need to better collaborate and invest in women’s education in technology and digital skills.

Additionally, it’s important to focus on giving back. Pay it forward and support your community – whether that community is ethnicity-based, your alma mater, or your local neighborhood. It’s our shared responsibility to create a more diverse and inclusive environment for the next generation – including the next generation of technologists – and use emerging technologies such as AI to create a more inclusive future.

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