Matteo Panero: Dear Mikiya, NTT DATA is a leading Japanese multinational in the IT and AI sectors, serving a diverse global customer base. I’d like to explore with you some of the key elements that characterize NTT DATA’s approach to developing products and solutions, particularly those involving advanced mathematical models and machine learning. But before we dive into specifics, could you please introduce yourself?
Mikiya Tanizawa: Thank you. I’m Mikiya Tanizawa, a deputy manager at NTT DATA Group Corporation, where I work closely with multidisciplinary teams across our global organization to deliver innovative technologies to our customers to create co-R&D.
Matteo Panero: Could you share some examples of model-based AI solutions or products that NOR is currently developing or has already introduced?[MT1]
Mikiya Tanizawa: Certainly. There are numerous areas of focus, each influenced by regional trends. A common global priority is revenue and pricing optimization, which has gained significant attention, as highlighted in Gartner reports. Additionally, large language models (LLMs) have become a pivotal technology, with applications across multiple sectors. At NTT DATA, we’re interested in integrating these technologies into other tools like math-optimization solvers. These solvers power everything from supply chain efficiency to pricing strategies, ensuring optimization at scale.
Matteo Panero: That’s intriguing. Can you elaborate on the key elements of your pricing optimization solutions?
Mikiya Tanizawa: Absolutely. Pricing is a strategic lever for retailers—it defines their market position, shapes customer relationships, and influences competitiveness. At the same time, pricing is a highly operational challenge. Retailers must update prices for thousands of products while monitoring competitors and managing dynamic promotions.
To address this, we’ve developed pricing decision-making technologies that bridge strategic and operational needs. It enables top managers to set high-level strategies while allowing day-to-day operations to implement those strategies consistently. For instance, a retailer might prioritize customer acquisition for specific products while maintaining profitability in others.
Our technology employs predictive optimization, where forecasting is crucial. It’s also an “explainable optimization tool,” emphasizing transparency. Users often find machine learning and mathematical optimization models opaque, but we ensure the scenarios and proposed solutions are clear and actionable.
Imagine managing thousands of products, each with unique costs, customer behaviors, substitutes, and competing offerings. Add the dynamic nature of pricing in e-commerce, and the complexity becomes clear. Our models help navigate this complexity while ensuring coherence with the retailer’s strategy.
Matteo Panero: It sounds sophisticated. What challenges do retailers face when selecting and adopting these kinds of solutions?
Mikiya Tanizawa: One major challenge is comparing solutions in the market. Many deep-tech aspects—like how forecasts are generated or elasticity is modeled—are not immediately apparent but greatly influence the quality and usability of the solution. Additionally, the rollout time and the learning curve for users to master these tools can vary significantly.
Retailers also face unique internal hurdles, such as varying data quality or organizational cultures. That’s why, during our sales process, we engage a consulting team to work closely with customers. We help them define priorities, analyze their data, and ensure the solution aligns with their specific needs and characteristics.
Matteo Panero: You mentioned that NTT DATA also has a math-optimization solver and is developing an LLM-based solution. Could you elaborate on these?
Mikiya Tanizawa:: Certainly. Our solver, Nuorium Optimizer, is a key component of our pricing and revenue optimization tools. It’s a robust engine developed by NTT DATA Mathematical Systems Inc., which has a lot of data scientists and engineers. Nuorium Optimizer delivers the computational power behind our optimization models. Utilizing the solver, by controlling order and production volumes, we were able to maximize profits while reducing food waste in Japan. In addition, we are integrating the solver into Syntphony Pricing Management, which is developed by NTT DATA Italia and has several commercial performances.
Regarding LLMs, NTT DATA launched Tsuzumi through the Microsoft Azure AI Models-as-a-Service (MaaS) offering, which is a Large Language Model (LLM) with robust capabilities in Japanese and English. We’re actively developing applications that enhance user experiences. For example, within our pricing solutions, we’ve proposed a great solution such that LLMs can act as intelligent assistants, helping users configure optimization parameters or quickly extract data insights. This adds a layer of usability and efficiency to our tools.
Matteo Panero: NTT DATA operates on a global scale, merging technologies and expertise from diverse teams. How has that experience been for you?
Mikiya Tanizawa: It’s both challenging and incredibly rewarding. Combining complex technologies with the insights and expertise of multicultural teams requires careful collaboration. But when it all comes together in a successful solution, the sense of achievement is unparalleled. In one word, it’s exciting.
Matteo Panero: Thank you, Mikiya, for sharing such valuable insights into NTT DATA’s innovative approaches and technologies. It’s been a pleasure exploring your work. We look forward to continuing the conversation and invite you to join us at ISC 2025, organized by Decision Science Alliance, where these topics will take center stage. See you there!
