Neda Nia is Chief Product Officer at Stibo Systems, a provider of enterprise master data management solutions. She started her career at Metro Supply Chain, where she went from a receptionist to a technical analyst and, later, a product manager. Neda then worked in product at Centennial Optical Limited and Catalina USA. She joined AppBuddy (acquired by Validity), co-founded her own startup, Caioty, and then transitioned to Contentserv before her current position at Stibo Systems.
In our conversation, Neda talks about how Stibo Systems has used AI to minimize liabilities around data ecosystems. She shares her experience building zero-to-one products in various verticals, as well as how she views the role of a product manager like a “trained pilot.”
A new momentum in master data management
Can you tell us a bit about your journey into product management and your current role at Stibo Systems?
I definitely didn’t plan to be a chief product officer. I’ve always had a passion for solving problems and helping users, so I went with the flow and ended up here. Stibo Systems is a master data management company, and we strongly believe that with technology, anyone can build the right products to solve user problems.
As a CPO, I see myself as a servant-leader to a fantastic team that includes product management, user experience, R&D, and cloud architecture.
In the context of Stibo Systems, what exactly is master data management software?
MDM, or master data management, is also often called unified data management or centralized data management by our customers. It’s a tool that helps companies manage the core data operations of their enterprise. This can be product, customer, or supplier data. The most important capabilities of MDM include data quality management, data governance, and workflow management around operations like data onboarding, data integration, and data syndication.
Typically, businesses and enterprises invest in MDM to have a reliable source of data for their enterprise teams and applications. But master data can be used in different ways, such as analytics, to drive better decisions. It can also be used to enrich product detail pages for a better shopping experience and drive conversion, or fuel prediction or recommendation engines because we know that with better and high-quality data, these engines produce precise outcomes.
With the rise of artificial intelligence, MDM has gained renewed momentum, which is exciting because data quality and governance capabilities are crucial to a successful AI strategy.
The transformation to a larger ecosystem
You mentioned AI’s influence on MDM. How has AI impacted your business model, and what does that evolution look like?
The software business and things like adtech, martech, and data management are always fast-moving. Innovation and progress have always been part of the process. Master data management is a well-established category, and with the introduction of AI, it has gained renewed interest from business users and technical teams. Specifically, enterprise AI, which includes sophisticated decision engines, has oxygenated our business model.
Since we are an enterprise provider, we see this renewed energy around MDM because companies want to invest in enterprise AI. They all expect certain returns and are well aware of the potential risk of wasting resources and lower ROIs if they don’t have high-quality, trustworthy data to train their AI models. They know that if they don’t have tools to validate the output of their AI models before those results get into their data ecosystem, it would be a liability. This is the exact problem that we solved. That’s why we have been seeing renewed energy in this category.
Our business model has shifted from being a data management tool to being a part of a larger AI-plus-data ecosystem for successful AI adoption. I find myself engaging more with distinguished analysts and leaders from our Fortune 100 customers. These interactions are becoming more like a partnership, extending beyond the typical vendor-analyst or vendor-customer relationships. And I enjoy it because this is a pivotal moment for data management, and it’s fascinating, humbling, and exciting at the same time.
Stibo Systems has changed a lot in your tenure. What were your department’s top two or three achievements this past year?
Working in Stibo’s product team has been demanding but rewarding. I’m grateful for the dedication that each individual brings to our product development teams. We have about 20 scrum teams, and our products are in different stages of their lifecycle. Success and milestones can vary by product and team, but every day, we achieve something, and each of these micro achievements deserves a celebration.
One of our recent accomplishments has been scaling up our SaaS products. We have also launched new products in the last 24 months. The scale has mattered, too. We have 600 customers, and in whatever we do, we have to cater to our existing customers while attracting new ones as well.
We’ve also had multiple AI-related POCs, one of which is a global fast-food chain. We are doing some auto-classification using AI. I’m proud of these achievements, but I don’t want to say that all the other things that we are doing don’t deserve a celebration. Each one of them is really wonderful and pushes us forward.
How do you plan on building on that success in the next few years?
My team is not here just to solve technical problems. At the end of the day, we are in the people business. Through mindful leadership and encouraging curiosity and humility to learn, we get to know our team members, amplify their strengths, and create an environment where everyone can grow and contribute. This mindset allows us to work with our customers, watch the market, understand what is going on, and then feel empowered and inspired to build great products.
That’s how we are going to guarantee our success in the future. We’ll remain curious and learn fast, fail fast, and build fast. It’s hard work, but that’s what leadership entails. We’re blessed with many wonderful customers who keep us on our toes. They have requests, they get exposed to issues we don’t typically get to see, and then we learn through them. So, mindful leadership and amplifying every individual’s strength will drive further innovation and make us future-proof.
Ongoing governance of enterprise data
As you launch new products to excited customers, how do you stay connected to their feedback?
Product managers have a unique advantage compared to other stakeholders because after we build products and customers use them, and we receive feedback directly from them. I encourage my team to have as many customer interactions as possible while being mindful of communication fatigue. We don’t create too much noise that could be distracting, so every customer interaction is logged in a tool.
How do you encourage your customers to learn as much as they can about the data they have access to through Stibo Systems, and how do you see AI as a tool to educate them?
AI can deliver great value. The main value we bring to our customers, no matter what industry they are in, is to reduce liabilities that an enterprise may run into for not having the right data. We help ensure that there’s an ongoing governance happening, regardless of how many business users or partners are contributing to the data management process.
AI has accelerated our radical, incremental product innovation. Say we have a customer that’s in consumer packaged goods or retail and is mature in what they are doing but are looking for efficiency. AI has supported our work on new ideas that may not be fully hashed out but are worth trying.
At Stibo Systems, we have a fixed budget for radical innovation and we understand that it may not have a similar ROI to incremental innovation. As an example, a couple of years ago, we used the Metaverse to show customers how to create digital shelves. It opened many doors to connect with our customers and brainstorm ideas that generate revenue. We built credibility with our customers, who know we can move fast, innovate, and deliver POC.
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Scaling products in different environments
Since you have been through the zero-to-one product growth experience, what would you say was the best learning from that?
Software products, like all other commodities, go through this lifecycle — introduction, growth, maturity, and decline. Each transition from introduction to growth and maturity is considered a significant milestone.
Years ago, I read the book Crossing the Chasm. It had this visual where you see different phases of the technology adoption cycle and the activities happening in each transition phase. I encourage everyone to read that book because it gave me a really good visual of how these milestones all differ. As a product manager, understanding your product’s current state matters. You have to have a clear vision and actionable roadmap for the next year or so. Having an idea of where the product should be in the long term, say in three years, is important.
You’ve worked in startups and more mature environments. What would you say is the biggest difference between those two product journeys?
Scaling products in a mature company differs from launching new products in a startup. Efficiency, automation, security, performance — putting priorities like these non-functional requirements on the back burner in a startup is harder. If you’re introducing a new product in a startup setting, they become priorities in the scale-up phase. And then, in this phase, the balance between incremental and radical innovation matters because you already have live customers.
They have specific expectations from you, and you likely have legal obligations to provide them with continuous improvements. You must also innovate to attract more customers if you want to grow. Balancing that with ensuring that whatever you’re introducing does not disrupt what you’ve already given to your customers is crucial in a startup environment.
Further, investors in the startup space typically expect steady, sustainable growth and are less tolerant of high-risk, rapid iteration strategies. That means you need to get better at negotiating with your customers or be comfortable saying no to new requests from your paying customers. You can technically build anything, but is it the right thing for your company? Is it going to contribute to your profitability, growth, or strategic direction that you have committed to as a product manager? This can be a challenging balancing adventure.
How have you dealt with that balance in the past? Do you lean on any specific frameworks?
Adopting a specific framework that is both internally and externally understood is helpful. One of the frameworks we’ve all used is agile. Frameworks like pragmatic marketing are also helpful because they encourage teams to unite on the same page. Although agile can have different flavors, it enables a common understanding of what is happening and why.
Product management frameworks encourage teams to validate ideas and ensure the investment is worthwhile. But the caveat is that whatever framework you adopt, it’s important not to become too religious about it. Purist practitioners will do well on paper, but at the end of the day, you have to deliver value. Who cares how you got there, what framework, and what processes you followed?
‘Product managers as trained pilots’
What exactly do you think is important for product managers to build skill in as they scale products?
You can manage both startup and scale-up products if you know product management. I see product managers as trained pilots. You’re a pilot, and your product is your plane. You should be equipped to take the plane through any weather conditions. You can’t wish someone else would take over when the weather is bad.
Sure, experience will matter, but that’s not an absolute necessity because regardless of experience, a trained pilot is expected to know how to operate the plane. You need to have a deep understanding of the controls you have in front of you and when, where, and how to use them. In the PM context, these controls could represent risk, budget, market demands, customer requirements, investor expectations, etc.
In the scale-up phase, you may become more risk-averse. What has helped me is awareness. Be aware of your situation and the products you’re managing. Besides awareness, curiosity, humility, reflecting on results, and commitment to continuous learning are also important. Just maintain the humility that you can actually be wrong, and you can fail. But correct your actions, and stand up and provide value.
I cannot emphasize enough that communication and collaboration with stakeholders matter. As a product manager, you should know what stakeholders expect from the product and have a collective awareness of what success looks like. Working with stakeholders will help you land the plane at the agreed-upon destination. You may get a couple of bumps along the way, and that’s perfectly OK, but it’s a team effort.