Build Better Data Foundations with Dr. Irina Steenbeek

In this special edition of The Executive Outlook ,we had the pleasure of sitting down with Dr. Irina Steenbeek , Managing Director of Data Crossroads and the mind behind the O.R.A.N.G.E. Data Management Framework. During a conversation filled with insights and personal stories, Dr. Irina opened up about her journey, her approach to leadership, and how she’s shaping the future of data management and AI readiness.

Dr. Irina shared that her journey didn’t start with data at all. “My first professional background was in civil engineering,” she said, smiling. She even completed a PhD in reinforced concrete constructions. But life had a different path in store for her. 

She recounted that after civil engineering, she moved into the world of finance, earned her MBA, and worked in ERP implementation. This blend of engineering, finance, and technology slowly brought her into the world of data. “I started from scratch. I didn’t even know what data management was,” Dr. Irina admitted honestly.

According to her, her first project in data was exciting but challenging. She had to build a governance framework for a mid-sized company without having formal resources. No books. No guides. Just learning by doing. “That’s when everything started,” she said. 

Over time, she began noticing that most industry frameworks lacked clarity about how different data management capabilities worked together. That observation sparked something new. “I had to explain data management in a simple, structured way,” she shared. So, she created her own framework, the O.R.A.N.G.E. Data Management Framework.

“The O.R.A.N.G.E. Framework is really like a roadmap,” she explained. “It helps you manage data management capabilities in a way that actually works in real life—not just on paper.”

She described it as a practical guide comprising six steps: scoping the project, designing capabilities, planning their implementation, defining the method, measuring progress and maturity, and ensuring everything can scale. “Each part connects to the others,” she said. “Just like the pieces of an orange—it’s one fruit, but with many slices working together.”

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Unlike traditional models that often stay high-level, the Orange Framework is built from hands-on experience. “I didn’t read it in a book. I lived it,” she said. “It’s flexible. You can use it whether you’re just starting out or already running complex AI projects.”She also noted that it’s not just for big companies. “Even small teams can use it to bring clarity and structure. The goal is to avoid confusion and make sure every part of your data setup serves your business goals.”

Dr Irina said the name “Orange” started as a joke. “I had a feeling that when I compared industry guidelines, I compared peers with apples. The industry guidelines have quite different viewpoints on data management and governance. I was looking for an analogy to demonstrate my framework, which aimed to solve this issue: on one hand, bring the best practices in the industry, and also provide a straightforward, practical approach to setting a DM framework. The orange fruit is a hybrid; it doesn’t exist naturally. Just like my framework, it’s a mix of my practical experience and what already exists.” This framework can be applied to any data, metadata, and AI initiative.

When asked how a company should start building a strong data foundation, Dr. Irina’s answer was simple and powerful. “Forget about technology. First, think about your business needs,” she said. She further defined this as understanding your business drivers. What are you trying to solve? What does the business want to achieve? 

She continued by saying, “Only after you understand the business goals can you define the scope and choose the right technologies.”

Irina warned against the shiny object syndrome. “Just because a tech solution is popular doesn’t mean it’s the right fit,” she said. “You can solve the same problem with different technologies, but the results and the cost will be different.” 

Dr. Irina recounted a moment that shaped her coaching mindset. Years ago, a large international company came to her and said, “We have nothing, but we want everything in six months.” She didn’t laugh. She got to work. But instead of doing everything herself, she used a coaching approach.

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“Consultants come and go,” she explained. “But internal teams stay. They must know what’s been done so they can keep it going.” 

She spread light with her words when she said, “I pick people with zero experience and skill them up.”

Within six months, the company had established a governance framework, clear policies, and defined roles for core capabilities such as data modeling and architecture, data quality, governance, and metadata management. More importantly, the person who initially started the project as a solution architect went on to become the Director of Data Governance. That’s the power of coaching.

She also discussed another interesting challenge: assisting companies that struggled with analytics because their reports were slow or confusing. Her first question to such clients? “Do you know how many reports you have?” And often, the answer was “No.” 

She explained that many companies still depend on manual reports. These reports may look different but show the same key metrics with different values. Why? Because their data isn’t aligned. 

“Before building analytics or starting an AI initiative,” she said, “create a data management foundation.”

Dr. Irina also warned against skipping steps. She shared an example of a company that built an expensive analytics pipeline with streaming data and fancy models. But when she asked if they had a data dictionary, the answer was, “We didn’t come that far.” 

“How can you trust your results,” she asked, “if you don’t even know what your data means?”

According to her, a strong foundation doesn’t have to be complex. “Not all companies need the same kind of setup,” she added. “But everyone can start somewhere—even small steps can bring big improvements.” 

She spoke passionately about the link between AI and data management. “You cannot implement AI if your data foundation is weak,” she said. She even ran a LinkedIn poll and found that more companies think AI is a priority than data governance. “But AI can’t work without good data,” she said firmly. 

When discussing leadership in data, Dr. Irina pointed out three key qualities. 

First, she said, leaders need a helicopter view. “Most people know one part of data management. But a leader should see how all capabilities connect.” 

Second, she said communication is essential. “Sometimes people sit at the same table and still don’t understand each other,” she smiled. “If you can explain things in their language, they will help you.”

And third, she stressed the importance of being goal-oriented. “I don’t believe agile alone brings results. Combine smart project planning with agility,” she said. 

When asked about her many roles as a consultant, trainer, and author, Irina made it sound simple. “I always do things myself first. Then I create models and explain it in books and articles. Then I train, coach, and consult others,” she said. 

According to her, writing policies is part consulting, explaining models is training, and doing it together is coaching. “They all go hand in hand,” she smiled. 

Throughout the conversation, one thing stood out: Dr. Irina doesn’t just teach data management and governance. She builds people. She transforms teams. She brings clarity to chaos. And she does it with warmth, humor, and wisdom. 

She also highlighted how her framework has evolved to serve more than just one domain. “It’s not only about data management anymore,” she said. “It’s about AI readiness too.” Her framework includes guiding companies on how to plan and execute AI projects without having to backtrack to fix the basics. She explained that this is where many organizations go wrong by jumping ahead without grounding their initiatives in solid data practices.

Dr. Irina Steenbeek continued by saying that scalability and adaptability are the latest pillars in her model. She emphasized that frameworks must evolve in tandem with the company. “What works for a small startup won’t work for a multinational giant. But the model should help scale smartly,” she explained.

She also emphasized the human side of data transformation. According to her, “Technology is only as effective as the people using it. If your team isn’t prepared, no tool will save the day.” She added by sharing a personal observation: “In all my years of work, the companies that succeed are the ones that invest in their people—upskilling them, supporting them, and trusting them.” 

She ended our conversation with a hopeful message for data leaders everywhere: “Don’t wait for perfection. Start small. Build wisely. And always put your people at the center of change.” 

For anyone starting their journey in data or struggling with AI readiness, Dr. Irina’s story is a reminder, Start with your goals, build a strong base, coach your people, and then, and only then explore the tech.

Her O.R.A.N.G.E. Framework isn’t just a model. It’s a mindset.

Click here to discover more life stories and insights from leaders shaping the future of data and tech.