Stibo Systems provides master data management technology designed to help organizations govern and connect critical enterprise data. Its STEP platform supports multi-domain master data across areas including customers, products, policies, accounts, entities, brokers, counterparties, consent, locations, hierarchies, and relationships. Pulse 2.0 interviewed Stibo Systems FSI Industry Practice Lead Mark Blake to learn more.
Mark Blake’s Background And Responsibilities
When asked about his background and primary responsibilities at Stibo Systems, Blake shared:
I have worked in Banking, Financial Services and Insurance for 20+ years, having worked across digital and data-led transformation environments. My experience gives me a practical understanding of the pressures facing regulated firms: improving customer outcomes, modernizing legacy data foundations, supporting compliance, reducing operational friction and creating trusted data for better decision-making.
I lead the Banking, Financial Services and Insurance industry practice at Stibo Systems. My role is to help banks, insurers and financial institutions understand how trusted, governed master data can unlock AI, improve customer experience, strengthen risk control and support regulatory confidence.
In financial services, the challenge is rarely a lack of data. The challenge is whether that data is connected, governed, reusable, and explainable at the point of decision. That is where Stibo Systems is highly relevant.
Stibo Systems was recently named a Leader in the 2026 Gartner Magic Quadrant for Master Data Management Solutions, which we see as strong recognition of our enterprise-scale execution and strategic relevance in a market increasingly shaped by trusted, AI-ready data.
Evolution In Financial Services
When asked how Stibo Systems and its solutions have evolved to serve the financial services industry, Blake explained:
Stibo Systems has evolved from being seen primarily as a master data management provider to becoming a strategic data foundation for AI, governance, and digital transformation.
Our STEP platform is multi-domain by design. That is critical in financial services because banks and insurers cannot make trusted decisions from customer data alone. They need governed context across customers, products, policies, accounts, brokers, counterparties, entities, locations, hierarchies, consent and relationships.
This is one of our strongest differentiators. Many platforms focus on one domain, one function, or one application layer. Stibo Systems helps organizations govern the critical data domains that sit across the enterprise.
We are also independent, which matters in a market shaped by consolidation. Our platform approach is not built around stitching together acquired technologies. Stibo describes its approach as a unified, single-codebase platform designed to support consistent governance across domains.
For regulated financial institutions, that independence and multi-domain capability are important because AI, compliance and customer experience all depend on the same thing: trusted, explainable data.
Data Challenges When Scaling AI
When asked about the most common data problems that emerge when financial institutions try to scale AI across the enterprise, Blake noted:
AI exposes the weaknesses in an institution’s data foundation.
The most common problems are duplicated customer records, inconsistent entity data, fragmented product definitions, unclear ownership, weak consent governance, limited lineage and different teams using different versions of the truth.
Those issues may be manageable in a pilot. They become major barriers when AI moves into service, risk, fraud, underwriting, claims, advice or compliance.
There are three consequences.
First, AI outcomes become inconsistent because the inputs are inconsistent.
Second, explainability becomes difficult because teams cannot show what data was used, who owns it, or how it changed.
Third, AI remains trapped in pilots because every use case requires a new data remediation effort.
Stibo Systems addresses this by creating governed, reusable and explainable master data across domains. That is the foundation financial institutions need if they want AI to move from experimentation to enterprise-scale adoption.
Priorities For CIOs And CDOs
When asked what should be on a CIO or chief data officer’s 12-month priority list to ensure AI investments hold up under real-world pressure, Blake recommended:
I would focus on five priorities.
- Identify the critical data domains AI depends on: customer, entity, product, account, policy, broker, counterparty, consent, hierarchy, and risk context.
- Define ownership and governance for those domains. AI cannot scale responsibly if no one can explain who owns the data, which rules apply, or how changes are approved.
- Move beyond narrow Customer 360. Financial decisions depend on relationships, permissions, obligations, products, exposure, and risk context, not just a profile.
- Create reusable governed data foundations for AI, analytics, digital channels, risk and compliance.
- Build explainability into the data foundation through lineage, rules, stewardship, approvals, and auditability.
The key message for CIOs and CDOs is simple: do not treat AI readiness as a model problem alone. Treat it as a trusted data foundation problem.
This is where Stibo Systems is differentiated. We are not another analytics platform or data warehouse. We provide the governed, multi-domain master data layer those environments need to deliver trusted outcomes.
What AI Readiness Looks Like
When asked what “AI readiness” looks like from a data perspective for a bank or insurer, Blake explained:
AI readiness means the institution can trust the data behind the decision.
For a bank or insurer, that means critical data is mastered, governed and connected across domains. The organization knows who the customer is, what products or policies they hold, what permissions apply, what relationships matter, what exposure exists, and what risk context should influence the decision.
This is why multi-domain MDM matters. AI does not make decisions on customer data alone. It needs trusted context across product, policy, account, broker, counterparty, consent, hierarchy, location and relationship data.
Stibo Systems STEP helps create that governed context so AI can be more consistent, explainable and defensible. In regulated financial services, that is essential. AI readiness is not simply data availability. It is data trust, data governance and decision explainability.
Customer Success
When asked about specific customer success stories, Blake said:
We support large, complex organizations that need to manage trusted data across critical domains and business processes. In financial services, the value usually appears in three areas.
The first is creating trusted customer, entity and relationship data to improve operational consistency, customer experience and risk visibility.
The second is governing product, policy, broker or reference data so teams can reduce reconciliation and improve confidence in downstream processes.
The third is enabling more controlled, explainable and reusable data foundations for AI, analytics, regulatory reporting and digital transformation.
Where customers have approved public references, we share those through formal case studies. More broadly, the success pattern is clear: the greatest value comes when firms stop treating master data as back-office clean-up and start treating it as an enterprise capability for trusted decisions.
The Next 12 To 24 Months Of AI
When asked what will define the winners in AI across financial services over the next 12 to 24 months, Blake said:
The winners will be the institutions that industrialize trust.
Over the next 12 to 24 months, financial services will move from AI experimentation to AI operationalization. That shift will expose which firms have governed, reusable and explainable data foundations, and which firms are still relying on fragmented data and manual reconciliation.
The winners will do three things well.
They will govern the critical data domains that AI depends on.
They will connect customer experience, risk, and compliance through the same trusted data foundation.
They will build for reuse, so every AI initiative does not require a new data remediation exercise.
That is where Stibo Systems has a strong role to play. STEP sits between the operational systems that create data and the AI, analytics and digital systems that consume it. It provides the governed master data foundation that makes AI more scalable, explainable and enterprise-ready.
The next phase of AI will not be won by the firms with the most models. It will be won by the firms with the most trusted intelligence.
Connecting Data Versus Trusting Data
When invited to discuss another topic, Blake concluded:
One topic that deserves more focus is the difference between connecting data and trusting data.
Financial institutions have invested heavily in cloud platforms, data lakes, analytics, AI tools, and ecosystem connectivity. Those investments are important, but they do not automatically create trusted customer, product, entity, or relationship data.
That is the gap Stibo Systems addresses. We provide a governed, independent, multi-domain MDM platform that complements data platforms and AI tools. We help institutions create the trusted context those systems need to operate at scale.
For banks and insurers, the opportunity is not simply to become more data-driven. It is to become more decision-ready. That means turning fragmented data into governed intelligence that can support AI, compliance, and customer experience at the same time.
