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At InvestOps 2025, Managing Consultant Rikard Grafstroem joined Northern Trust’s the Faster Forward podcast team to spotlight common pitfalls organizations face when managing data. From treating data as an afterthought to overlooking its business value, the conversation underscored why data must be a strategic priority, not a downstream task, especially in the era of AI.
If you've ever championed the importance of data in transformation efforts, some of these challenges may resonate. See his interview here.
Data considerations are frequently deferred until after technology and organizational changes are underway. This sequencing undermines the strategic value of data and can lead to misalignment between systems and the insights they are meant to generate.
Insight: Bring data in early. Not as a dependency, but as a design principle. Data should shape the architecture, not just fill it. Embed data strategy into the blueprint—so structure and insight evolve together.
Transformation programs often prioritize the technical implementation of data platforms while neglecting the business context. This results in systems that are technically sound but fail to deliver actionable insights or support decision-making.
Insight: Start with the decisions the business needs to make. Then reverse-engineer the tech to support those decisions. Tools are only as valuable as the clarity of the questions they’re built to answer.
Data initiatives frequently overlook the specific business use cases they are meant to serve. Without clear alignment to business objectives, data structures and hierarchies may be ill-suited for operational or strategic needs.
Insight: Define the “why” before the “how.” Every model, every metric, every dashboard must trace back to a business outcome. If it doesn’t serve a purpose, it doesn’t belong.
This can limit the organization’s ability to define and maintain appropriate data hierarchies impairs the ability to aggregate, analyze, and report data effectively. This limits the organization’s ability to derive value from its data assets.
Insight: Build hierarchies that mirror how the business thinks—not just how systems store. Structures are intuitive, scalable, and aligned to decision flows—not database schemas.
When data is treated as a purely technical asset, ownership can become fragmented. Without joint accountability between business and IT, data governance and quality suffer.
Insight: Make data a shared responsibility. Business and IT co-own quality, governance, and value delivery. Because data isn’t just infrastructure—it’s leverage.
If you’ve ever fought to elevate data from backend burden to strategic asset, this conversation should sound familiar. Let’s talk about how TorreBlanc can help you turn data into leverage. Contact us—we’re ready to help you tackle your toughest data challenges.
