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The Data Is There. The Value Is Not.
Why are banks and insurers sitting on their most underused competitive asset and what to do about it?
Pierre Nemeth · CEO, SOPIAD
Reading time: 3 minutes
I recently took part in a working group with senior executives from across the financial sector.
When the facilitator asked about their most pressing internal challenges, I expected answers around
AI implementation or blockchain.
Instead, the first response came hesitantly, almost apologetically, as if raising an old topic:
“Data management.” Then another voice. Then the entire room.
Despite years of transformation and billions invested, data management remains the industry’s most
stubborn unresolved issue.
The paradox is striking: institutions with the richest data are often the worst at monetizing it.
McKinsey shows that less than one third of expected value from data initiatives is captured.
Not a technology issue. A trust issue. So what needs to change?
The Numbers That Frame the Problem
73% of European financial institutions cite data quality as their top barrier to digital transformation. (Gartner, 2025)
61% of French banks have no unified client view across business lines. (Capgemini, 2025)
$12.9M average annual cost of poor data quality per organisation. (Gartner, 2025)
60% of data and AI initiatives fail to deliver expected value, most often due to quality, governance or integration failures. (BCG, 2025)
Four Structural Challenges the Industry Can No Longer Defer
Legacy silos. Most institutions run 10 to 30 disconnected systems (core banking, CRM, compliance platforms,
investment tools). No unified client view is structurally possible without resolving this.
Raw data capture and model quality. Data ingested from heterogeneous systems arrives incomplete, inconsistently formatted, or duplicated. Downstream models are only as good as what feeds them. Skipping this layer means automating
imprecision at scale: outputs that cannot be trusted, decisions that cannot be defended.
Governance, traceability and security. Who owns the data, who transformed it, who accessed it and when: unanswerable in most organizations. Without data lineage, regulatory auditability is impossible. Without access controls and security
architecture, data assets become liabilities.
Regulatory complexity. Regulatory pressure is accelerating. New data requirements keep piling up, while compliance remains
largely manual, already accounting for ~10% of revenues and growing faster than any other cost line.
These challenges define the ceiling of the AI agenda. AI initiatives are only as reliable as the data
they operate on. Automating decisions on fragile foundations does not create efficiency, it scales
error. Solving the data problem is the prerequisite, not an afterthought.
Three Levers That Turn These Challenges Into Value
Each of these challenges has a calculable return once resolved, across three levers that institutions
consistently underestimate.
1. Better advice, better conversion
Banks with the highest client advocacy scores grow revenues 1.7x faster than peers; engaged clients
hold 17% more products with their primary institution (Accenture, 2025). A trusted, unified
data layer is the prerequisite for personalization at scale.
2. Operational efficiency
AI-enabled models reduce client interaction costs by up to 10x, with 40–50% savings on select
operations within twelve months (BCG), but only when the underlying data can be trusted.
3. Compliance as a competitive lever
Automating regulatory controls generates 30–40% cost reductions while improving the client data
quality that feeds commercial teams. Compliance is not a cost centre, it is a trust infrastructure,
and trust is the only durable competitive advantage in financial services.
Where Sopiad Fits In
SOPIAD’s SAFIR platform addresses the data management, quality and security layer that most
institutions are still missing — notably through its Smart Data Capture module, which automates
the extraction and structuring of complex data.
It enables automated, personalised reporting and connects governed client data to compliant,
personalised advisory at scale.
Institutions that defer their data transformation are not making a neutral choice. Every quarter
of inaction is a quarter in which competitors consolidate their edge and client expectations
continue to rise.
The value of data is not in holding it, or in making it flow. It is in using it with precision
on a foundation that can be trusted