The Digital Oil is Contaminated: Understanding Why Your Data Strategy Fails
AIFEATURED
Jose Cortinat
11/17/20252 min read
The Broken Promise of the "Data-Driven" Enterprise
Poor data quality is not an abstract IT problem; it is an operational disease with systemic root causes34. The classic "garbage in, garbage out" axiom manifests through:
Inefficient Information Sources: Manual data entry remains the norm, creating a database inherently prone to errors, omissions, and inconsistencies.
Systemic Integration Failure: The problem is not just incorrect data, but isolated data. Industrial executives identify "applications that are not sufficiently connected" as the main obstacle to productivity. This is aggravated by "AI implementations in silos," which prevent a 360° view of the operation, which is essential for informed decision-making.
Lack of Governance for Unstructured Data: The vast majority of a company's information—up to 97% in some cases—is unstructured: PDF reports, images, emails. This data, rich in context, often lacks any governance, turning it into a vast ocean of inaccessible knowledge.
The instinctive reaction is to launch massive "data cleaning" projects that rarely yield results. At VSC, we advocate for a radically pragmatic, iterative, and value-focused approach:
Identify a Critical Value Stream: Instead of trying to clean the entire database, select a fundamental business process, such as maintenance planning for critical assets.
Map the Data Journey: Perform an "autopsy" of the information flow through that process. Trace every step, identifying manual entry points, inefficient transfers between systems, and gaps.
Resolve at the Source: Implement improvements directly at the weak points identified in the process. This may involve automating data capture, creating APIs to connect systems, or standardizing procedures. The goal is to ensure that new data entering the system is high quality by design
Over the last decade, "being a data-driven company" has become the holy grail of corporate strategy. However, the frustration is palpable. A Kongsberg Digital survey revealed that only 5% of industrial professionals feel that their access to data allows them to make decisions very quickly. The promise is broken. The problem is not a lack of data; industrial operations are flooded with it. The problem is that this "digital oil" is deeply contaminated. Organizations try to build AI refineries on a foundation of low-quality, non-integrated data