When data observability and remediation tools work, the only measure of success is silence – nothing happens, no data breaks, no anomalies, no dramas.
It might seem hard to demonstrate the success of something when there is nothing to show, especially when the business is required to prove the value of the process to secure more investment.
In the data world, however, no news is good news and there’s a surefire way of proving the worth of that investment: highlighting the likely costs of those tools were not there.
That was the message from a panel of experts during A-Team Group’s latest Data Management Insight webinar, which discussed how trust in data can only be achieved when there are tools in place to react to errors and anomalies in the data. Observability tools find the problems, while those remediation tools fix the problem.
If one of the links fails in that chain of trust-guarantee, then the repercussions could be felt further down the data pipeline, reaching the data consumers and compounding over time.
The potential impacts of such events are well understood by senior executives, the panel agreed. The costs of missed business opportunities, regulatory fines, operational downtime and reputation damage – all of which are likely results of a data failure – will be heavy.This is how you demonstrate the value of your observability and response tools to senior leaders, one of the panellists stated.
Expert Panel
The webinar, entitled The ROI of Data Trust: Quantifying the Business Value of Data Observability, gathered Jay Reilly, SVP, sales – Global Centre of Excellence – at Precisely; Christina Schack, head data operations and strategy at Vontobel; and Paul Barker, chief control officer – cross controls enterprise technology at HSBC. The discussion was moderated by Data Management Insight editor Mark McCord.
Observability and associated tools are vital to the smooth running of modern financial institutions because many of their pipeline processes are automated, many using artificial intelligence. With less human oversight, those processes work faster, but they can also be more vulnerable to data errors. Tools have been created to mitigate against that but, still, some anomalies can creep into the data stream unnoticed.
The efficacy of those early alert and rectification processes is the gatekeeper of data quality.
The webinar panel agreed that framing observability as a preventative measure rather than a cost centre shifts the narrative to one of cost and damage control, or an insurance policy, as one of the panellists described it.
The operational risks inherent in transitioning from human-in-the-loop processes to autonomous systems were examined in depth, with a warning that removing human oversight reduces points of observability, meaning bad data errors quickly become bad decisions at scale.
Moreover, modern machine learning models do not always fail visibly. AI models trained on compromised data often fail plausibly rather than loudly, complicating error detection, the panel heard.
To counter this, the panel discussed combining AI-driven anomaly detection with hard mathematical rules. Establishing rigid boundaries for critical data points prevents AI from hallucinating acceptable thresholds and ensures that automated systems halt processing when critical errors are detected.
Hybrid Infrastructures
From a compliance perspective, regulations such as GDPR and BCBS 239 mandate strict controls over data movement and lineage. Observability tools address this by operating on metadata and statistical profiling rather than the underlying transactional data. Checks are performed in situ, ensuring that sensitive customer data or proprietary trade information is never exposed or replicated unnecessarily, thereby aligning privacy mandates with data quality initiatives.
Ultimately, the goal of data observability is to establish a high baseline of trust. However, trust is not dictated by the data producer; it is determined by the consumer. When business units have visibility built into data provenance, freshness and quality scores, their confidence increases. This confidence acts as an accelerant for both self-service analytics and broader AI adoption.
Understanding the concept of “good enough” allows organisations to allocate resources efficiently. While statutory financial reporting requires absolute precision, exploratory thematic testing can proceed with lower-tier data, provided the users are aware of the confidence metrics attached to that data.
Actionable Strategies
The panellists offered several practical recommendations for deploying modern data pipelines:
First, embed monitoring into the initial design phase. Observability should not be an afterthought; error and exception handling must be integrated at each step of the data lifecycle.
Second, ensure that every alert generated by the system has a designated owner, a defined routing path, and a measurable outcome. Without these, organisations risk creating an expensive notification system that fails to resolve underlying issues.
Finally, avoid monolithic implementation strategies. Rather than attempting to overhaul core legacy systems immediately, begin with newly deployed applications to create a minimum viable product. This agile approach allows teams to build trust gradually and simplify the architecture over time.
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