Solving Today’s Data Challenges: Trust, Automation, and Visibility as Strategic Imperatives

Sanjeev Aggarwal

April 2025

Data you can trust

Governance you can automate

Visibility you’ve never had

A blueprint for modern data architecture and governance

This is my view—not just a strapline, but a practical approach for overcoming the persistent data challenges we see across enterprises.

In today’s digital economy, every organisation is drowning in data—but rather than being empowered by it, many are lumbered by it, impaired by its complexity, and burdened with lost opportunity cost as valuable time and resources are spent resolving data issues instead of unlocking its full potential.

How much of your data strategy today is focused on fixing what is broken rather than enabling what is possible?

I continue to read articles, see reports and real-world examples where data is fragmented, lineage is unclear, and governance is not only reactive, but often perceived as a compliance overhead rather than a business benefit. As a result, operational decision-making is frequently hindered by a lack of confidence in data quality, which in turn undermines data trust across the enterprise.

What is especially concerning is that these problems persist despite significant investments in modern platforms, cloud technologies, and AI initiatives. And while the tools are getting better, the outcomes often are not. My take on this is that it is because the true challenges are not technical—they are architectural, approach and cultural.

People are still discussing how they’re failing to meet their data targets, and I see a growing disillusionment with data programmes that over-promise and under-deliver. This isn’t about a lack of ambition—it’s about a lack of alignment at the foundation.

The question I ask is have we built our data strategies on scalable, trusted foundations, or are we trying to accelerate without alignment?

I recognise that we don’t have the luxury of starting from a blank slate but what is preventing us from crafting a strategy that works with the reality of today’s landscape, while also positioning us for long-term success?

Are we ignoring the present to concentrate on tomorrow at the cost of solving what matters now?

To move forward, I believe we need to adopt a new strategic approach. One that prioritises trust in the data we use, automation in how we govern it, and visibility into how it flows, transforms, and supports decision-making. These are not aspirational ideals—they are essential design principles that must be embedded into the core of our data ecosystems.

If we want to enable real-time insight, respond confidently to regulation, and harness the full potential of AI, we must rethink how we approach data—not as a raw asset, but as a governed, observable, and trustworthy capability by design.

Establishing Trust in an Unstable Data Landscape

The concept of “data you can trust” goes far beyond quality scores or occasional cleansing routines. Trust is about knowing that data is reliable, explainable, and fit for purpose—every time it is used. This is especially important when data is used to inform high-stakes decisions: credit scoring, fraud detection, investment allocation, regulatory reporting, and increasingly, the training of machine learning models.

Yet trust is often undermined by poor lineage, inconsistent definitions, and weak validation. Data may pass through five or ten systems before it reaches an analyst, and by that point, it is often unclear where it originated, what transformations it underwent, and whether it is still fit for the intended business use.

According to Gartner, 84% of CEOs are concerned about the quality of the data on which they base decisions (Gartner, 2021). Trustworthy data must be defined, traceable, and complete in its business context—not just technically correct.

Automating Governance at the Speed of Business

Historically, data governance has been treated as a compliance-driven discipline—important, but slow. It focused on policies, committees, documentation, and reactive oversight. In modern data ecosystems, I believe this model breaks down. While governance is critical it needs to adapt, be lean and agile, useful not an hinderance.

The volume, velocity, and variety of data mean that manual governance simply cannot scale. A modern approach to governance must be proactive and automated, embedded directly into the architecture of data operations. Governance rules must be executable, auditable, and consistently enforced across environments, allowing organisations to move from static control to dynamic policy enforcement.

McKinsey research shows that companies with high-performing data governance capabilities are 2.5x more likely to outperform peers in revenue growth (McKinsey Digital, 2022).

Achieving Unprecedented Visibility Across the Data Estate

Many data failures don’t occur because policies were wrong, or intentions were bad. They occur because nobody saw the issue coming. Data moved unexpectedly, quality deteriorated, transformations changed logic, or a feed broke silently. These are common problems, that all data professionals, sadly, have encountered in their careers.

My belief is that the solution is operational observability: not just knowing that data exists, but understanding how it moves, evolves, and behaves over time. Organisations need observability into the data supply chain—so they can detect problems before they cascade, understand where data is duplicated or misused, and continuously improve how information supports the business.

According to a 2023 Gartner report on data observability, 60% of data leaders cite lack of visibility into data pipelines as a major barrier to data-driven decision-making (Gartner, 2023).

A Strategic Architecture for the Future

“Data you can trust. Governance you can automate. Visibility you’ve never had.” This is not a vision of the future—it is a design principle for the present. These three pillars are not discrete projects or siloed initiatives. They form the architecture that underpins a data-driven enterprise.

They empower confident decision-making, simplify compliance, accelerate innovation, and de-risk AI adoption. Most importantly, they reframe the role of data governance from an overhead function to a strategic enabler of digital transformation. In an environment shaped by rising regulation, increasing complexity, and a growing appetite for data products, these capabilities are no longer optional—they are foundational.

If we want to treat data as an enterprise asset, we must govern it like one: with clarity, confidence, and control at every stage of its lifecycle.

This is the problem deltamap was built to solve.

deltamap: Realising Trust, Automation, and Visibility in Modern Data Ecosystems

“Data you can trust. Governance you can automate. Visibility you’ve never had.”

In an era defined by exponential data growth, tightening regulatory expectations, and AI-driven transformation, the fundamentals of data governance have shifted. It’s no longer enough to define data policies; organisations must operationalise them. It’s not sufficient to document lineage; businesses need to observe it—live, in real time. And trust in data must be earned not through assumptions, but through evidence.

This is the problem deltamap was built to solve.

Data You Can Trust

Data trust begins with transparency and integrity. deltamap analyses actual data content—not just metadata—to map how data enters, transforms, and exits your ecosystem. It traces lineage and information flows across systems, identifies quality issues at the point of entry, and validates data intent using intelligent classification and content-based profiling.

By embedding content-aware insight at the heart of your data architecture, deltamap ensures that the data being used in AI models, regulatory reporting, or operational dashboards is accurate, complete, and explainable.

Trust is no longer a leap of faith. With deltamap, it’s a traceable fact.

Governance You Can Automate

Most governance programmes fall short because they rely on manual policy enforcement, siloed metadata catalogues, or after-the-fact audits. deltamap redefines governance as a live, intelligent layer that observes, enforces, and responds.

With policy-aware observability, deltamap allows organisations to:

• Detect violations of access, retention, or classification rules in real time

• Automate lineage documentation for GDPR, BCBS239, and DORA audits

• Embed controls directly into dataflows—without modifying source systems

Governance becomes operational and adaptive, no longer a static framework but a live service woven into the data fabric.

Visibility You’ve Never Had

Most organisations don’t know where their data is, how it’s used, or what risks lie hidden across systems.

deltamap changes this.Its event-driven, schema-less architecture provides real-time, zero-copy observability—mapping the flow, structure, and evolution of data across cloud, on-prem, and hybrid environments. This includes:

• Understanding who’s using what data, and why

• Tracking schema drift and unexpected transformations

• Surfacing shadow pipelines and uncontrolled data copies

This level of visibility enables data teams, governance leads, and business units to make decisions based on evidence, not assumptions.

The Outcome: Data as a Strategic Asset

With deltamap, data is no longer a liability hidden in pipelines or tangled in legacy infrastructure. It becomes a strategic asset—trusted, governed, and visible.

Whether your goal is to enable responsible AI, comply with regulatory requirements, or build scalable, federated data products, deltamap provides the intelligence layer to power your next step forward.

Data you can trust.Governance you can automate.Visibility you’ve never had.

deltamap the future of data management. Contact as on hello@deltamap.io

References

Gartner (2021). ‘CEO Survey: The Role of Data in Executive Decision-Making’.McKinsey Digital (2022). ‘The Data-Driven Enterprise of 2025’Gartner (2023). ‘Hype Cycle for Data and Analytics Governance and Observability’.