Unlocking the Power of Data: Why Modern Software Engineering Is the Key to Business Growth

For today’s CTOs and technology leaders, the conversation around data has moved well beyond storage, pipelines, or dashboards. Data is now a strategic asset — one that can fuel innovation, improve decision-making, and unlock new sources of business value when handled well.

But treating data as a true competitive advantage requires more than a modern tech stack. It calls for strong engineering practices, clear governance, cultural alignment, and readiness to scale responsibly into areas like AI. These are challenges Codurance explored in-depth at a recent roundtable dinner with senior tech leaders, where one theme became clear: unlocking the power of data requires both technical excellence and organisational maturity.

Culture and Buy-In Still Matter Most

No amount of tooling will fix a culture that doesn’t value data. This is one of the biggest challenges many organisations face — not technical blockers, but cultural ones.

When data is seen as someone else’s responsibility — usually IT — it becomes siloed and neglected. But when it’s treated as a shared, strategic asset, people start to make better decisions, faster. CTOs can drive this shift by reframing data initiatives around business impact. Reducing the time it takes to launch new products, improving forecasting, enhancing customer experience — these are outcomes people across the business can get behind.

Embedding data into day-to-day thinking means creating shared ownership, building internal advocates, and linking data projects directly to commercial goals. It also means encouraging cross-functional collaboration between engineering, product, operations, and leadership. When that happens, data maturity becomes a collective ambition rather than a specialist project.

Strong Engineering is a Prerequisite

Engineering maturity underpins everything. Without it, even the most ambitious data strategies get bogged down in inefficiencies and technical debt.

Modern data platforms need to be built on scalable, secure, and reliable foundations. That includes modular architectures, automated pipelines, infrastructure as code, and robust testing. These aren’t just technical choices — they’re strategic enablers. They make it easier to evolve systems, onboard teams, and deliver changes safely and quickly.

More importantly, good engineering unlocks capacity. When teams aren’t constantly firefighting, they can focus on building better products, improving data flow, and experimenting with new ideas — including the responsible use of AI.

Governance as an Accelerator

Governance is often misunderstood as something that slows teams down. In reality, good governance helps organisations move faster — with more confidence.

It’s about creating clarity. Where does data live? Who owns it? Who can access it, and under what conditions? When those questions are easy to answer, teams spend less time chasing approvals or second-guessing reports. They can trust the data they’re using, and focus on delivery.

Governance also plays a key role in scaling responsibly. As your organisation grows — and as regulations evolve — having clear policies around data access, classification, usage, and retention becomes essential. The key is embedding governance into everyday workflows, so it becomes a natural part of how teams work, rather than an obstacle they try to work around.

Data Quality Affects Everything

Even the most advanced platform can’t deliver results if the data running through it is inaccurate, inconsistent, or incomplete. Data quality has a direct impact on almost every part of the business — and when it’s poor, the consequences compound quickly.

• Inaccurate inventory data leads to overstocking or lost sales
• Supply chain disruption increases when routing data is unreliable
• Poor CRM data makes personalisation ineffective and reporting untrustworthy
• Pricing models misfire when fed with out-of-date product or market information

These aren’t just technical issues — they’re business risks. That’s why data quality can’t be treated as a one-off clean-up job. It needs to be baked into your engineering processes, monitored continuously, and owned across teams.

Observability and Metrics Build Trust

One of the most powerful enablers of data maturity is visibility. Without it, teams are left guessing when something breaks, where data is stale, or whether what they’re seeing can even be trusted.

Data observability gives you a clear, real-time understanding of what’s happening across your data pipelines. It allows teams to monitor health, trace lineage, track freshness, and detect anomalies before they disrupt decision-making or downstream systems. It turns vague questions like “why is this report wrong?” into actionable answers.

This kind of transparency also helps teams move from reactive to proactive. With the right observability tooling in place, issues can be spotted and resolved early, leading to fewer outages, less duplication of effort, and more confident decision-making across the organisation.

Pairing observability with delivery metrics — such as DORA’s deployment frequency and time to recovery — gives a full picture of both platform reliability and team performance.

To help organisations assess and improve in this space, Codurance offers a dedicated Observability Maturity Assessment. It helps identify gaps, surface risks, and prioritise improvements that make data platforms more resilient, scalable, and trustworthy.

AI is Only as Good as Your Foundations

AI is changing how businesses operate — but it’s not a shortcut to value. Without the right foundations in place, it can do more harm than good.

In the right context, AI can:

• Optimise pricing and forecasting
• Detect fraud and anomalies in real time
• Drive personalisation across digital channels
• Reduce manual overhead and streamline operations

But when AI is built on messy, biased, or ungoverned data, it can reinforce harmful patterns, amplify risk, and undermine trust. It’s essential to ensure your data is clean, governed, and reliable before layering AI on top.

Equally important, AI should augment — not replace — software engineers. While AI tooling can accelerate delivery by generating code, surfacing insights, or automating repetitive tasks, it can’t replace the architectural thinking, domain knowledge, or problem-solving that experienced engineers bring to complex systems. Engineering expertise is still the cornerstone of secure, scalable, and maintainable platforms.

Being AI-ready means building with intention. You need robust infrastructure, clear usage policies, and a culture of responsible experimentation. Codurance’s Data & AI Readiness Assessment helps leaders benchmark where they are today, and develop a practical roadmap to adopt AI in ways that are safe, scalable, and grounded in real business value.

Final Thoughts 

Unlocking the power of data doesn’t come from a single tool or one-off initiative. It’s the result of aligning culture, engineering, governance, and strategic intent. It means treating data as a product, not a by-product — and investing in the practices that make it trustworthy, visible, and scalable.

Modern software engineering plays a crucial role in this. But it only works when paired with a mindset that sees data not as a cost centre, but as a driver of innovation and growth. If you’re ready to understand where your organisation stands, start with our Data & AI Readiness Assessment or explore our Observability Maturity Assessment to build trust in your platform from the ground up.

Want to Be Part of the Next Discussion?

At Codurance, we regularly host roundtable discussions and events across the UK, exploring trending and thought-provoking topics in the software development and tech industry. If you’d like to get involved in the next one, please get in touch at hello@codurance.com.