Progress over perfection: Why waiting for better data is holding you back

• Waiting for perfect data is one of the biggest barriers to successful transformation. Organisations that delay digital, data, and AI initiatives until data quality is flawless risk missing opportunities for growth, efficiency, and competitive advantage.
• The most effective data transformations balance quick wins with long-term foundations. By delivering targeted, high-value use cases while strengthening data strategy, governance, and ownership in parallel, organisations can build momentum and demonstrate measurable business value sooner.
• Progress beats perfection. Financial institutions that act, iterate, and scale continuously are better positioned to unlock value from data, accelerate AI adoption, and stay ahead of evolving customer expectations and market demands.

If your next digital, data or AI transformation project is on hold until your data is in better shape, here’s an uncomfortable truth: organisations that wait to act are already behind. By failing to extract value from the data you have today, you’re leaving opportunities for revenue growth, cost reduction, and competitive advantage on the table.

This is not an argument against data quality. Far from it. The best analytics and AI investments are worth nothing with bad data. Accurate, comprehensive information is also necessary for risk assessments, capital calculations, and compliance reporting in financial institutions.

The aspiration to achieve data excellence is entirely legitimate. The trap lies in pursuing perfection before extracting value. What if, instead of waiting until everything is right, you focused on identifying the highest-value opportunities your data can unlock right now, and used that progress to demonstrate impact to colleagues, build credibility with clients and differentiate yourself from peers?

The myth of perfection

Banks and Insurers have enormous wealth across vast amounts of data, but many aren’t realising its full value. The reason is often a belief that their data is not good enough to act on.

At Baringa, we typically see financial services firms caught between two extremes. On one end are those who bite off more than they can chew, pouring millions of pounds into multi-year programmes aimed to remediation and perfectly enhance their data before anything else can begin. These projects become endless cycles of expense and effort, compounded by the lack of a clear vision, direction or meaningful understanding of data. Despite the scale of resources committed, many fall short of their promise, leaving stakeholders frustrated and the business without the results it set out to achieve.

On the other end are companies that sit on their hands, setting unrealistically high standards for data quality, then never making any real progress. They tackle a handful of tactical use cases, but neglect the deeper strategic shifts needed to sustain true transformation. This approach may resolve immediate pain points, but leaves their root causes unaddressed. As a result, the benefits remain localised, with organisations unable to scale up deployments and harness data to drive transformative change across the business.

In both of these scenarios, limited direction from the C-suite often contributes to the problem. Executives often delegate data initiatives to the Chief Data Officer, then step back. However, the reality is that data isn’t the CDO’s responsibility alone, and leaders need to take accountability for how data is created, consumed, transformed and analysed in their functions.

Improving the understanding, quality and value of data is an enterprise-wide effort that requires leadership from the top and commitment from across the organisation. The keys to success are selecting right-sized projects, aligning around a shared vision and understanding of data, and recognising that consistent progress matters more than the pursuit of perfection.

Start building with what you’ve got

At Baringa, we advocate for a balanced approach. Rather than tackling large-scale transformation in one fell swoop, most financial institutions will achieve better results by focusing on functional wins where data can deliver tangible outcomes fast.

As they’re working to implement these targeted use cases, organisations should strengthen their strategic foundations in parallel. This dual approach delivers the quick wins needed to sustain transformation and business buy-in, while simultaneously building the foundations needed to compound value over time.

In our experience, agile and iterative delivery models work well for data transformations, because they prioritise continuous improvement. This is far more effective than waiting for conditions to be perfect or wasting effort on work that fails to move the needle. 

As delivery progresses, it is vital to translate enterprise-level data strategy into a meaningful vision for specific areas of the business, giving individual functions clear, relevant objectives rather than abstract targets. This has the added benefit of sharpening understanding of the data and what value it can realistically yield. It creates the conditions for teams to develop better solutions, enhance business processes, and extract progressively greater value from their data over time.

The time to act is now

The longer financial institutions wait to undertake transformation, the more difficult it will be to make meaningful improvements. Data volumes will keep growing, technology will advance, and customer expectations will evolve. Without data readiness, digitalisation and AI adoption efforts will stall, leaving organisations at a distinct disadvantage compared to more ambitious competitors.

The question, then, is not whether to act, but where to begin. For organisations serious about making data a source of lasting value, the following steps provide a practical starting point:

1. Get to know your data on a conceptual level. Develop a clear, enterprise-wide view of what data you hold, where it originates, and how it moves through the organisation. This becomes the cornerstone of any meaningful transformation
2. Define a clear strategic direction. Set out the principles and priorities that will guide the transformation, giving teams a coherent framework for evaluating and designing data solutions
3. Scrutinise investments carefully. Challenge efforts that focus solely on assessing or quantifying data quality without a broader strategic purpose or measurable value
4. Set specific objectives. Focus on concrete, outcome-driven goals for specific areas of the business. This creates a virtuous cycle where sharper data understanding unlocks further opportunities to innovate, improve processes, and extract greater value
5. Be deliberate about data. Establish clear ownership and standards for how data is created, managed, and used, ensuring every decision about data is intentional.

Tomorrow’s leaders are not waiting for the right moment. They are focusing on how to move from incremental change to exponential returns. They know that to win, they need to act, iterate, and scale as fast as they can.

So ask yourself: are you chasing perfection, or are you delivering progress?

If you’re ready to move your data-driven transformation forward, Baringa is ready to help. Together, we can identify where progress is possible today and build the momentum that makes tomorrow’s transformation achievable. Get in touch with our team to find out more.