By Trista Wang, Iress
From trading desks to compliance engines, financial firms are awash in dashboards and analytics. Yet turning those insights into strategic action remains a challenge. It’s often said that most organisations are drowning in data but starving for clarity.
The issue isn’t about having more data; it’s about making sure that firms have a clear problem to solve, they can identify the relevant data, use it, and learn from it in real time.
And the data evolution extends beyond just presenting compelling narratives and visualisations, the next step is moving towards initiating and managing a sequence of actions autonomously. The rise of agentic AI is making this much faster to implement. It’s early days yet, but agentic AI delivers a system able to make decisions and achieve complex goals with limited human supervision. A Moody’s report says financial sector firms are beginning to use agentic AI to monitor markets and detect correlations in real time, rebalance portfolios, continuously assess credit risk and automate compliance workflows.
The most underestimated risk firms face is the opportunity cost of poor data management. While regulatory fines and operational hiccups grab headlines, the real loss is in the insights never uncovered, the strategies never tested, and the alpha never captured.
Here’s what that looks like in practice:
- Missed revenue opportunities
Firms sitting on vast datasets but lacking the tools or talent to analyse them are essentially leaving money on the table. Valuable patterns in client behaviour, market inefficiencies, or risk signals go unnoticed. - Regulatory exposure
Inadequate data governance increases the risk of compliance failures, leading not only to multimillion-dollar fines but also to reputational damage, license restrictions, and heightened scrutiny from regulators. - Slower innovation
Without clean, accessible data, it’s harder to experiment with new models, test hypotheses, or pivot strategies quickly. That agility is a competitive edge in fast-moving markets. - Wasted resources
A 2025 study[i] found that poor data quality costs large Australian firms an average of $493,000 per year in inefficiencies and missed opportunities. Globally, Gartner estimates the average cost of bad data at $12.9 million[ii] annually per organisation. - Undervalued intellectual capital
Data is a strategic asset, but only if it is used properly. When firms fail to harness it, they are not just wasting storage; they are underutilising the knowledge and creativity potential of their teams.
Four key barriers consistently prevent organisational progress
- First, siloed systems fracture visibility. Firms might collect vast amounts of data, but fragmented platforms and inconsistent quality mean decision-makers rarely get the whole picture in real-time.
- Second, there’s often a missing link between data teams and business leaders who sometimes fail to see each other’s perspectives.
- Third, distinguishing between gaining data insights and applying them for strategic action is not always straightforward. Take, for example, Iress’s new Data Insights product. The Regulatory Reporting module enables firms to receive proactive alerts about suspicious activities, giving compliance officers clear guardrails while allowing them to make the final judgment. In this way, the insight itself is valuable, but the true impact comes from the subsequent action, where feedback from those decisions helps refine and improve the system’s automation over time.
- And finally, firms often underestimate the cultural transformation required. Being data-informed isn’t just about tools; it is a mindset shift that demands buy-in, process change, and continuous iteration.
What success looks like
Successful organisations align four critical capabilities:
- Building cross-functional teams: People with hybrid technical and business skills who can interpret data models in terms of business levers and risk strategies. Cross-functional teams can drive momentum, but where possible, firms benefit from hiring individuals who embody both technical and commercial fluency. These ‘bilinguals’ are the fast-track to clarity.
- Driving business accuracy with reliable data platforms: Platform integrity that delivers clean, complete, and consistent data, which reflects the right customers, products, and timeframes.
- Integrated workflows for smarter business decisions: Integrated workflows that embed insights where decisions are made – CRMs, OMS platforms, executive dashboards.
- Embedding a data governance framework: Robust data governance ensures that insights are trustworthy, secure and responsibly managed. Firms that embed governance into platform design can mitigate risk, preserve decision integrity and scale automation more confidently.
And most important of all, good data platforms use a continuous feedback loop. Without structured evaluation, even good decisions become guesswork. But with it, data strategy is constantly refined.
Take the example of an Australian firm that was looking to improve customer retention. Instead of launching a broad data churn model, the team zeroed in on a single friction point in the customer journey. Working backwards, they identified the data they needed, embedded insight into the adviser workflow, and tested a new action path. It worked. Retention improved and the process was repeatable.
And crucially, it started small. Remember that strategic capability isn’t ‘launched’. It’s built, tested, and expanded.
Beginning (or accelerating) the journey
Few firms start from zero. But maturity varies. For those still in transition, remember to:
- Start with one real problem.
- Map the decision-makers involved.
- Define what success looks like.
Firms that get it right don’t just analyse data; they build entire ecosystems around it using quantitative modelling, machine learning and AI, backtesting and simulation, risk management models, and a host of other tools.
These strategies aren’t just about crunching numbers; they’re about building a dynamic, adaptive system that learns and evolves with the market.
Which leads to one final truth: turning data into impact is a transformation, not a project. It’s iterative. It’s cultural. And done well, it never stops.
[i] https://www.ironmountain.com/en-nz/about-us/newsroom/press-releases/2025/june/data-flaws-hit-large-australian-firms-impacting-channel-partners-significantly
[ii] https://www.gartner.com/smarterwithgartner/how-to-improve-your-data-quality
