2026’s quiet advantage: Why better data wins in alternatives

In 2026, data quality, risk management and standardisation are not optional extras for alternative fund managers – they are strategic imperatives.

By Penelope Rance

12 minute read time

Clean, consistent data underpins faster capital deployment, stronger investor confidence and operational resilience. Beyond these core advantages, it increases the ease of doing business, supports seamless onboarding, and delivers credible reporting. It’s time to reframe data – once a back-office burden – as a front-office competitive edge.

“For fund managers who are dealing with investors, with HV depositories, with banks, by being able to quickly and accurately provide information and interpret that information, they’ll build deeper relationships, they’ll build trust and confidence, they’ll become trusted partners, which is good for them and ultimately good for the business they run,” says Nick Kinnear, Head of Economic Crime, COO at RBS International.

By contrast, a lack of clean, standardised data can lead to slower capital deployment, regulatory failures and long-term reputational damage. Despite this, RBSI’s Pulling Together report reveals that the majority of funds urgently need to address data quality, with 90% of respondents saying poor data quality is constraining their ability to meet operational challenges, while three-quarters believe poor data will result in them failing to attract and retain investors in the run up to 2030.

At this critical point in the implementation of the advanced analytics and automation technologies key to future competitiveness, our research shows that only 40% of funds are confident their data is good enough to support these processes. To overcome all of these issues, fund managers need to understand where and how better data can help them win; and the practical steps they can take to improve it.

Investor expectations

In a digital world, LPs expect funds to provide comparable, audit-ready reporting across illiquid portfolios, and this requires high quality, reliable data. Investors also want to be able to access information themselves from complex data sources in a format that makes sense to them.

“As AI is increasingly employed for users to self-serve access to data, insights and reporting – for example through natural language queries – clean, high quality, documented data is key to ensuring that responses are accurate and consistent,” says Chris Wright, Lead Data Engineer, Data & Analytics at RBS International.

Good data also improves investor experience by removing friction around investments, reducing the amount of rework, repetitive information requests and manual clarifications between managers, administrators, banks and regulators. “High quality data enables the automation of processes and removes non-value add human intervention in processing,” says Chris. “This allows colleagues to spend more time helping customers and solving complex issues, while ultimately reducing cost for firms.”

With clean investor master data, accurate commitment schedules and consistent entity identifiers there is less manual checking and faster decision-making. Digital onboarding, automated due-diligence and transaction screening are all improved by accurate, well‑structured data which allows systems to detect anomalies and reduce false positives, reducing the onerousness of KYC. Pre-drawdown readiness is also improved, increasing capital velocity for investors. Where fund managers have confidence around investor commitments, ownership transfers, payment instructions and bank account details, risk is reduced, there are fewer exceptions and follow ups, and greater certainty that capital will arrive as expected. Fund finance approvals become smoother, and valuations are more defensible, reducing audit challenges and increasing investor confidence.

Investors also benefit from fund managers using data to tailor their offerings. “Reliable data is key to being able to understand customers and how as a firm you can best serve them,” says Chris. “An example is ensuring that relationship managers have an accurate, up to date picture on the customers they support in order to understand when they may benefit from a new product or service.”

Similarly, having access to consistent data allows fund managers to screen more opportunities than if they have to manually review every deal. “Having that good quality, traceable, validated and understandable data means funds can get so quick at onboarding, at providing the next service, at understanding opportunity, at responding to a customer’s needs,” adds Nick.

Regulation and security demands

In addition to the need to inspire investor confidence, regulatory requirements have made traceable and accurate data a condition of doing business in the financial sector. “Regulators expect that firms can evidence the quality of data, how it was collected and how it is being used to make decisions,” points out Chris. “And as machine learning and AI are rolled out further, the requirement to be able to evidence the underlying data quality used to facilitate any decisions will only increase in importance.”

For example, regulators require that any ESG‑aligned products and sustainable finance frameworks are linked to consistent, verifiable, traceable datasets to mitigate the risk of greenwashing or loss of institutional capital. “High quality third-party reference data is essential to being able to assess ESG’s impact on investments and ensuring that it is within appetite,” says Chris. “This is particularly important where an investment is complex in nature and the underlying assets are diverse, which makes it hard to build a full picture of its ESG impact for a single firm.”

Both regulators and investors also expect funds to demonstrate that the opportunities for financial crime are limited through them ensuring the highest levels of data security. Good quality data strengthens AML and KYC decisioning, reduces fraud and identity theft, and improves customer risk scoring. Consistent data also helps flag operational, counterparty exposures and financial crime risk across increasingly complex structures.

“When dealing with very complex counterparties, transactions and business models, to be able to understand all of the potential risks that exist there, fund managers need access to good data that they can apply good skilling to,” says Nick. “For a fund to meet its obligations to identify, consider, assess, mitigate and manage, and ultimately prevent financial crime, it needs a strong database and accurate data. Having that clean data helps make things quicker and more effective for the good customer because it helps to more easily identify any potentially bad actors that are in there.”

The ability to detect when something outside the norm occurs is central to being able to spot fraud and financial crime, adds Chris. “This allows firms to take action quickly to protect customers and their money. If data quality is poor or incomplete, it becomes harder to identify unusual behaviours or events, or to spot sanctions evasion.”

Technical considerations

As the technology employed by funds becomes more advanced, the data it’s built on has to be more robust. “AI adoption is wholly reliant on good quality data, with the quality of responses directly correlating to the quality of data which has been used to train models. In other words, bad data in will lead to bad data out,” says Chris.

“A focus on data governance and data cleanliness and taking steps to analyse and understand the data funds hold, to see where they need to enrich or enhance it, would be time well spent in preparing for what they want to do next – because they’re not going to be able to take advantage of all of these big moves forward without it,” says Nick. He believes investing in the technology without investing in the data is a false economy. “Without putting the time and money into having the good quality data, without the real rigour and framework around ensuring that, there’s little point spending any money on the technology.” In the long term, he believes, the growth and development opportunities that open up through good data will pay for the technology.

Where operational outsourcing occurs, good data supports collaboration with fund admins and other service providers and is essential for mitigating errors. “Where there are intermediaries involved in a transaction, it is important that data quality is prioritised by design for any systems that facilitate the flow of data from one company to another,” outlines Chris. “This avoids costly remediation exercises downstream.”

Practical takeaways: Five steps to improving data quality

Define your data dictionary: Agree mandatory fields, formats and owners with administrators and banking partners. Don’t forget to also define who is responsible for key data by assigning Executive Data Owners for example.
Implement preventative controls: Validate critical data before capital notices, drawdowns and NAV strikes.
Document lineage for key LP reports: Make it easy to trace data from LP reports back to source.
Standardise reporting templates: Reduce due diligence questionnaire friction and improve comparability for LPs.
Measure quality monthly: Track completeness, accuracy, timeliness and exception aging.

A single source of truth

Unlocking the full potential of AI and emerging technologies requires an industry‑wide commitment to higher data quality and the creation of a trusted single source of truth within firms.

In the meantime, fund managers can work towards ensuring that outputs – whether through operational processes, analytics or AI – derived from data are accurate, reliable and repeatable.

Having a ‘single source of truth’ within the firm ensures that data is discoverable, observable and valuable. “In practice what this means is that colleagues who need access to data know where to go to find it, how they can consume it, and that they can trust any outputs they produce from it,” concludes Chris.

If you would like to discuss any of the themes covered in this article further, please reach out to your Relationship Manager.

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2026’s quiet advantage: Why better data wins in alternatives

By Penelope Rance

12 minute read time

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