RIGHT DATA, RIGHT TIME: ASSET OWNERS APPROACH DECISION-READY INVESTMENT DATA

Integration, accuracy and timeliness as top priorities

Asset owners are facing persistent challenges in managing data across fragmented systems, providers, and asset classes, limiting their ability to generate consistent and reliable insights. As investment portfolios become more complex and operating models more interconnected, the ability to bring data together in a way that is accurate, timely and usable has become a more pressing priority.

Integration of data across different sources and accuracy of data now rank as the joint top data management challenges, each cited by 57% of asset owners in the 2026 study. Both have increased significantly year over year, with integration rising from 50% and accuracy rising from 46%. The parallel rise of integration and accuracy challenges highlights that combining data is not sufficient on its own; asset owners also need confidence in its reliability once integrated.

Timeliness of data has also become a growing concern, as cited by 41% of the surveyed asset owners, up from 35% in Northern Trust’s 2025 Asset Owners in Focus study. This reflects the need to ensure that data is available in line with investment decision cycles. At the same time consistency of data remains unchanged year over year at 39%, indicating that standardization across datasets continues to be an ongoing structural issue rather than a newly emerging one.

Together, these findings point to a shift in focus from expanding data access to improving data quality, alignment, and usability across the investment process. Asset owners are not simply looking for more data. They are looking for data that can be trusted, connected, and applied in the moments when investment decisions need to be made.

Prioritizing decision-ready data over real-time feeds

As these priorities evolve, asset owners are placing greater emphasis on data that is available at the right moment for decision-making, rather than continuously streaming real-time information. While the importance of timeliness has increased, our 2026 study found that demand for more real-time data has declined to 23% from 36%, indicating a more targeted view of what “timely” data means in practice.

This shift reflects a growing recognition that real-time data is not always necessary or possible within institutional investment workflows. Asset owners often manage opaque asset classes like private markets, where information may not be available in real time, as well as complex operational workflows such as governance reviews, reporting cycles and portfolio oversight. In these contexts, the value of data depends less on constant immediacy and more on whether it is structured, aligned, and accessible when needed.

Instead, asset owners are prioritizing data that is available for specific decision points, structured and accessible for analysis, and aligned across systems to provide a coherent view of the portfolio. The decline in demand for better integration of applications, which fell to 22% of asset owners surveyed in 2026 from 43% of asset owners surveyed in 2025, further suggests a move away from broad technology expectations toward more outcome-driven requirements.

As a result, the concept of “decision-ready data” is becoming more relevant than simple measures of speed or volume. For asset owners, the goal is not necessarily to receive every feed in real time, but to ensure that the right information is connected, reliable and usable at the point of decision-making.

AI’s role in the quest for stronger data foundations

AI is increasingly viewed as a tool that can enhance the value of data by improving how insights are generated, interpreted and delivered across the investment process. Its most immediate relevance lies in supporting areas where asset owners are already focused, including risk monitoring and analytics, reporting and data interpretation, and the organization and use of investment research.

However, the effectiveness of AI is directly dependent on the quality, integration and accessibility of underlying data. More than ever, the concept of “garbage in, garbage out” can severely limit AI’s usefulness. Fragmented or inconsistent data environments limit the ability of AI tools to produce reliable and meaningful outputs, making data quality a prerequisite for more advanced analytics.

As a result, AI is best understood as an enabler of stronger analytics and insight, rather than a standalone solution to data challenges. Its value depends on the strength of the data foundations beneath it. When those foundations are weak, AI may struggle to deliver outputs that are complete, accurate, or actionable. When they are strong, AI can help asset owners more effectively identify patterns, interpret information and surface insights across increasingly complex investment processes.

 AI may also help identify poor, incomplete or dated data, where appropriate controls, governance and data quality frameworks are in place.. In this sense, it may not only help asset owners use data more effectively, but also help strengthen the quality of the data environment itself.

Bridging investment research and decision-making

The trends in data and analytics point to a broader shift toward more integrated investment operating models, where data, research and decision-making are more closely aligned. Asset owners are seeking to strengthen their ability to access and organize large volumes of data, support research and due diligence processes, and generate insights that inform portfolio construction and risk management.

This need is becoming more important as investment teams manage larger volumes of information across more sources, asset classes, and internal workflows. Research and insights often remain fragmented across tools, datasets and team processes, limiting their accessibility, scalability and overall impact. As investment processes become more complex, this fragmentation can hinder the ability to translate insights into timely, informed decisions.

Addressing this challenge requires a more connected approach—one that better links data, research and analytics across the investment lifecycle to support more consistent and actionable outcomes. For asset owners, improving data readiness is not only about strengthening reporting or operational efficiency. It is also about creating a clearer path from information to insight, and from insight to investment action.

Competitive advantage from actionable insights

Asset owners are placing increasing importance on their ability to manage and use data effectively, driven by the need for greater agility, transparency, and risk oversight. The most pressing challenges—integration, accuracy and timeliness—highlight the importance of building a strong data foundation that supports the full investment process.

At the same time, continued investment in analytics and reporting underscores the need to convert data into meaningful and actionable outputs. AI is beginning to play a role in enhancing this process, but its impact will depend on the strength of underlying data environments.

The asset owners best positioned for the future will be those that can move beyond data accumulation to achieve true decision readiness, where information is connected, trusted and available when it matters most. Connected, trusted and available data can enable a scalable and repeatable process with a built-in feedback loop, helping asset owners translate stronger data foundations into more actionable insights over time.