Navigating Judgment Calls with Incomplete Information
Streamlining Investment Decisions: Addressing Data Challenges for Allocators
In the world of investment, allocators are grappling with a multitude of data-related challenges that hinder informed decision-making. These challenges, including fragmented data management, poor data quality, and escalating infrastructure costs, among others, can undermine AI initiatives, increase regulatory and operational risks, and limit the ability to realize full business value from data investments.
One of the most pressing issues is fragmented data management. With disparate data sources and ungoverned access, allocators face siloed insights and workarounds, inhibiting cohesive enterprise-wide decision making and increasing security risks.
Another significant challenge is poor data quality/veracity. Relying on incomplete, outdated, or inaccurate data can lead to flawed decisions, increased costs to fix mistakes, and expose organizations to AI misfires and compliance issues.
High data infrastructure costs are another hurdle. Scaling data capabilities without unified approaches can cause spiraling costs and technical complexity, making it harder to deliver timely and accurate insights.
New CIOs often inherit inherited legacy systems and low trust, challenging rapid transformation and investment prioritization. Additionally, pressure to demonstrate ROI has increased, as CIOs face growing demands to show tangible business impact from technology investments.
To address these challenges, several strategies can be employed. Implementing federated, governed data ecosystems via data products is one such strategy. Instead of moving or centralizing all data, creating domain-specific data products with embedded business logic, quality controls, and access governance can enable unified, trusted data access without disrupting existing systems, supporting scalability and AI ambitions.
Investing in data quality and unification is another crucial step. Prioritizing data unification and quality improvement initiatives can reduce data risk and technical debt that erode AI performance and compliance.
Conducting comprehensive IT health audits can help new CIOs assess infrastructure maturity, cybersecurity posture, technical debt, and stakeholder alignment before action, building a solid foundation and identifying key risks.
Engaging business stakeholders early is also essential. Listening and collaborating with department heads and users uncovers hidden frustrations and aligns IT investments with real business goals, improving trust and impact.
Foster strong technology partnerships is another key strategy. Collaborating with vendors and partners that can help navigate complexity, optimize investments, improve performance visibility, and maximize ROI can be highly beneficial.
Focusing on investments with direct business impact is the final piece of the puzzle. Prioritizing initiatives such as environmental technologies that have shown tangible financial benefits can help balance innovation with measurable returns.
The 2008 financial crisis has led to CIOs having to identify more data, and identifying the three or five main capabilities needed to make better decisions can help allocators utilize data more effectively. Allocators should aim to build a framework that integrates qualitative and quantitative data and normalizes it to support decision making.
However, deriving proactive value from the vast amount of data coming from various channels and sources remains a challenge for many allocators. Up to 90% of a firm's processes may be overlapping, and consistency in data transparency is often considered a desirable feature rather than a necessity. The inconsistent depth of data poses a challenge as it is difficult to compare and analyze data that is not in the same format. Many allocators struggle to make sense of the varying degrees of transparency and detail in the data they receive.
Despite these challenges, it's important to remember that every allocator's decision-making process is unique. Data transparency varies significantly among different investors and investment vehicles, making it crucial for each allocator to understand their unique needs and develop strategies to address their specific data challenges.
Chad Erwin, the SVP of Asset Owners at Backstop Solutions, is one individual who is helping to address these challenges. His work focuses on providing solutions that help allocators manage their data more effectively, ultimately leading to better investment decisions.
In the pursuit of making informed investment decisions, addressing the issue of fragmented data management is crucial. With disparate data sources and ungoverned access, allocators face siloed insights and workarounds, inhibiting cohesive enterprise-wide decision making and increasing security risks.
Another challenge that allocators encounter is the problem of poor data quality. Relying on incomplete, outdated, or inaccurate data can lead to flawed decisions, increased costs to fix mistakes, and expose organizations to AI misfires and compliance issues.
