Charting the Data Maze: Ensuring Quality in a World of Complexity 

Data quality plays a vital role in today’s data-driven world. However, as the volume and complexity of data sets increases, combined with fewer resources and a more cost-conscious mentality, maintaining trust in data is an ongoing challenge for investment managers.

At the recent InvestOps USA event in Orlando, FINBOURNE’s Christopher Farrell and a series of data experts explored the characteristics of a ‘data-first’ mindset, and a culture where data is not just an afterthought but a strategic asset driving decision-making at every level.

System-first to data-driven

There is an ever-increasing amount of data available, but companies often cannot organise and analyse it to optimise decision-making or identify possible opportunities efficiently. To fill gaps or resolve problems in the short-term, traditionally firms have looked for technology solutions that offer a ‘quick fix’.  However, the shift is now moving towards a ‘data-driven’ approach, to solve data integration and management challenges at their roots.

Central to this approach is data democratisation – the process of making data accessible, understandable, and actionable to all stakeholders across the organisation, in a secure way.  It involves breaking down silos and integrating data from disparate sources to create a comprehensive understanding of the data to fuel well-informed investment decisions. Democratising data in this way fosters innovation and agility, enabling asset managers to harmonise high-quality data across different platforms.

ETL to Data Virtualization

Many firms have a certain set of systems to control data from an operational perspective and another set from an analytics perspective. However, as data sets grow, syncing those two is crucial to gain a holistic view of what is happening in your organisation.

Achieving this does not require a re-design of the entire system landscape, and instead firms can leverage an interconnected, flexible data ecosystem which allows data to be connected from different applications, validated for quality and consistency, and then distributed to those that need it.

In doing so, firms are increasingly transitioning away from traditional ETL (Extract, Transform, Load) and towards advanced data delivery methods such as data virtualization.  Unlike ETL, data virtualization enables real-time access to data from disparate sources without the need for duplication. This agility translates into reduced complexity, lower costs, and a faster time-to-insight, as organisations can derive value from high-quality data more efficiently.

Enhancing data lineage with bitemporality

In the face of the increasing complexity and diversity of data sources, data virtualization and data lineage techniques are interconnected components of a modern data management ecosystem, working together to enable organisations to access, integrate, and govern their data effectively.

Data lineage is crucial to ensuring that the movement of data is traceable and understandable, regardless of its source or destination. Leveraging bitemporality, users can analyse trends, track performance, and understand the impact of past events on investment strategies, to allow for better risk assessment, compliance monitoring, and performance attribution, ultimately leading to improved portfolio management and client outcomes.

Fostering a culture of responsible data management

By viewing data as a strategic and durable resource, rather than just the systems that facilitate it, organisations will recognise the potential for long-term insights, historical analysis, and future innovation.  A data-first approach to transformation encourages future investment in data quality and governance, ensuring that valuable insights can be derived from data regardless of its nature or complexity.

To learn more about FINBOURNE’s technology solutions for asset managers, get in touch.