The ability to extract actionable, real-time insights from multi-source data is an increasingly important success factor for wealth management businesses. Here, we introduce data virtualisation as a way for them to build this ability and why they should consider adding it to their digital tool belt.
Conceptually, data virtualisation involves the consolidation of multi-type, multi-source data into a logical layer without transferring the data itself. Special middleware allows users to virtually access and analyse the data, which remains in its original sources.
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Traditional approaches to data integration, in contrast, typically involve extraction, transformation, and loading (ETL) processes for bringing disparate data together into a single repository, such as a data warehouse or data lake. The data is extracted from original sources, transformed into a format and structure that fits the target system, and finally loaded into that system.
The data virtualisation process
The first steps in the data virtualisation process are to connect to the original data sources and create a virtual representation of the data. The creation of a virtual metadata layer describes the structure and location of the data without physically copying the data itself. Via this centralised logical layer, data stored across multiple heterogeneous sources can be accessed in near real-time from practically anywhere.
Using solutions like digital wealth platforms, wealth professionals can present data accessed this way in dashboards, which can be customised according to clients’ unique requirements. Users can pull up-to-date reports, manipulate data, and perform advanced analytics.
Top three benefits of data virtualisation benefits for wealth managers
- Real-time access
For many wealth data use cases, ETL is unnecessary. Instead of physically moving data to a new location, data virtualisation allows users to access and manipulate source data through a virtual logical layer in real-time, allowing faster, more accurate investment decision-making and greater productivity.
- Comparatively low cost
Implementing data virtualisation requires less resources and expenditures versus running a separate storage system for consolidated data. Also, in comparison to the ETL approach, virtualised data does not need not be replicated and stored in order to be accessible. The replication process is often expensive, can result in duplicate or inaccurate data that can skew analysis and requires ever-growing storage capacity. Therefore, insights from virtualised data are often more accurate, available faster, and less costly to obtain.
- Greater agility
Data available through a single virtual layer can be flexibly used for a variety of wealth management use cases. Depending on their requirements, users can design and run whatever reports and analyses they need without worrying about data formats. When integrated with visualisation tools virtualisation allows users to see real-time wealth data in easy-to-understand forms such as charts or graphs.
Structured and unstructured data
Virtualisation of both structured data – typically consisting of numbers and other values from sources like asset price feeds – and unstructured data from sources like social media allows wealth managers to access a wide variety of information from one, easily accessible layer providing a single source of truth.
Data virtualisation challenges
Leveraging data virtualisation solutions, like any other digitally transformative tools, requires investment. In addition to the costs of accessing a data virtualisation solution itself, staff training and professional advice on selecting a solution may be necessary.
Even so, such investments are often worthwhile. Over the long term, data virtualisation can offer a cost-efficient path towards better insights from multiple data sources, centralised data security, effective compliance with data governance policies, and a competitive advantage for data-driven wealth managers.
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