The digital revolution has heralded a new age for the financial services industry. What has stood out the most, however, has been the massive accumulation of data – both professional and personal – that digitisation has enabled. This data exists as a virtual goldmine, offering limitless possibilities to companies that can successfully harness its full potential.
If correctly utilised, the massive cache of data available to every financial services organisation holds the potential to improve every metric. Revenue, customer satisfaction and retention, employee productivity – the possibilities are endless. The true difficulty lies in sifting through this enormous amount of information to make data-driven decisions. Without this critical ability, the result is an enormous pool of indecipherable data, tantalising businesses with knowledge that lies just out of their grasp. Leveraging this information for decision-making needs to be a company’s biggest priority.
Sifting through the Data
This expansion in the volume of available data has been accompanied by an increase in variety. In addition to the variety in the data itself, there is now also a huge variety of data-gathering processes, methodologies, and storage systems, all of which have their own infrastructure and protocols. The load of keeping all these systems operating in sync frequently leads to slowdowns and logjams. In the fast-paced and highly competitive world of financial services – one in which new challengers arise every day – a business that loses ground will not survive for long.
Data mesh, first proposed in 2019 by Zhamak Dehghani of ThoughtWorks, a global tech consultancy firm, provides a solution to this issue.. By applying a new approach to the principles of modern software engineering, data mesh offers analytical data management based on a distributed architecture. It enables end-users to access data at its source, without needing to be transported to a traditional centralised architecture like a data lake or data warehouse.
In a data mesh, each organisational unit within a company has its unique data product owners which own domain-specific data. They handle the modelling and aggregation of this data, aiding data democratisation and self-service data for the business as a whole. This approach enables data to be treated as a product, directly owned by the teams that know the data best and consume it most.
The advantages of this approach are self-evident. Individual departments within an organisation can maximise their efficiency by virtue of having a direct conduit to the data they most require. This data remains decentralised, without any barriers to accessibility or issues of unavailability. In this way, the bottlenecks that typically arise from traditional, centralised infrastructure are removed. But while data mesh offers financial services organisations a chance to maximise the full potential of gathered data, it is important to ensure that no mistakes are made in its implementation.
Growth within Regulatory Frameworks
An innovative approach to data handling lies at the heart of this issue. However, financial services organisations must not overlook standards of governance and compliance in their quest for efficiency. The financial sphere is held to strict regulatory standards, with numerous governmental and industry bodies overseeing the industry. Financial services organisations need to find a balance between efficient data management, safeguarding gathered information, and maintaining consumer trust. These factors all need to be considered when implementing a data mesh strategy, in order to maintain compliance with the latest legal and industry frameworks.
Maintaining interoperability between the domains of different financial services organisations is another important factor to consider. This is achieved through data virtualization, which creates a logical layer between siloed data sources and domain-specific data consumers.
With data virtualization, users can access the data they need at the time of their choosing, with virtually no delay. In contrast, traditional data warehousing and extract, transform, load (ETL) models rely on moving the data from one location to another. By bypassing this process, unnecessary duplication is avoided, bottlenecks are eliminated, and the entire process becomes faster, safer, and more resource-efficient.
Data virtualization’s logical layer also proves helpful in adhering to data compliance regulations. This is accomplished by automating the enforcement of global security policies, such as by masking sensitive personnel and consumer data and only granting full access to individuals with the correct authorisation.
The Future of Financial Services
When all of these factors are taken in conjunction, the vital role of data innovation in financial services organisations is made abundantly clear. Modern architectures such as data mesh play a pivotal role in the future success of businesses in the field. Any company looking to get ahead in this fiercely competitive field must make it a priority to implement these transformational technologies.
The author is Regional VP at Denodo.
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