The challenges of data lifecycle management in financial services

The financial services sector was among the first to implement enterprise data warehousing systems to scale the management of mission-critical data. However, while these architectures were innovative for their time, the proliferation of data coming in from a constantly increasing range of sources has proven extremely difficult to keep up with. Today, financial services organizations face unrelenting pressure from more agile startups who have been able, from the beginning, to manage all these data sources at scale for leveraging insights and reducing risk.

The big challenge that companies now face, especially given that they operate in such a data-heavy vertical, is that data is simply everywhere. While enterprise data might be high-quality, structured, and have clear governance, substantial challenges persist as soon as we step into the realm of big data. Characterized by petabyte-scale data sets comprising text, image, audio, and video, this deluge of information is typically unstructured and, as such, lacks appropriate oversight.

Bridging the gap to simplify data lifecycle management

For many enterprises in the financial services space, a single, consolidated data environment is still just an aspiration. In most cases, data still exists in multiple siloed departments across core applications, data lakes, data warehouses, cloud storage objects, and mobile apps – to name a few. Data is often collected in the first place without proper governance and, in some cases, a lack of a clearly defined business reason to collect the data in the first place. Yet all data, especially if it is subject to privacy rules like GDPR or specific security requirements, must be managed throughout its lifecycle from the moment it is first collected to the moment it is retired.

By bridging the divide between enterprise data and big data, financial services firms can keep a close eye on all their digital assets. With a centralized data environment, in which all data sources are connected and fed into the same database, it will be much easier to apply data lifecycle management policies at scale. This will streamline routine compliance processes like responding to subject access requests (SARs) or erasure requests. For example, if a customer exercises their right granted by GDPR to request deletion of their data, it will be quicker and easier to comply when all the data in question is referenced in one place.

A unified data lifecycle management process should be repeatable and understandable so it can scale across the entire enterprise. This is why it is so important to start at the source. With a strong foundation that validates the needs and requirements for collecting the data in the first place, it should be possible to manage the entire data lifecycle in accordance with both external regulations and internal policies. From data acquisition to secure erasure, the ability to effectively govern the data lifecycle will simplify operations, maintain high data quality, and reduce your data footprint.