The challenges of data lifecycle management in financial services

The financial services sector generates enormous amounts of data, presenting difficulties that must be overcome to maintain compliance and sustainability

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.

The missing link between enterprise data and big data

Recent technological innovations, such as artificial intelligence, machine learning, and natural language processing are helping businesses make sense out of big data. However, the nature of big data is such that it typically lacks enterprise-grade governance, security, and full lifecycle management. Big data is routinely stored in data lakes or cloud object storage systems, and while these provide affordable and highly scalable storage, they rarely interface well with the enterprise data management systems that have been in place for decades.

For many financial services firms, the rapid rise of big data is a missed opportunity. Most big data is dark data, which means it is not being operationalized for deriving valuable insights via sentiment analysis and other advanced analytics. But there is an even more serious problem, especially in the case of finance, where trust, integrity, and transparency are especially crucial. The missing link between structured enterprise data and big data is not just a barrier to digital transformation – it is also a barrier to lifecycle management, compliance, and security.

Being subject to stringent regulatory measures, including those specific to the financial sector, organizations often find that breaches of compliance stem from big data. For example, GDPR legislation requires that companies respond to requests for access to data or erasure within 30 days. This is a lot more complicated when tackling the sheer scale of big data. Furthermore, certain financial information must be retained for a specific period of time, but there is no easy way to ensure data retention periods are being adhered to when there is a lack of data lifecycle management in place.

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.