In today’s environment, data is produced and consumed at a rapid pace. However, the way it is currently being stored, accessed, and processed is inefficient.
Migration to the cloud promises to change this reality for financial institutions, but there are several obstacles tied to digital transformation that must be addressed in the process.
This is an extract from ‘Accelerating Enterprise-Wide Innovation with Cloud Migration and Data Governance,’ a Finextra Research impact study in association with Collibra, part of the AWS Cloud Series.
According to a 2018 study by PWC, the firm predicted that by 2020, around 1.7 megabytes of new information will be created every second for every human being on the planet, and that the accumulated digital universe of data would grow from 4.4 zettabytes in 2018 to around 44 zettabytes - an eye watering 44 trillion gigabytes.
This exponential increase in data is set to continue in the coming years, especially as adoption of mobile technology rises.
The risk and opportunity introduced by big data to the financial services industry is unparalleled. As the most data-intensive sector in the global economy, the ability of financial institutions to obtain, process, and analyse their complex data assets - structured or unstructured - is becoming fundamental to market success and remaining competitive. Moving data to the cloud with a partner such as Collibra establishes a governance foundation for banks as ungoverned data lakes can quickly become data swamps.
Data intelligence vendors can provide a platform that helps banks gain a unified view of data assets to unlock their true value. These technology companies can help improve trust in data to strengthen analytics and hasten time to insight through machine learning powered features such as automatic data classification, guided stewardship, and auto-discovery functions.
Cloud migration and services such as those offered by Amazon Web Services (AWS) should be at the centre of banks’ digital transformation, but challenges around lack of executive alignment, technical skillsets, and data lake experience must be overcome in order to become masters of their data.
Exploiting the evolution of cloud innovation
Because legacy systems are no longer powerful enough to process large, complex data sets, more banks today are turning to the cloud to drive real-time analytics and enable smart decision making. Cloud services such as those offered by AWS provide banks with the flexibility and scalability to implement data lakes and global cloud data solutions. Using the cloud, banks avoid long-term and expensive on-premises deployments. They can easily accommodate spikes in demand, cost-effectively store growing volumes of data, and seamlessly connect with new tools and technologies to enable faster time to insight.
Beyond the siloes, towards easier compliance
Banks have enormous amounts of data, but traditionally this data has resided in siloes and remained untapped. Despite investing heavily in data collection and processing technologies, such as data warehouses and business intelligence, banks are not maximising the potential of their rich data sets.
With increasing customer expectations and competition from fintech players, financial services organisations need to change their approach to data to better understand customers and gain competitive advantage.
Global regulatory pressures emerging from the increasing awareness of data value are an equally significant driver of data management and data governance needs. The recent influx of new regulations such as Basel III, FRTB, MiFID II, and FATCA, have forced banks to disclose more diverse, granular data to central banks and regulators - all contributing to growing internal recognition of the inherent limitations of existing data policies and data solutions.
The substantial liabilities associated with non-compliance around data are highlighting the significant limitations of infrastructure within existing solutions. These financial and reputational repercussions are forcing organisations to better understand their data, the value inherent with that data, and how the data is collected, controlled, reported against, and made accessible to legitimate access requests (from both internal stakeholders and external regulators).
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