APR. 22, 2024
Understand how data lakehouses usher in a new age of digital transformation in banking.
Data is an invaluable resource for financial institutions. It improves customer experiences, improves trading strategies, detects fraud … the list goes on. But we create over 59 zettabytes of data each year. Traditional data management tools can no longer keep up, leaving leaders hunting for innovative solutions.
Data warehouse modernization is the focus for some Chief Information Officers (CIOs). But many more now turn to data lakehouses because they enhance agility without sacrificing performance. It marks a groundbreaking shift, aiming at the data challenges the financial services industry faces.
Understand how data lakehouses usher in a new age of digital transformation in banking. Explore their critical impact on protecting privacy and maintaining security. How do they navigate the vast ocean of unstructured data financial institutions own?
For financial services teams, data is both their biggest asset and obstacle. Financial institutions of all sizes struggle to democratize data without the tools to manage it all.
Financial institutions often operate within a split data environment. They compartmentalize information in different departments. Doing so creates barriers to accessing and analyzing data. It also leads to inefficiencies and increased risks.
Legacy systems compound these issues. Their rigid, outdated structures cannot adapt to modern data processing and analytics.
As financial services grow in complexity and volume, scalability becomes a critical concern. Traditional architectures struggle to scale, resulting in performance bottlenecks and increased operational costs.
Compliance adds another layer of complexity. Financial institutions must navigate regulatory standards while ensuring data integrity and security.
Some financial companies still use data lakes and warehouses to manage their data. Yet, these solutions face challenges with data governance, semantic consistency, and access controls. Without proper governance, a data lake can turn into a “data swamp.” In the swamp, stored data becomes unusable and difficult to navigate.
Data lakes can’t track previous business insights, hindering our ability to leverage historical data. Developers design data warehouses for structured data. Yet, this impedes their ability to accommodate and analyze unstructured data sources.
Additionally, preprocessing and transforming data before loading it into the warehouse is time-consuming.
Data warehouses also rely on pre-load data cleansing. Any errors in the processing can lead to data distortion that persists over time. Organizations thus must demand robust governance, consistent data semantics, and strict access controls.
A powerful solution to our demands for improved data management is a data lakehouse.
These solutions fuse data lake storage capabilities with data warehouse query capabilities. They offer a unified platform for managing and capitalizing on vast quantities of data.
Lakehouse architecture dismantles the data silos plaguing financial institutions. It facilitates a seamless flow of information across departments and ensures compliance with regulations through centralized governance mechanisms.
Financial entities to leverage the power of lakehouses to utilize customer data for tailored financial solutions. Some banks now use alternative data sets, like phone bills and utility payments, to enhance credit decision-making and generate business insights.
Data lakehouse architectures provide financial institutions with a flexible framework. They can more easily navigate new regulations like the EU’s AI Act and democratize data across the organization. These platforms enhance metadata and governance to support data quality and agility by enabling self-service to stakeholders.
The dynamic financial market experiences fluctuations and needs to be able to scale seamlessly — lakehouse architectures offer invaluable scalability. They ensure a more resilient data infrastructure to better account for historical time-series data and the increasing adoption of alternative data sets.
The infrastructure must be able to support higher volume and higher volatility data while continuing to meet compliance standards.
Data lakehouses consolidate diverse data types, ranging from transaction records to customer interactions, into one integrated platform. This approach streamlines data storage and analysis. It also enhances governance through metadata management, lineage, and catalogs.
A unified platform also strengthens security and access management, supporting robust data operations, auditing, and monitoring functions.
The finance industry thrives on timely information. Data lakehouses facilitate timely analysis of streaming data sources like market feeds. Utilizing them makes swift decision-making and effective risk management possible. Lakehouses enable the immediate assessment of transaction fraud risks and other financial anomalies.
Data lakehouses offer a cheaper solution for data storage and processing. Around half of businesses report reduced costs by adopting a data lakehouse architecture. Centralizing data into a unified platform can significantly reduce processing and storage costs while promoting trust and data quality.
As a result, costly and time-consuming data replication techniques can be avoided.
Financial institutions are empowered through data lakehouses by enabling data scientists and engineers to drive customer journey optimization through better segmentation, identity resolution, insights, and decision support for next-best-action activities.
Such enriched understanding paves the way for personalized customer experiences, driving loyalty and retention.
Organized, clean data with robust governance is critical for training AI models. A data lakehouse sets financial institutions up for future data-driven innovation.
Don’t underestimate the importance of solid data management to become AI-ready.
Financial services firms are leveraging lakehouses as a platform within a larger ecosystem to enable advanced analytics, AI and machine learning. These firms are rapidly becoming AI-ready or even AI-powered.
AI is fast becoming the industry standard for financial institutions. Integrating AI and ML into data lakehouses opens up vast opportunities like automating decisions, enhancing operations, and revealing insights humans miss.
AI and ML can analyze datasets to identify patterns and predict market changes. Companies can then personalize their customer interactions, optimize workflows, mitigate risk and fraud, and amplify customer loyalty and retention — becoming proactive rather than reactive.
Related: 7 ways AI boosts your software QA process
Predictive analytics lies at the core of the data management revolution. It enables financial institutions to be more forward-looking. Yet, as noted by McKinsey, the “time-to-market life cycle for [analytics] modeling [is] between 15-18 months.”
Financial institutions can leverage the lakehouse’s data system to their advantage. They can forecast trends, assess risks, and tailor services to customers' changing needs.
The impact of predictive analytics extends beyond improving operations. It also improves customer engagement by emphasizing satisfaction and loyalty above all.
Combining data lakes and warehouses, lakehouses offer a new level of financial service, solving today’s data management challenges when combined with AI and ML technologies.
They enable bank agility by providing a unified data platform for driving operational efficiency, regulatory compliance, robust risk and fraud management, and enhanced customer and employee experiences.
As disparate data is integrated across first-party and third-party data sources organizations can create complete customer views to serve their needs and improve the financial health of their customers.
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