Data Discovery in Banking: Get Business Value from Siloed Data
Share this article
In banking, data is often siloed across various systems, departments, and even physical locations, making it challenging to manage and use effectively. Seamless data discovery in banking can help improve operational efficiency, reduce costs, improve compliance, and make informed decisions to stay competitive.
See How Atlan Simplifies Data Discovery – Start Product Tour
This article explores the role of data discovery in banking, its benefits and challenges, and how a metadata control plane can help.
Table of contents #
- What is data discovery in banking?
- Business benefits of data discovery in banking
- Challenges of data discovery in banking
- Data discovery in banking: How a unified control plane for data and metadata can help
- Data discovery in banking: Unlock business value of your data estate
- Data discovery in banking: Related reads
What is data discovery in banking? #
Data discovery in banking is all about analyzing data collected from various sources to spot trends and patterns. It helps you find the right data quickly, with adequate context. You can visualize complex datasets, identify trends, and understand customer behaviors.
More importantly, you can leverage customer data to improve your business outcomes – enhanced customer satisfaction, risk management, and operational efficiency.
According to ISACA, a global community of IS/IT professionals, banks rely on customer data for everything from “routine day-to-day banking to mortgage lending, commercial banking, wealth management, investment products, and enterprise trading in capital markets.”
Here’s a scenario. Effective data discovery happens when all of your customer data is under one roof – a single pane of glass. Data teams at banks can use this data to understand the products a customer is currently using, such as checking or savings accounts.
By analyzing factors like income, lifecycle stage, and other relevant data, bankers can assess whether the customer is a suitable candidate for cross-sell or upsell opportunities – investment products, credit cards, loans, and insurance, to name a few.
Some of the most common use cases of data discovery in banking include:
- Customer 360° view to deliver personalized financial recommendations
- Regulatory reporting to ensure compliance with data privacy regulations
- Risk management to spot fraud patterns and assess credit risks effectively
- Data-driven insights on products, pricing, customer preferences, and more
Before we proceed, it’s vital to note that data discovery is different from data exploration and, in data analysis, it usually comes after data exploration. Data exploration helps you assess datasets and frame questions for analysis, while data discovery focuses on identifying trends to answer specific questions.
“While data exploration focuses on understanding a dataset’s characteristics and patterns, data discovery involves using the prepared data to answer business questions.” - Snowflake
Also, read → Data discovery 101
Business benefits of data discovery in banking #
Effective data discovery brings great business value by solving some of the biggest challenges in banking. We already saw an example of effective data discovery earlier – increasing revenue streams by spotting relevant upsell/cross-sell opportunities.
Now, let’s explore more such business benefits of data discovery in banking:
- Cost savings
- Enhanced compliance
- Personalized customer experiences
- Improved operational efficiency
- Faster innovation
Cost savings #
Efficient data discovery in banking helps reduce operational costs by automating resource-intensive processes. For example, a bank could use automated data discovery to identify duplicate records in customer databases, reducing storage costs and manual cleanup efforts.
Enhanced compliance #
Banks operate under stringent regulatory frameworks like GDPR, PCI DSS, and Basel III. Data discovery can streamline compliance by offering clear visibility into data usage and lineage.
For instance, you can identify sensitive data across your data ecosystem and verify if it’s adequately encrypted and masked, ensuring compliance with data privacy regulations.
Data discovery also plays a crucial role in data governance.
“You can use data discovery to gain greater visibility and understanding of the organization’s sensitive data, secure sensitive data with appropriate controls and policies, and support compliance, privacy, and ethical data use.” - Heidi Shey, Principal Analyst at Forrester
Personalized customer experiences #
You can use unified data to analyze customer behavior and provide hyper-personalized services.
For example, spotting spending patterns helps promote relevant savings or credit offers. Your recommendations can also be proactive — a customer frequently using their card for international travel can receive a tailored offer for a travel rewards credit card.
Improved operational efficiency #
Using a single pane of glass to discover data within your banking enterprise reduces manual work and allows faster decisions. For example, risk teams can quickly analyze customer profiles and assess insurance applications, speeding up the approval process.
Faster innovation #
Data discovery can help in unlocking insights that drive innovation in banking products and services. For instance, a bank could identify a gap in affordable micro-loan offerings, leading to the launch of a new product targeting a niche demographic.
How Austin Capital Bank launched a new product and improved its customer service with seamless data discovery #
Texas-based Austin Capital Bank was about to launch FreeKick, a groundbreaking credit-building product for minors. But crucial to success would be a timely launch before graduation season.
With effective data discovery from Atlan’s unified control plane, the bank’s client service team could access account-level data through a self-service interface, avoiding weeks of manual query dependencies.
Ian Bass, the Head of Data & Analytics at Austin Capital Bank, notes that his team spent just two hours delivering a solution. The alternative would have been painful: Turning their data team into an on-demand service center for basic client data requests, and taking their eye off more strategic engineering investments.
“Austin Capital Bank sees Atlan as its window to the world, better understanding their fast-evolving technology and processes, and the impacts that changes to them will make.” - Ian Bass, Head of Data & Analytics
Read more → How Austin Capital Bank is launching new products at unprecedented speed
Challenges of data discovery in banking #
Despite the implied benefits, implementing data discovery in banking comes with many challenges. These problems often happen because of outdated systems, regulatory needs, and disconnected teams. Overcoming these challenges is necessary for banks to fully unlock their data’s value.
Top challenges that impact effective data discovery in banking include:
- Fragmented, siloed data spread across systems
- Limited data visibility
- Poor data quality
- Regulatory and privacy concerns
Fragmented, siloed data #
Banks often use both legacy and modern systems, leading to data chaos and a disconnected data estate.
For instance, fragmented data across multiple systems makes it difficult to get a unified view of the customer. If the risk assessment teams struggle to access critical loan default data spread across systems, this can delay credit approval processes and increase business risks.
Another example is that of a legacy system managing fixed deposits, which might not fully integrate with the modern mobile banking platform.
This disconnect makes it difficult to see all of a customer’s information in one place, affecting the customer’s experience with the bank and also making it harder for the bank to spot opportunities for proactive recommendations, upselling, and cross-selling.
Limited data visibility #
One of the biggest consequences of a siloed data ecosystem is a lack of transparency and visibility into data flows across systems. Disconnected data systems make it hard for teams to leverage their data effectively.
For example, during a market downturn, a bank’s wealth management team might need to analyze customer portfolios to offer personalized investment advice. If the team cannot locate or understand all the relevant data due to limited visibility, it may miss opportunities to engage customers and mitigate losses.
Poor data quality #
Data inconsistencies and inaccuracies can affect decision-making and cause compliance issues.
For instance, during an audit, banks may find incomplete or mismatched records, failing to meet AML (Anti-Money Laundering) requirements. Such data quality issues, if left unfixed, can invite regulatory fines and affect the bank’s reputation.
Regulatory and privacy concerns #
With stringent regulations like GDPR, CCPA, and PSD2, ensuring compliance while managing sensitive customer data is a major requirement for banks, and also one of their biggest challenges.
For instance, a bank must respond to a customer’s request to access or delete personal data within a specified time frame. However, poor data discovery can lead to delays in locating the data across systems, exposing the bank to potential fines for non-compliance.
Plus, operational inefficiencies arise when bank employees spend excessive time searching for data instead of acting on insights.
To overcome the above challenges, banks must adopt modern data discovery practices and technology that improve business operations, ensure better collaboration, support governance efforts, and foster innovation.
Let’s see how a unified control plane can drive data discovery in banking, producing a clear picture of the data assets with relevant context and collaboration options.
Data discovery in banking: How a unified control plane for data and metadata can help #
A unified control plane for the data and AI stack integrates trust and context into the digital fabric. Banks can leverage this control plane to transform a fragmented data landscape and glean useful insights, thereby improving overall efficiency, compliance, and cost optimization.
The key features of such a control plane for data, metadata, and AI would include:
- Google-like search for data and metadata: Simplifies finding relevant data by providing you with a Google-like search and browse experience, so that you can search using context.
- Automated profiling: Auto-profiling for multiple assets in a database or schema at one go, alongwith auto-generated statistics, making it easier to understand data quality and credibility
- 360-degree visibility with embedded collaboration: A unified control plane is a single platform for data discovery, cataloging, and governance. This gives a 360-degree view of all data assets and traces their flow throughout your data estate, so that you can better understand your data. Such a platform also integrates seamlessly with your daily workflows, enabling embedded collaboration and enhancing the overall user experience.
- Contextual discussions: Streamline collaboration and communication with features like in-line chats for resolving issues and asking questions about data assets or glossary entries. You can also integrate data-related notifications and alerts into daily workflows, such as delivering updates via Slack messages.
- Granular access controls: Enable data democratization without compromising data security and privacy. You can ensure that your data is secure by configuring permissions based on user roles, projects, data domains, and more.
Also, read → A single source of truth for data discovery
Case study: How a Brazilian banking giant set up a single pane of glass to discover, understand, and use data #
Brazilian banking and insurance giant Porto had a disorganized catalog, which made it harder to get maximum possible ROI from their data stack, and to achieve their data literacy goals. Porto has roughly 14,000 employees, and has been in operation since 1945, leading to siloed infrastructure and knowledge.
Further complicating things was their existing data catalog. Porto’s organizational structure had data scientists embedded within multiple teams across the enterprise. They were responsible for locating and sourcing data for advanced modelling. With the existing catalog, they found it difficult to understand the data available to them, and whether that data was appropriate for modeling.
Here’s how Porto’s chief data scientist explains the data discovery struggles of his team.
“You needed to determine who knew in which database your data was located, assuming it existed. So you were trying to find someone that you didn’t know existed, and you didn’t know if that person wanted to talk to you or had the time. If you found that person, you wouldn’t find anything that resembled metadata. You wouldn’t know what a column meant.” - Pedro Ribiero, Chief Data Scientist for Porto’s R&D Division
Porto wanted to enable do-it-yourself data discovery by setting up a single pane of glass to discover, understand, and apply Porto’s data, in less time than ever before.
Using Atlan, Porto built that single pane of glass – Datapedia. Datapedia has enabled Porto’s data scientists and business users to locate all available data, and the context around it, in a simple search.
Also, instead of wasting time searching for data and understanding its relevance, data scientists can quickly review a table’s definition and either refine their search or start developing an improved data model right away.
Datapedia already has 300 users within four months, and the goal is to scale it to 1000 users by 2025.
Read more → How Porto set up self-service data discovery in less than six weeks
Data discovery in banking: Unlock business value of your data estate #
Data discovery is transformative for banking, enabling personalized customer experiences, cost efficiency, revenue opportunities, and compliance with data privacy regulations. By integrating data across siloed systems, banks can address challenges such as fragmented data, limited visibility, and regulatory hurdles.
The examples of Austin Capital Bank and Porto highlight how a metadata control plane can enable effective data discovery, driving faster product launches, self-service access, and operational agility.
As the banking industry evolves, adopting a unified control plane for data, metadata, and AI is central to data discovery, and the key to unlocking growth and delivering value.
How organizations making the most out of their data using Atlan #
The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:
- Automatic cataloging of the entire technology, data, and AI ecosystem
- Enabling the data ecosystem AI and automation first
- Prioritizing data democratization and self-service
These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”
For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.
A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.
Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes #
- Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
- After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
- Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.
Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.
Data discovery in banking: Related reads #
- What is data discovery? Understand the concept of data discovery and its importance in finding and organizing data.
- Data governance in banking: Discover best practices for managing and protecting customer data.
- What are data silos? Explore how data silos hinder decision-making and how to overcome them for better data collaboration.
- What is data governance? Learn how data governance is essential in banking for regulatory compliance and securing customer data.
- How enterprise data catalogs drive business value
- Unified control plane for data: The future of data cataloging
- Data Governance in Fintech: Outcomes & Best Practices
- Financial Data Governance: Strategies, Trends & Best Practices
- Key Objectives of Data Governance: How Should You Think About Them?
- Data Governance Strategy: How To Get Started?
- Data Governance Framework: Examples, Templates, Standards, Best Practices & How to Create One?
- Data Governance and Compliance: Act of Checks & Balances
- How to implement data governance? Steps, Prerequisites, Essential Factors & Business Case
- How to Improve Data Governance? Steps, Tips & Template
- 7 Steps to Simplify Data Governance for Your Entire Organization
- Automated Data Governance: How Does It Help You Manage Access, Security & More at Scale?
- Enterprise Data Governance: Basics, Strategy, Key Challenges, Benefits & Best Practices
- Data Governance in Insurance: Why is it Important and How it Drives Positive Business Outcomes
- Data Governance in Healthcare: Benefits, Framework, and Tooling
- Metadata Management: Benefits, Automation & Use Cases
- Data Governance Policy: Examples & Templates
- Unlocking Data Governance with Data Lineage
- 10 Data Governance Challenges & How to Overcome Them!
- Data Privacy vs. Data Security: Definitions and Differences
- What is Data Reliability? Examples, How to Measure & Ensure!
- Data Compliance: Everything You Need to Know in 2025!
- Data Compliance Management: Concept, Components, Steps (2025)
Share this article