A data management platform for banking gives financial institutions a way to collect, organize, secure, and analyze information from multiple sources, including core banking systems, mobile apps, payment channels, CRM tools, and open banking APIs.
In banking, the value of such a platform goes beyond reporting. It supports faster decisions, stronger compliance, fraud detection, customer personalization, and quick access to trusted information.
The scale is easy to see in real examples. PKO Bank Polski’s IKO app serves around 8 million users, handles 32 million daily interactions, and supports 361 transfers per minute. This gives a sense of how much customer and transaction data a bank may process every day. KIR’s PSD2 Hub adds another side of the same challenge: connecting more than 300 Polish banks with third-party providers under open banking rules.
This is why data management platforms are critical in banking and financial services. Volumes are growing, and so are the rules around using them. Financial companies need a reliable way to keep information accurate, secure, and easy to access. With so many options on the market, choosing the right solution can be challenging.
This guide covers leading platforms for stronger data management and smarter business decisions.
Is there a need for data management platforms?
As companies generate and use data faster than ever, ensuring data quality through effective management solutions and platforms is essential to make sense of the enormous amounts of information.
By collecting and analyzing different types of data, including first-party data collected directly from users, and third-party data from external providers, companies can find out how many users they have and which ones are paying customers. Businesses will know how often clients use their product, what they’re looking for, and which features are the most popular. This information helps brands design a better user experience, create a user-friendly onboarding process, improve customer support, and strengthen customer relationships.
It also lets companies identify and address security concerns, detect suspicious activities, understand business risks, and optimize pricing plans. Essentially, data management platforms provide the foundation for making informed, strategic business decisions tailored to each customer segment.
What are the main benefits of using a data management platform?
At a high level, businesses that use DMPs can see a wide range of benefits that support smarter decisions, stronger collaboration, and faster business outcomes. Some of the key advantages include:
- Connected data: Information from marketing, sales, product, and support is brought together in one place, giving teams a clear, shared view that saves time and improves collaboration.
- Better data quality: Unorganized or incomplete data slows teams down. A DMP makes sure the data that’s being used is accurate, consistent, and up to date. With clean and reliable information, including properly handled data classification, it’s easier to make smart, confident decisions.
- Lower costs: By removing duplicate data and streamlining how it’s handled, businesses can reduce operational costs. A DMP also allows teams to move faster from insights to action, turning raw data into real business value more quickly.
- Stronger security and compliance: Security and privacy rules like GDPR or CCPA are built into most platforms. With access controls, encryption, and clear policies, a data management platform protects sensitive information and lowers legal risk.
- Task automation: A good data platform handles routine work like sorting, tagging, or syncing data automatically. That means less manual work for teams and more time to focus on high-impact tasks.
- Clearer insights and improved targeting: A DMP organizes insights in a way that makes analysis easier. Marketing teams can better understand customer data, spot trends, and personalize content or marketing campaigns to boost engagement and loyalty, and ultimately increase revenue.
- Tool integration: Data management platforms can connect with other tools like existing software like CRMs and analytics or marketing platforms. As a result, the workflows are smoother and less time is wasted switching between systems.
How to choose the right data management platform (DMP)?
Selecting the right data management providers can be challenging for organizations striving to achieve their goals. To get the best results, match the platform’s features with the business’s specific needs. Consider the following key factors:
- Factor #1: Define the company’s business needs: Failing to do so might result in investing in platforms that don’t have the features the business needs.
- Factor #2: Examine data integration: There should be the option of integrating various data sources to create unified customer profiles and ensure the ability to integrate data across departments and platforms. It’s particularly important for larger companies and enterprises where data is often scattered across multiple systems within the organization.
- Factor #3: Prioritize data security: A reliable data platform should be equipped with security tools to protect data from internal and external threats.
- Factor #4: Emphasize data cleansing:Advanced platforms are necessary for accurate, consistent, and reliable data organization.
- Factor #5: Highlight Business Intelligence (BI) analytics: To easily examine data, choose a platform that provides straightforward access to automated data analytics or visualization. This allows companies to analyze data effectively, identify trends and patterns, and make more informed decisions. This is a key component in driving digital marketing strategies that adapt to shifting consumer behaviors.
- Factor #6: Insist on a configurable interface:A modern, intuitive interface offering a personalized experience is what every good provider should have as well.
- Factor #7: Prioritize real-time capabilities: To identify and resolve issues more quickly, a data management platform must have the ability to manage data in real-time as it’s created.
- Factor #8: Address data storage needs: Efficient storage options, including disaster recovery, are vital for accessibility and scalability, ensuring data protection against security threats.
Why banking demands more from a data management platform
Generic data management platforms and CDPs are often built for broad business use. Banking needs something more specific. Financial institutions deal with stricter rules, more sensitive data, higher transaction volumes, and more complex integrations than many other industries.
- Reason #1: Compliance is one of the biggest differences.
A retail company may mainly focus on GDPR. A bank also has to consider AML and KYC checks, MiFID II reporting, DORA requirements, and other sector-specific rules.
These regulations often apply to the same customer and transaction data, so the platform must support strong governance, access control, audit trails, and clear data handling rules.
- Reason #2: PSD2 and open banking add another layer.
Account data can move between banks, third-party providers, and API systems. Banks must track its source, permissions, and consent status at every stage. For a general platform, this may look like a simple integration task. In banking, it is also a compliance and security issue.
- Reason #3: Performance is just as important.
Fraud detection often has to work in real time, not after a batch report is ready. When banks process millions of records and transactions, the platform must support fast reads, writes, and analysis without slowing down key services.
- Reason #4: Multi-currency settlement and reconciliation also create extra complexity.
Banks need to match transactions across currencies, systems, counterparties, and cut-off times, while keeping a full audit history.
For banks and financial companies, these requirements shouldn’t be treated as optional extras. They are core features. The platforms reviewed below differ in how well they support this level of banking data management.
Banking data platform architecture: The three-tier pattern
Most banks don’t lack data. The real challenge is getting it to the right systems quickly, accurately, and at the right time, whether for fraud detection, mobile banking or regulatory reporting.
Tier 1: Ingest
The ingest layer collects data from transaction streams, Open Banking API calls, core banking exports, payment feeds, and customer events, often in varied formats and at uneven speeds. It must capture incoming records reliably, remove duplicates, and track their origin. This is especially crucial in financial services, where regulators may later ask how specific data was collected, changed or used.
Neontri’s work on the KIR PSD2 Hub shows the importance of a reliable ingest layer. The solution connected more than 300 banks with third-party providers, with each institution sending data in its own format and under strict PSD2 timing requirements. By standardizing incoming records and tracking their origin, the ingest layer gave the rest of the platform consistent data to work with.
Tier 2: Processing
Once data enters the platform, it has to be cleaned, enriched, and prepared for use. This layer supports tasks such as fraud scoring, AML pattern detection, real-time balance updates, reconciliation, reporting, and historical analysis.
At PKO Bank Polski’s scale, with around 70 million records processed per day, performance becomes critical. Apache Cassandra in banking data management is useful for high-volume environments where systems have to process large amounts of information quickly and reliably. Combined with stream processing engines, it can support both real-time tasks and batch workloads without slowing down key services.
Tier 3: Serving
The processed data then flows out to wherever it’s needed. A mobile banking app may require a fast response, while compliance teams rely on structured reports and analysts use data warehouses for deeper analysis. This layer provides the right data in the right format without each team building a separate pipeline.
The three-tier model is not new, but banking has little room for delays or errors. A slow query can affect compliance, payments, fraud detection or customer trust. That is why the architecture must be built for reliability from day one.
Regulatory requirements for banking data platforms
Banking data is highly regulated, so the platform must do more than store and process information. It has to track access, consent, security, and reporting across every system that uses the data.
- PSD2 and Open Banking: These require banks to share customer account data with authorized third parties through secure APIs. For a data platform, this means handling API-based data flows, tracking consent, and keeping records of what data was shared, with whom, and when.
- GDPR: Banks must be able to find personal data across their systems, respond to deletion or access requests, and show a lawful basis for processing. A data platform should support classification, lineage, and subject access requests, so teams can handle GDPR obligations without searching manually through disconnected systems.
- DORA: It deals with digital operational resilience. Since 17 January 2025, financial institutions in the EU have had to show that their ICT systems can withstand, respond to, and recover from disruptions. For data platforms, this calls for tested recovery procedures, clear continuity plans, and strong third-party risk controls.
- AML and KYC: Anti-money laundering and know-your-customer obligations place responsibility on banks to monitor transactions, verify customers, detect suspicious activity, and keep reliable records.
- MiFID II: This applies to investment services and covers client order records, related communications, and transactions. A data platform should keep this information complete, easy to find, and available within required timeframes.
- PCI DSS: This standard covers payment card information. A banking data platform should protect it with encryption, limited access, secure network design, and audit logs wherever it is stored, processed, or shared.
Business Intelligence (BI) platforms
Instead of relying on gut feelings, BI platforms empower companies to leverage data for smarter business decisions. Serving as a central hub for a company’s data, these systems help businesses gather, view, and understand all of their data in one place.
Equipped with dashboards, charts, and various tools, they let businesses easily digest large amounts of information and gain valuable insights into their operations.
Moreover, user-friendly interfaces put control in the hands of users which allows them to customize dashboards to suit their specific needs. Stakeholders and decision-makers are also empowered to cherry-pick the key performance indicators (KPIs) and visualizations essential for their decision-making processes.
1. Power BI
Developed by Microsoft, Power BI is a powerful business intelligence platform. It simplifies data management by handling integration, cleansing, and security, allowing companies to transform raw data into clear and actionable insights. Power BI also offers secure storage and convenient access to data, making it readily available whenever needed. Should important data be accidentally deleted, Power BI’s Query Editor feature can effortlessly rewind and restore everything to its original state.
Beyond management, Power BI excels in data visualization. It enables the creation of interactive dashboards and reports from various sources, making it simple to spot trends and make informed decisions. The platform offers built-in reports and also supports third-party Power BI report templates for more tailored reporting experiences.
Power BI also offers strong integrations with Microsoft Excel, PowerPoint, and Teams which enhance collaboration and workflow efficiency. Businesses can seamlessly import data from Excel spreadsheets into Power BI for analysis and then export captivating data visualizations back to their PowerPoint presentations. This creates a cohesive data environment, streamlining the process of utilizing insights across projects.
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