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Data Management in Finance: Common Challenges and the Need to Modernise

8th April 2024

Speaking at an event in 2022, Nikhil Rathi, CEO of the FCA (Financial Conduct Authority), emphasised the growing importance of data in how they supervise the UK’s financial industry. He hinted at the likelihood of future requirements for organisations to evidence how they gather, store and manage their data. 

Almost two years on, and the FCA’s drive to be more data-led is one shared by many regulatory bodies globally. Businesses need to master their data if they are to keep pace with requirements. 

But that’s not the only incentive—aside from compliance, developing robust data management practices drives value across a financial institution’s entire operations. 

In this blog, we’ll explain why data management is so important, the benefits of modernising, and the typical challenges businesses face in doing so. 

Why is data management so important?

Data management is a term that describes a broad range of activities, including:

  • Data collection
  • Data cleansing
  • Reconciliation
  • Data integration
  • Data storage
  • Data analysis
  • Reporting 

The significance of these tasks for financial organisations cannot be overstated. They are essential for maintaining data accuracy, accessibility and reliability, which forms the cornerstone of operational efficiency, risk management and product development. 

Effective data management practices ensure that reliable data supports all business decisions. Unless decision-makers can trust the data in front of them, it cannot be used to inform strategic planning, conduct operational assessments or anticipate risk. 

The benefits of modernising data management capabilities

Developing a robust and automated data management framework offers enhancements to the following areas:

  • Data quality and accuracy


With the financial industry becoming more data-driven every year, it’s important that leaders and regulators have access to reliable data. Inaccuracies and errors can have far-reaching consequences for decision-making, operational efficiency and regulatory compliance.

By controlling aspects like data quality standards, acceptable tolerances and security standards, businesses can ensure their information is accurate. A data governance framework should provide the opportunity to define and implement standards for critical data validation, cleansing, standardisation, and enrichment processes.  

  • Data security and privacy


Maintaining data security and privacy measures is critical for any business that collects or holds financial data. Many regulations (such as GDPR and PCI DSS) set rules for how customer data should be treated. 

An advanced data management framework involves robust procedures for storing and accessing financial data. These include role-based controls, data encryption, and activity monitoring. By ensuring data security and privacy, businesses can effectively safeguard customer data, minimise breaches, avoid regulatory penalties and retain customer trust.  

  • Risk management


How organisations manage and monitor their data is vital in mitigating risk—both business risk and systemic risk to the financial sector as a whole. Superior data management capabilities allow for accurate risk identification and assessment, whether credit, market, operational, or compliance risk. 

  • Fraud detection and prevention


Data management techniques—such as data analytics and machine learning—help detect patterns indicative of fraud. By analysing transactional data in real-time, companies can identify suspicious behaviour and take proactive, preventative measures. This not only protects the company’s assets but also safeguards consumer interests and enhances trust in the financial system. 

  • Stress testing


Financial institutions conduct regular stress testing and scenario analysis to assess their resilience to adverse events. Stress tests rely on historical and current data to model potential outcomes. 

Effective data management ensures the availability of accurate and reliable data for simulations. This enables companies and regulators to make informed decisions and strengthen risk management frameworks. 

  • Decision-making and business intelligence


Businesses rely on data to make informed decisions about many aspects of their operations, such as risk management, customer relations, and investment strategies. 

Robust data management practices ensure that leaders can access accurate, timely, and relevant data to choose the best course of action. Businesses should look for the capability to simplify and compare complex data from various sources if they want to transform raw data into actionable business insights. 

  • The obstacles to effective data management 


Despite the benefits of developing advanced data management capabilities, many financial organisations find this difficult to do. Some of the reasons for this include: 

  • System complexity


Financial businesses today hold data across various sources, including payment processing systems, data warehouses and core banking systems. These systems often operate independently and store data in various formats, creating challenges with data integration, consistency, and accessibility. 

  • Outdated technology


Data management is more complicated when legacy systems are involved, which often lack the data auditability, analysis and integration functionalities of modern applications. Relying on outdated technology means additional effort from staff to bridge the gap between different systems, leading to data fragmentation, reduced data quality and elevated risk of errors.  

  • Siloed data


Challenges with data silos are common in finance. Departments frequently work in isolation and use separate systems to manage data. Storing data across multiple silos affects data consistency, adding more complexity to the reconciliation and reporting process.

  • Reliance on people rather than tech


Performing data management processes manually is labour-intensive and time-consuming, taking staff away from genuinely value-added tasks. While sometimes a viable option for small businesses, relying on staff rather than technology to perform these tasks increases the risk of human error, limits data accessibility and impedes scalability. 

Key takeaways

Data management practices are essential to thrive in today’s data-driven financial landscape. By mastering data collection, storage, analysis and reporting, businesses can enhance overall efficiency, ensure compliance with regulatory requirements and mitigate risk proactively. Modernising data management practices will involve overcoming common challenges, embracing advanced technology and consolidating systems. 

Data management at Kani

Kani works with many clients across the card payments and banking industry to automate and streamline data management processes. Some of the work we currently do includes: 

  • Standardising data from a variety of payment processors into a common language, providing easy comparison of volumes between different products, merchants and geographies
  • Compliance and regulatory reporting, generating reports on transaction data, audit trails and other compliance metrics
  • Integrating data from multiple sources (legacy data warehouse feeds, daily file feeds or real-time API data loads) and supporting new industry standards like ISO 20022
  • Real-time performance monitoring on KPIS, such as transaction volumes, transaction success and revenue
  • Providing a fully scalable, cloud-hosted data source using industry-leading technology



What is data management?

Data management is the process of collecting, storing, organising and using data to support business operations and decision-making processes. For financial organisations, data management involves activities like data cleansing, reconciliation, integration, governance, storage, analysis and reporting. 

What is data analytics?

Data analytics extract meaningful insights, detect patterns and predict trends within data. It involves applying statistical analysis, machine learning algorithms and data visualisation techniques to draw information from datasets. Organisations use data analytics to gain a deeper understanding of their operations, customers and market dynamics. 

Can data management capabilities enhance the customer experience?

Yes, robust data management practices provide insights into customer behaviour, preferences and needs. By analysing this data effectively, businesses can personalise services, offer targeted products, streamline processes and provide a seamless omnichannel experience. 

What are some best practices for enhancing data management?

Best practices for high-quality data management include leveraging automation to reduce errors and the need for manual intervention, integrating multiple sources and normalising them into a common language, and implementing financial controls for data quality standards, matching logic, acceptable tolerances and security standards. 

How do you measure the effectiveness of data management practices for continuous improvement?

The effectiveness of data management practices is measured through key performance indicators (KPIs) such as data accuracy, completeness, timeliness and consistency. Conducting regular audits can also help identify areas for improvement and ensure compliance with regulatory requirements. 

What role does data governance play in data management for financial organisations?

Data governance establishes policies, procedures, and controls to ensure the integrity, availability and security of data held across an organisation. It ensures trust in data by defining roles and responsibilities, establishing data standards and facilitating regulatory compliance.

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