If you want accurate, reliable data to fuel digital innovation in finance, you need effective Data Management – but building a business case for it isn’t always easy.
You may need to overcome scepticism or misunderstanding from stakeholders and decision-makers, but these are the business cases for four data management initiatives in Finance, and how to communicate the return on investment for data transformation.
How to build a business case for Master Data Management in Finance
The business case in one sentence: Without accurate, centrally accessible data, you cannot know your customer.
If you want to base decision-making on accurate data, improve your customer’s experiences, and successfully identify financial risks, you need to know that the data available to you is the only version of that data.
In the financial sector, cross-departmental customer data is a particular problem, making it difficult for banks to see a complete picture of the person behind the customer profile. An MDM platform can centralise your data to make it accessible across the business, boosting accountability for Data Quality and creating a single version of the truth across your master data.
Master Data Management in action: How a financial giant achieved a centralised and trustworthy view of its data
The goal: To get a reliable single view of data to comply with complex regulations, manage financial risks and reduce operating costs.
The challenge: Manual reporting from data across multiple sources meant time-consuming reporting processes and human error.
The solution: An MDM platform with Data Quality and Data Governance: streamlining compliance, increasing trust in data, improving accountability and getting data buy-in across the business.
How to build a business case for Data Streaming in Finance
The business case in one sentence: Real-time data means you can head off crises and challenges before they escalate.
In the financial sector, the faster you can react to data, the faster you can identify risks, tailor customer pathways, tighten up efficiency and deal with challenges or crises.
With Data Streaming, you can process high volumes of data in real-time to create outputs almost instantly – giving you the ability to monitor, analyse, visualise and, ultimately, react to incoming information in an instant.
To make data streaming a success, you have to build a solid Data Management foundation to ensure that your data streams are supplying high-quality, up-to-date data – and are based on a single version of the truth.
Data Streaming in action: How one bank improved the customer journey with real-time data
The goal: To offer intuitive and tailored customer experiences, with real-time data.
The challenge: The bank had a product-focused approach to customer data. They needed to switch to a real-time, customer-centric view.
The solution: A Single Customer View framework, using Data Streaming to create a single point of change, synchronise updates, and align all systems.
How to build a business case for Automation in Finance
The business case in one sentence: Automation reduces human error and speeds up processes.
Automation can help banks and other financial organisations to tighten up their processes, streamline operations and reduce costs – but it’s not without its risks. Automations, without a cohesive data strategy, can result in customer alienation and frustration, not to mention costly compliance or risk analysis errors.
For automation to be successful, it again comes down to how well your data is managed: can you trust that the data feeding your automations is reliable, consistent and up to date? Is that Data Streaming from one, high-quality source – or is information scattered across the business?
Automation is part of the financial sector’s present and future: you need to make sure that your data is driving it effectively.
Automation in action: How one bank streamlined their AML procedures
The goal: To automate aspects of their Customer Due Diligence to enhance their Anti-Money Laundering process.
The challenge: A lack of high-quality, real-time data available for insights and automations.
The solution: Streaming real-time business event data to decision engines through high quality data pipelines, using Data Streaming technologies.
How to build a business case for data Cloud Migration in Finance
The business case in one sentence: Customer expectation grows and changes rapidly — your infrastructure must be able to as well.
The rise of cloud-native banks and financial services providers is accelerating customer expectations, with users expecting fast, flexible and fully tailored financial solutions.
For banks, a flexible, scalable future that meets these expectations relies on cloud data capabilities: but in a highly regulated, high-risk sector, you need to be confident that your cloud infrastructure is secure as well as agile. Ensuring that any Data Migration to the cloud is underpinned by robust Data Management is essential to making your cloud migration a success.
Cloud migration in action: How a building society retained and attracted more customers
The goal: To collate and analyse KPIs around member satisfaction, improving member experience and attracting a new generation of members.
The challenge: Varied data sources needed to be ingested and processed, ranging from corporate finance systems to member communications. This included sentiment analysis of natural language data from online Chatbots.
The solution: A cloud-based data platform was built using Azure, Snowflake and Purview to process data from various sources, run analytics, measure KPIs and ultimately improve their members’ experience through data insights.
Common Data Management Challenges in Finance and How to Address Them
Financial institutions face several hurdles in managing their data effectively. Scattered data across various systems creates siloed information, making it difficult to get a comprehensive view. Implementing a Master Data Management (MDM) platform and data integration tools can help consolidate information into a single source of truth. Additionally, ensuring data quality is crucial to avoid poor decision-making. Establishing data cleansing routines and promoting data ownership within the organisation can address this challenge. Furthermore, robust security measures are essential for protecting sensitive financial data and complying with regulations.
Encryption, access controls, and regular security assessments are necessary steps. Finding skilled professionals can also be a roadblock. Investing in data management training and partnering with specialists can bridge this gap. Finally, overcoming resistance to change requires clear communication of the benefits of data management and establishing a data governance framework to define roles and responsibilities. By proactively addressing these challenges, financial institutions can unlock the full potential of their data and gain a significant competitive edge.
Are you missing Data Management essentials from your strategy?
Data Management is the foundation of every successful data-driven project, but it’s a step that’s often missed in the race to innovate financial services. Agile Solutions can help you to build a business case for Data Management, and deliver measurable value throughout the process.
Speak to Agile to find out how we can build a Data Management strategy tailored to your organisation: from technology selection and implementation, to Data Governance, Data Quality and every other aspect of the process.