Best Practices for Data Architecture Modernization in Financial Services

The financial organization has been entirely digital, from digital marketing to digital allotment and every activity in between. But why do many financial services depend on outdated technology to perform core functions?

Due to customer demand, there is a need for digital transformation and digital technology within financial industries. But many companies need help to keep up with modern data systems due to increasing cybersecurity threats and reforming regulatory landscape.

However, financial services are pressured by government bodies and competitors to modernize their systems. Banks are agile and reaping the benefit of a flexible data architecture that helps them thrive in the modern financial landscape.

Surprisingly, modernizing legacy software is cost-effective and offers financial service industries agility and flexibility, and better meets the needs of their customers and stakeholders.

What is Data Architecture?

Data architecture refers to designing, organizing, and managing an organization’s data assets, including how they are stored, processed, and used. It involves creating a blueprint that outlines how data flows through an organization and how it is stored, accessed, and used by different stakeholders.

There are several components of data architecture such as data models, dictionaries, management processes, and governance policies. It is designed to ensure that an organization’s data assets are accurate, consistent, and accessible to those needing them while confirming that data is stored securely and complies with applicable regulations.

Data architecture is an ongoing process, as organizations must continually adapt to new data sources, technologies, and business requirements. As per data modernization predictions, it helps to reduce the risk of data breaches and regulatory violations by ensuring that sensitive data is properly managed and secured.

Problems that Financial Organizations Face

There are several challenges that financial services face, but they can be solved through data architecture and modernization. This includes:

1. Evolving regulations: The financial services landscape is constantly changing due to factors like the rise of digital banking, advancements in data collection, and geopolitical security challenges. This has led to the expansion of regulatory boundaries and heightened regulatory expectations.
   The regulatory agenda is expected to focus on a range of issues, including consumer protection in the realm of digital assets and the impact of climate change on the financial sector. These transformational topics will shape regulatory priorities both presently and in the future.
2. Market competition: To thrive, financial services need to bring new products to market and adapt to changing consumer demands. This demands business innovation and agility, which outdated systems cannot offer.
3. Lack of interoperability: Most companies need help to cope with new technology. This means vital data like investment products, loans, and ancillary services- will be trapped within systems that cannot communicate with one another and impede operations.
   Companies can support the seamless integration and interoperability between business-critical systems through effective data architecture practices.
4. Cybersecurity: The vast amount of data financial organizations has made them vulnerable to cyberattacks. The attacks demand advanced analytical software to identify and reduce cyber threats.
   Companies that practice modernization can take advantage of improved security and scalability.
5. Shrinking margin: The power to manage operational costs at scale is crucial for financial services. The legacy system makes it challenging for financial assistance to improve margins and price their products more competitively.
   Modernization provides the perfect opportunity to mitigate legacy pressure from the data center to the cloud, reaping the benefits of new economies of scale and significant cost savings not otherwise achievable.

The Significance of Data Architecture Modernization

In addition to helping businesses to manage some of the most common challenges, data architecture modernization offers several advantages. This includes:

• It can improve the data’s accuracy, consistency, and completeness, making it more reliable and trustworthy. This can positively impact decision-making, as it enables business leaders to make informed decisions based on accurate and timely data.
• Personalized and optimized customer experience drives growth, improves customer satisfaction, and secures long-term loyalty.
• Data architecture modernization also improves the agility and scalability of an organization’s data infrastructure, allowing it to respond quickly to changing business needs and requirements.
• It can help reduce data silos and enable data sharing across the organization, improving collaboration and fostering innovation.
• Mitigates operating, infrastructure, and maintenance costs by shifting systems off on-prem servers and into the cloud.
• Help organizations comply with regulatory requirements, such as GDPR or CCPA, and ensure data privacy and security. Organizations can better manage data access, usage, and protection by implementing robust data governance practices and policies.
• Provides an opportunity to develop a culture of innovation and business transformation.

Best Practices to Achieve Data Architecture Modernization

Modernizing your financial data can be simple. Data architecture modernization follows a chronological process that starts with knowing data accessibility gaps and ends with operationalizing the data layer with microservices. A few data architecture best practices include:

1. Aligning business objectives with modernization efforts: Business objectives offer a strategic roadmap to companies who struggle to start their modernization journey. For instance, a company focusing on reducing operational costs may aim for modernization efforts that can be easily moved to the cloud. Furthermore, reduce data center costs from the balance sheet.
2. Manage executive sponsorship and stakeholder buy-in: For most companies, the success or failure of any significant organizational change- modernization or other- rely on administrative support. Securing buy-in from key stakeholders across the organization- majorly end-users is crucial for the long-term success of any data modernization effort.
3. Plan your future: With data architecture modernization, the aim should be to transform business operations and thrive value in a manner that offers long-term success. Financial service managers should choose the most suitable strategy depending on their end goal.
4. Strategically prioritize applications to modernize: Modernizing too quickly could be counter-productive and cost-prohibitive, a significant risk to business continuity. An appropriate strategy is to take a phased approach, prioritizing applications for modernization, implementing a steady-state program, and much more.
5. Design a data migration checklist: To avoid data loss when shifting applications from on-prem units to cloud platforms. Organizations must create lists detailing how data needs to be shifted, how it should be presented once it arrives in the cloud, and what testing needs to be conducted to ensure data accuracy.
6. Maintaining open lines of communication with clients: Data modernization brings significant change to the operational environment, like a direct impact on clients or end-users. Hence, financial service organizations must communicate with their workforce at every mechanism phase. This includes defining clear expectations and providing the workforce with the support and training to successfully reduce operations to modernize systems.
7. Appreciate data platforms for low-latency access: Identifying the key features of an elite real-time data platform with low-latency access and knowing how to leverage it in the company’s data architecture practices.

Bottom line

To quickly adapt to marketing changes and demands, the fintech services industry must move to cloud platforms and modern systems, microservices-derived applications they can offer directly and continually.

Hence, data architecture modernization is critical for organizations that want to stay competitive and innovative in today’s data-driven business landscape. It can enable organizations to leverage the full potential of their data assets and drive business growth and success.

WRITTEN BY

Anjali Goyal

Anjali Goyal is a content writer at TechEela. She helps businesses increase their online presence with optimized and engaging content. Her service includes blog writing, technical writing, and digital marketing.

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