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The Future of Data Management in Financial Services

Data management has always been a critical element of successful financial services (FS) operations. But the data management landscape has grown more complex as types of data, new regulatory requirements, market needs, and technologies emerge.

Now, only 16% of financial services leaders can easily leverage financial data for decision-making, Gartner reports. FS teams have to completely rethink their data management strategies and methods in order to keep up with growing data volumes while ensuring regulatory compliance.

In this article, we explore the common challenges faced by data management leaders in financial services and discuss the opportunities available to them to stay competitive through new data management approaches, methods, and technologies.

The Current Data Management Landscape

Financial services firms have faced significant change over the past decade in terms of data management opportunities and requirements. This critical area of business is growing more complex as a result of multiple factors, including:

  • Regulatory changes. The compliance landscape for financial services is constantly evolving, with new regulations and requirements being introduced frequently. For example, the General Data Protection Regulation (GDPR) in Europe has forced FS firms to rethink how they handle and protect personal data.
  • Data volumes. The amount of data generated by financial services operations is increasing at a rapid pace. FS firms must find ways to effectively manage these large volumes of data while also ensuring their accuracy and integrity.
  • Data types. Financial services must work with a wider range of data types, from structured financial data to unstructured customer information. Entirely new “realms” of data, such as environmental, social, and governance (ESG) data, are being defined as FS firms look to integrate and automate their use of this data in regular business processes. This presents a significant challenge for data management leaders to effectively collect, store, and analyze this diverse range of data.
  • Shifting market dynamics. The financial services market is constantly changing, with new products, services, and customer needs emerging. Data management leaders must be able to quickly adapt to these shifting dynamics in order to stay competitive.
  • Advancements in technology. Emerging technologies such as artificial intelligence (AI), machine learning (ML), and blockchain present both opportunities and challenges for data management leaders in financial services.

Without sufficient data management capabilities in place, including the support of a dedicated data management partner, FS firms may struggle to adapt and keep up with these ongoing changes. “They may spend money buying duplicate datasets, waste time and money trying to aggregate their data, and struggle to onboard some of the new data that is becoming available,” Bloomberg describes. “Getting data quickly and stitching it together with other information will be critical to success.”

A Look at Modern Data Management Systems

Fortunately, the modern data management landscape provides newfound opportunities for FS firms to enhance their data management processes. With advancements in technology and new approaches, such as automation and cloud-based solutions, FS leaders can improve efficiency, reduce costs, and gain valuable insights from their data.

A modern data management tool “doesn’t make the decision, but rather, it gives the decision makers the tools they need to do the job easily and effectively,” Bloomberg describes.

Next, we explore the key ways that modern data management systems are helping financial services firms stay competitive:

  • Process automation
  • Advanced analytics
  • Scalability and security in the cloud
  • Emerging and future technologies

The Role of Automation in Modern Data Management

As CIO describes, “Transitioning to automated, data-driven processes is the best way for these companies to not only cope with change but also take advantage of it.” Indeed, robotic process automation (RPA) is emerging as a key component in modern data management strategies for financial services.

Automation allows FS firms to streamline their data management processes and reduce the potential for human error. This results in increased efficiency, cost-effectiveness, and accuracy of data-driven decision making. Through automated data quality checks and integration capabilities, FS firms can ensure that their data is accurate and reliable, reducing the risk of compliance issues and errors.

Specific ways RPA can enhance data management in financial services include:

  • Data extraction. RPA can automatically extract data from various sources and formats, including structured and unstructured data. This allows for accurate and efficient acquisition of critical financial data.
  • Data validation. Automation can validate the accuracy of data by comparing it to predefined rules and checks, reducing the potential for human error in manual validation processes.
  • Data integration. By automating the integration of data from multiple sources, RPA can provide a consolidated view of key data. This allows FS firms to make more informed business decisions based on comprehensive and accurate information.

As data management professionals enhance their use of RPA and other forms of automation, they will become more capable of handling even greater data volumes and complexities.

Advanced Analytics for Strategic Decision Making

Advanced analytics can transform raw data into actionable insights, enabling data leaders to drive strategic decision-making. “These models typically analyze vast amounts of historical data, as well as real-time market data, to identify patterns and predict future movements in the stock market,” as Forbes describes.

For example, financial firms can use real-time analysis of client or traffic data, integrate it with historical data, and identify trends or anomalies to reinforce future predictions. The more automated and intelligent this process becomes, the more likely financial firms will be successful when acting on those insights.

Advanced analytics tools and techniques include:

  • Machine learning. “Analytics powered by machine learning (ML) lets business leaders assess risk according to a wide variety of variables, many of which are not intuitively obvious,” according to CIO. Using ML algorithms, financial services firms can identify patterns and relationships in data that may not be apparent to the human eye.
  • Predictive modeling. By using predictive analytics models, FS firms can better anticipate future trends and behaviors based on historical data. This allows them to make more informed decisions related to product development, marketing strategies, pricing models, and risk management.
  • Data visualization. Advanced data visualization tools provide a visual representation of complex data sets, making it easier for financial services professionals to interpret and communicate findings to stakeholders. With interactive dashboards and reports, data leaders can quickly identify key trends and patterns in their data.
  • Artificial intelligence (AI). AI-powered analytics solutions can analyze vast amounts of data, recognize patterns and trends, and make proactive recommendations based on that analysis. By utilizing AI in data management, financial services firms can support critical decisions based on customer behavior, market trends, and internal operations.

Advanced analytics will continue to evolve, allowing financial services firms to utilize data in new and innovative ways to stay ahead of the competition.

Leveraging the Cloud for Scalability and Security

Cloud computing has transformed how businesses manage their data. For financial services firms, the cloud offers scalability, cost savings, and enhanced security features in data management. Financial services firms can:

  • Scale up or down. The cloud allows FS firms to easily scale their data management capabilities up or down based on their current needs. This ensures they are only paying for the necessary resources and can quickly adapt to changing market demands.
  • Reduce costs. By moving data to the cloud, financial services firms can eliminate the need for expensive on-premise infrastructure and hardware. This reduces overall costs while enabling FS firms to access advanced technology and features that would be costly to implement in-house.
  • Enhance security. With a dedicated team of security experts and advanced security features such as encryption, multi-factor authentication, and regular backups, the cloud offers a more secure environment for financial services data. This is especially important given the sensitive nature of financial data and the increasing threat of cyber attacks.

Overall, leveraging the cloud for data management allows financial services firms to focus on their core business while relying on a reliable and scalable solution for their data needs.

The Future of Data Management in Financial Services

Now, emerging methodologies are poised to further revolutionize data management in financial services. Technologies and techniques include:

  • Blockchain. This technology offers a secure, tamper-proof way to store and track data, making it an ideal solution for financial services firms. “Banks are exploring the use of blockchain for various use cases such as digital identity, trade finance, and cross-border payments,” says Forbes.
  • Quantum computing. As processing power increases, quantum computing has the potential to support both the rapid analysis of vast amounts of data and the timely extraction of valuable insights. Financial services firms may soon use this technology for advanced risk modeling and fraud detection.
  • Open banking. “Open banking, a concept that has taken off rapidly overseas, enables financial institutions to expose data selectively to other institutions via APIs to enhance customer experience,” as CIO describes. This allows for more efficient data sharing and collaboration between financial services firms.

While these technologies are still emerging, they have immense potential to transform how data is managed and utilized in the financial services industry.

Securing a Trusted Data Management Partner

Staying agile and competitive in financial services begins with sophisticated data management. But for FS firms to stay ahead in this fast-paced industry, they need a trusted data management partner who can help them navigate these changes and leverage new opportunities.

“That turnkey operator has the experience and technical skills to solve the problem and can make the necessary investments in hardware and software,” Bloomberg recommends. “That same operator can provide vital services such as acquiring, linking, and distributing data, as well as monitoring the data supply chain and alerting customers to potential problems.”

Data Management with Option One Technologies

Option One Technologies specializes in managed IT services for financial firms. As data management experts, we have the experience and capabilities financial firms need to stay ahead of the curve in this ever-evolving landscape. Contact us directly to learn more.

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