Navigate Market Volatility with Proactive Data Management


Leading organizations are taking a proactive approach to data management, which opens new opportunities for maintaining stability during such unpredictable times.

Financial institutions need both stability and agility to compete, but increasing market volatility has put pressure on front and back-office teams to keep pace. Significant increases in trading volume are stressing current IT systems; at the same time, new patterns of trading require ever-faster responsiveness. Though the market is familiar with volatility, the scale of recent events, such as the unforeseen uptick in the trading of GameStop stock, presents a new level of challenge. Proactive data management can play a role.

The volatility and uncertainty fueled by the pandemic pushed organizations to tap deeper into the vast amounts of data available to them to bolster resilience and adaptability. Leading firms are leveraging new architectural data structures to increase real-time visibility, but as the volume of complex data grows, it is becoming more difficult to meet service level agreements, requirements from the business, and expectations from customers.

Market volatility will never go away, but that does not mean companies should throw their hands up and hope for the best. Leading organizations are taking a proactive approach to data management, which opens new opportunities for maintaining stability during such unpredictable times.

Volatile times require resilience

Firms face both operational and strategic challenges during volatile times. Wild swings in the volume of traffic, unexpected spikes in valuations, surges in customer inquiries are operational challenges. Firms that have experienced slow response or even outages from key systems during extreme market uncertainty, often at the times when they need stability the most, are re-evaluating how to meet SLAs in times like this. New regulations and internal controls are also operational challenges. There are also strategic imperatives and opportunities in volatile times, for firms that can ‘see around corners’ better than others, rapidly take advantage of developing situations, and differentiate on service and performance when customers are reeling from events. Organizations have spent a decade incorporating ‘business agility,’ but meeting these challenges needs more; it needs resilience.

For financial services, resilience has two main aspects. On the business side, resilience means having easy access to insights that help you steer through troubled waters, adjust on the fly, and seize opportunities as they come up. On the technical side, resilience means being able to handle whatever load is thrown at you, even in highly volatile situations, including the robustness and security needed to keep operations continuous and safe.

Keeping pace with ongoing market dynamics

The more data sources organizations have, the more complex their data management practices become. As data grows, so does the prevalence of data silos, making access to a single, trusted, current, and usable representation of the data challenging. When data isn’t accessible across systems, heads of businesses do not have an accurate picture of the market and the relevant opportunities available based on ongoing client and market developments.

On top of this, increasing regulatory demands and security threats are putting pressure on firms.    Each new regulation and report requires access to data, as well as a clear data lineage back to the source and point in time. The rate of new security threats is also growing rapidly. Data management today means curating clean data across many sources, supplying a range of data demands from multiple parts of the business, and keeping up with the sheer volume of transactions, all while continually maintaining stringent compliance and security practices.

That’s why capital markets firms are taking new approaches to accessing and utilizing complex data in real time. Data fabrics present one such approach where firms can process, transform, secure, and orchestrate data from disparate sources in real time. These new types of architectural paradigms are vital to driving a more proactive, integrated, and cohesive data strategy for the digital economy.

Leveraging data for new actionable insights

When events suddenly erupt, financial organizations need to look at ‘what-if’ scenarios quickly and plan accordingly. The ability to do this relies on healthy data and the use of analytics to extract insights out of that data rapidly.  This has been a dream for many organizations for decades; important advances make this attainable today.

Machine Learning (ML) can play a strong role in building resilience into the financial services sector.  However, ML requires a large volume of current, clean, and accurate data from different business silos to function. Seamless access across a company’s multiple data silos is extremely difficult without a real-time, consistent, and secure data layer to deliver the required information to the relevant stakeholders and applications at the right time.

While data lakes have been implemented in attempts to solve many of these data management challenges, many data lakes have often been nothing more than data swamps – murky with disorganized data that presents challenges around accessibility and the ability to leverage the data without a huge data quality project. Data Lakes become another silo in the mix rather than a solution to the flood.

With rapid business change and enterprise data collection expected to increase at a 42% CAGR over the next two years, organizations need to streamline and accelerate operations by eliminating manual processes where possible to automate. To keep pace with volatile market dynamics, operational resilience becomes more than just a defense mechanism. It becomes a competitive advantage.

Achieving operational resilience

To become resilient, forward-thinking executives are looking to leverage the vast amount of data being collected for actionable insight. Managing data proactively is a key to responding well to unexpected volume and valuation volatility, making better business decisions faster and improving automation, compliance, and security. The ability to access and process a single representation of accurate, consistent, real-time, and trusted data is a priority for these executives. From scenario planning to modeling enterprise risk and liquidity, regulatory compliance, and wealth management, access to accurate and current data lets their organizations make smarter business decisions faster.

By streamlining and accelerating operations through automation, organizations can increase speed and agility and reduce the delays and errors associated with manual processes. This ensures they have sufficient headroom, processing capabilities, and systems in place to respond to unexpected volatility. With these systems in place, organizations can then take this a step further with proactive data management.

Going on the offense with proactive data management

It is natural to play defense in volatile times. This may help somewhat with stability, but it does not result in improved resilience. This requires playing offense.

Proactive data management arms organizations with a single view of accurate, consistent, and trusted real-time data that can be used to meet operational and strategic challenges. Some traditional patterns must be re-examined in the process. For example, maintaining separate analytics and transactional processing is a standard practice, but it results in more complex, more fragile data management. It is now possible to have one system that does the transactions and then mines them for insight. There can now be an intermediary layer of data modeling that makes it easier for business users to get the insights they need when they need them. Proactive data management includes finding places to prove out new patterns that may be more robust and flexible. The benefit is leveraging data to generate value, revenue, and profit.

Organizations across financial industry segments, including capital markets, are modernizing their data architectures. Legacy applications are often the bottleneck – sunsetting legacy applications is expensive and risky, yet running those very applications is also expensive, and they are often the most fragile systems, the hardest to integrate, and the biggest obstacle to resilience. 

Firms need not be held back by this dilemma. Forward-thinking organizations leverage architectural paradigms such as smart data fabrics to continue to run their legacy systems and stitch together distributed data from across the enterprise to power a wide variety of mission-critical initiatives. This approach enables organizations to shift from being reactive to predictive and proactive with their data management strategy.

It isn’t necessary to change everything at once; in fact, it isn’t wise. Start small. Measure and quantify the benefits of adopting modern approaches at a pace that makes sense for your organization and learn as you go. Being proactive means moving forward in the journey; learn, adjust, and get value at every step along the way.

Conclusion: Focus on data management

Investment in modern data management technologies provides organizations with a superior way to achieve a single representation of accurate, consistent, real-time, and trusted data on demand. This investment arms financial institutions with a holistic and comprehensive view of historical, current, and future activities so they can be one step ahead of market changes versus several steps behind. By becoming proactive with their data management, they achieve operational resilience.

It is likely that further volatility is ahead of us before we fully recover from the effects of the pandemic, but with the use of modern data management and analytics capabilities, financial organizations can reap the benefits of greater resilience and make it through to the other side with greater stability.

Jeff Fried

About Jeff Fried

Jeff Fried is the director of product management at InterSystems. In his career, Fried served as CTO of BA Insight, Empirix, and Teloquent, and ran product management for FAST Search and Transfer and for Microsoft. He has extensive experience in data management, text analytics, enterprise search, and interoperability.  You can reach Jeff on Twitter @jefffried, or on LinkedIn.

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