2024 needs to be the year of high-quality data.

By Tim FitzGerald, Financial Services Manager, InterSystems

Banks and financial institutions need to make 2024 the year of higher-quality data.

“Our systems and data are not performing as they should” is often a challenge met by frontline teams in investment, asset management risk-management, and, in fact, right across almost all functions in a bank or financial services firm when it comes to their company’s IT.

Senior executive teams should listen to them and examine more innovative approaches to data management, such as the Smart Data Fabric. Teams are frustrated because they can’t seize opportunities in the moment, making their own decisions based on evidence they can trust to deliver the right results.

Gaining better insight from many different types of data from both within and beyond the enterprise has become the new battleground in financial services. Excelling in a more unpredictable and much faster-paced world necessitates a culture of decision-making built on radically shortened time-to-insight. The rise of generative AI tools to augment human performance in financial services has also exposed the inadequacies of long-established and often ad-hoc IT set-ups.

Almost everyone in financial services needs faster time-to-insight.

But it is not just front-office revenue-generators who need insights from transactional and event data to heighten their performance and drive alpha.

Almost everyone does – from settlements, reporting and compliance to client relationship functions. They all need fast insights without relying on the expertise of scarce data scientists. The same is true for CFOs and boards, who want a more accurate, up-to-the-minute 360-degree view of performance, threats and opportunities, assets, and liabilities.

The importance of higher quality data

But it is not just about speed, it’s about data quality as well. To compete against more innovative competitors, financial organisations need clean, normalised and curated, timely data they can trust for faster, more informed decision-making and collaboration. Their portfolio management systems and other applications all need this higher quality of data – reliable, verified, and available in near-real-time. Predictive and prescriptive analytics offer huge gains in responsiveness and efficiency, but they, too, demand better management of data coming from inside and outside the organisation.

The problem is that traditional, heavily manual methods or ad-hoc technology stacks are no longer able to cope with the volume and complexity of data, nor the need for more sophisticated analytics in response to events and business questions.

A ‘smarter’ approach that works – the data fabric

As 2024 moves forward, organisations should adopt new approaches to bring all their data together quickly and simply so everyone can rely on it as a single source of the truth. To do so, they will turn to the data fabric – a more innovative IT architecture. The advantage of this approach is that it harmonises highly disparate data and makes it available without disruption or replacement of existing systems.

There are, however, many ways to implement a data fabric. One is to implement and integrate several different data management point solutions. But this is slow to deploy, difficult to maintain, lacks performance, and is inefficient in its use of infrastructure resources.

The smart data fabric is quicker to implement and simplifies data management.

The smart data fabric approach is preferable and has many advantages, providing the required functionality in one single product or platform. It can eliminate the errors and redundancies introduced by maintaining multiple individual data repositories that serve different consumers of the data.

Meanwhile, embedded analytics enable real-time advanced analytic processing without moving the data to a different environment. It is perfectly feasible to replace eight different technologies with a single product, gaining nine times better performance running on only 30% of the infrastructure, and with a far simpler architecture.

The smart data fabric in asset-management

An independent asset manager with billions of dollars of equities and fixed income assets under management has found this approach delivers significant results after trying point-to-point integrations, platforms, data marts, data lakes, and more.

The smart data fabric has met the company’s demand for timeliness and consistency, serving the entire business and its clients. Portfolio performance has improved, as has client engagement and retention. Business users in the firm make more timely decisions, trusting the data they use. They can examine the data in as much detail as they require, answering the business questions that matter to them. The whole project has radically improved enterprise and client reporting.

The key point for senior decision-makers in the financial sector is that the smart data fabric delivers results – for lines-of-business users and back-office teams. The applications a financial institution or organisation uses are fed harmonised and trusted data, regardless of its original source of format.

Financial services teams deserve better data.

In the current time of volatility and new opportunities, bottom-line performance depends on fast access to much higher-quality data than many organisations currently enjoy. As good as talented individuals may be, their performance will fail to deliver unless the financial organisations that employ them sort out their data management so they have timely access to accurate data and insights.

By transforming their approach through the smart data fabric, financial institutions are far more likely to emerge as decision-based organisations using data to be more agile, resilient, innovative, and profitable.