Why financial services can improve on intraday data for decision making

Tim FitzGerald, EMEA financial services manager, InterSystems

 

With financial volatility threatening to escalate at a moment’s notice, fuelled by inflation, war, political uncertainty, and rising interest rates, financial services firms need to keep on top of their game to maintain a competitive advantage. Staying agile in this complex environment requires getting access to data faster than ever, so that financial leaders can make fast and accurate decisions before the rest of the market catches up.

But data latency is a real issue, and making decisions from stale data can mean that the company’s strategy is continually being left one step behind. This is dangerous in normal conditions, and acutely so in periods of extreme uncertainty, when fluctuating risk exposure can undermine the high-value services provided to customers.

In fact, research undertaken by InterSystems shows that over a third (35%) of European financial services organisations aren’t basing critical business decisions on real-time data, with just 8% of firms using data that is less than an hour old to make decisions. Given the inflexibility imposed by this reliance on intraday numbers, better solutions to managing, distributing, and deriving data, that provide real-time outputs, are necessary.

The data challenges for financial services firms

The survey, involving almost 200 senior line of business leaders within European financial services firms, found the biggest data challenges being faced include delayed access to data (39%) and not being able to get the data in the correct format (33%) or from all the needed sources (31%).

Tim FitzGerald

As a result of this, the overwhelming majority (92%) of European financial services firms are forced to rely on data that is over an hour old, with 85% relying on data that is 24 hours or more old. Consequently, 35% of senior leaders said that they were unable to base decisions on real-time information and therefore were being forced to make assumptions that could well be out of date by the time they were made.

There are many reasons for data being delivered slowly across an enterprise, but the most common root cause is found in disparate legacy systems and applications that weren’t originally designed with the rest of the organisation in mind. As legacy systems age, and new ones are introduced, pressure builds up that eventually overflows to the IT department, where data-provisioning requests are stuck in huge backlogs. InterSystems research shows the forty-three percent of respondents also claimed they have anywhere between 25 and 100 data and application silos, adding even more complexity to the overall flow of data and slowing down access to the information required.

But the use of intraday numbers, which can be up to eight hours old, no longer has a place in financial services. Instead, firms must now feed their frontline teams with real-time data that tracks events moment by moment to ensure they are able to respond to market changes and customer demands as they happen.

But delivering actionable data in real-time only solves part of the problem. Firms within the financial services sector must also go further and equip their professionals with the data and analytics capabilities to predict what could happen next, through performing analytics on fast-moving transactional data, and provisioning access to those who need it.

The smart fabric architecture

One solution that that address this problem uses an innovative architectural approach, the smart data fabric, which accesses and harmonises data from existing systems and silos inside and outside the organisation. It generates results on demand, which ensures that the information is both current and accurate. The smart data fabric has the ability to perform analytics on real-time event and transactional data without impacting the performance of the transactional system, so demand for data doesn’t impact the system being queried. This means firms can move away from using information stored offline or elsewhere and equip themselves with real-time insights to drive their businesses forwards.

A smart data fabric architecture removes business latency and embeds agility by decoupling the reliance on old data derived via legacy methods. It achieves this by accessing, transforming, and harmonising data from multiple sources, on demand, to make it usable and actionable for a wide variety of initiatives. It allows existing legacy applications and data to remain in place, ensuring one source of truth, and reducing architectural complexity. The ability to bridge silos from multiple sources, and from disparate locations, allows employees to access, query, and manipulate this data to deliver informed decision-making across the enterprise.

It also eliminates delays in accessing data and allows organisations to incorporate analytics on real time event and transactional data without impacting system performance. This is due to its distributed nature and helps to eliminate errors and missed business opportunities. Allied to the enhanced flow of information, AI and ML can be utilised across the fabric to augment the decision-making process, delivering predictive and prescriptive suggestions while enabling programmatic decision-making when the use case warrants it.

The smart data fabric architecture addresses the challenges facing financial services companies, enabling real-time data to flow throughout the organisation without disrupting the operations of legacy systems. This capability meets the demands of financial services firms that require on-demand data to secure their success. By adopting this new approach to faster data, business leaders within financial services can transform their view of the enterprise, allowing them to make better decisions, from the freshest and most accurate data possible.

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