AI initiatives have also been hindered by poor data management, as faulty data leads to errors.
For too long, financial firms have relied on outdated data systems that undermine efficiency and inflate costs. Despite the effort to harness better technology like artificial intelligence, many institutions struggle with fragmented infrastructures.
These are the findings of recent research by Gresham, which highlighted that such systems result in inefficiencies and increase regulatory risks. Many financial firms reportedly depend on spreadsheets and outdated tools. According to the research, this creates tangled ecosystems of data silos and inconsistent quality, complicating integration and slowing decision-making.
For example, UK firms onboard new data faster than their US counterparts, taking weeks instead of months. This highlights the urgent need for streamlined infrastructures.
However, 44% of firms struggle with managing data stored across multiple locations, leading to redundancies and inflated costs. Escalating data volumes come with surging expenses, yet most firms lack real-time cost-tracking systems.
Only 21% monitor data consumption and costs in real time, leaving the rest vulnerable to unexpected bills. Smaller firms, in particular, reportedly struggle with manual tracking methods that delay reporting and strain budgets.
Opaque pricing models and fragmented budgets compound these issues. Hidden cost surprises related to data management remain a major concern, the report reveals, with 34% of firms identifying them as a significant challenge.
Real-time data management is critical for financial firms to maintain a competitive edge, yet many hesitate to overhaul their systems. While 79% of firms plan to increase their budgets for real-time data, foundational practices often lag behind.
Besides this, the report pointed out that relying solely on AI without data efficiency worsens these challenges. Faulty data results in errors through AI systems, creating misleading insights and higher costs. Without proper data management, AI initiatives can fail to deliver meaningful results, warned the report.
The research has now made recommendations for better data systems. This includes centralizing budgets and implementing scalable and real-time data systems to reduce redundancies and improve decision-making. It also recommended embracing Data-as-a-Service (DaaS) solutions to cut costs while increasing operational efficiency.