For too long, financial firms have relied on outdated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spreadsheets and outdated tools.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.
Cost of Data Mismanagement
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
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.
Expect ongoing updates as this story evolves.
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.
Cost of Data Mismanagement
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
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.
Expect ongoing updates as this story evolves.
This post is originally published on FINANCEMAGNATES.
