Nearly two-thirds (63%) of data scientists in financial services firms say their organisation is not currently able to combine data and analytics in a single environment. This was among the key findings of new research in the UK, US, and Asia, for Alveo, leading solutions provider of managed data services for data mastering and analytics.
The survey also found that nearly four out of ten respondents (38%) saw ‘the need to integrate structured and unstructured data’ as one of the main challenges their organisation faces in ‘bringing analytics to data and using the combination to drive effective decision-making’.
For financial services firms, closing the gap between data mastering and analytics capabilities is key in deriving insights from an increasingly broad range of data sources. In a financial context, structured data adheres to a pre-defined data model and includes everything from financial instrument terms and conditions to pricing feeds, while unstructured or semi-structured data does not conform to a pre-set data model and might incorporate earnings call transcripts and social media activity. It can also help gauge scores against ESG indicators.
Another key challenge highlighted by the research was the issue of ever-expanding data volumes. 39% of data scientists surveyed claim ‘it is difficult for us to manage large data files and scale our infrastructure to the volumes we face’ as the main challenge in bringing analytics to the data.
Mark Hepsworth, CEO, Asset Control, said: “Financial services firms struggle with growing data volumes that are often siloed in data stores and legacy systems, making access difficult. This causes a bottleneck when firms look to get a broader range of data to data scientists and decision makers, creating a range of challenges as a result including lack of integration of meaningful data and analytics.”
In line with this, the research shows that many financial services firms across the UK, US and Asia still struggle with significant issues in integrating different types of data and being able to scale infrastructure to cope with ever increasing data volumes.
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