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, a 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.
Firms’ efforts in bringing analytics and data together are also hampered by inadequate data lineage and poor data quality. Nearly a third of firms (32%) don’t have full transparency of data lineage across their organisation and the wider operational ecosystem, while just under a quarter (22%) list ‘lack of contextual information such as data lineage that helps us trust the data’ among their main data management challenges. This can lead to a lack of trust and redundant data sourcing and verification.
In the search for data quality, lack of a data catalogue leading to time-consuming data searches or double sourcing’ is the top issue, referenced by 28%, followed by the fact that ‘a proactive focus on data quality is hampered by need for ad hoc incident resolution’, identified by 23%.
According to Hepsworth: “There are tools now available that can help firms identify data quality issues and proactively address them. More specifically, –increasingly there are solutions that enable businesses to explain the value and origin of data, trace data back to its external sources and ensure data lineage.”
“And thanks to the latest advances in cloud, data processing and analytics, it is now possible to combine analytical and data management capabilities and use the results to maximise market data ROI and enable data scientists and other business users.”