Although data is often dubbed as the new oil, it is still siloed, held within legacy applications and difficult to access. In fact, around two thirds of quants and data analysts in financial services firms spend as much as half their time ensuring the data is fit for purpose, according to a new study by managed data services provider, Alveo.
The study which polled banks, investment firms, insurance companies and hedge funds in the UK, US and Asia, showed that 66% of respondents found quants and data analysts in their organisation spend between 25% and 50% of their time collecting, preparing and quality-controlling data.
Unsurprisingly, they note that their time could otherwise have been spent on modelling and analysis.
The report said that analysts often have to contact the IT department to write a query or set up a report.
Even when quants access data, they may find the metadata that should provide insights into the quality and origins of data, and what the license permissions and approvals are, has not been tracked effectively.
Although artificial intelligence (AI), machine learning and other advanced technologies are often touted as key components, adoption has been slow with only 37% of data scientists using them in their key analysis and investment processes and workflows.
The report said risk management and market making are the two key problem areas. Despite this, risk management is the area where analytics are most commonly and extensively used.
Better data integration and automation is highlighted in the report as solutions to help firms improve productivity.
However, just 37% of financial services organisations polled in the survey have the capability to incorporate innovative data science solutions such as AI or machine learning into market analysis, investment processes and operational workflows.
“If financial services firms are to harness the power of analytics they must develop an integrated approach to managing and provisioning data,” says Mark Hepsworth, CEO, Alveo.
He added, “This will require AI, machine learning and related technologies to prepare the right data. Highly skilled quants and data analysts should not be held back by having to spend hours improving poor quality data when the technologies are there to complete the task for them.”