data analytics
Europe Insights

Exploring Fintech’s Data Landscape: Study Probes Challenge of External Data

In the fast-paced world of fintech, data analytics reign supreme. Fintech companies are increasingly turning to sophisticated data analytics and data science to drive growth, enhance personalisation, manage risk, fortify security, and address sustainability concerns.

Now, a new study has uncovered the prevalence, impact, opportunities, and potential risks of using external data when making critical business decisions.

The survey of data scientists, including those in the fintech sector, reveals that the vast majority have significantly improved their models by using external data – yet poor quality data is also their number one challenge.

The Importance and Impact of Using External Data to Make Critical Decisions report by Doorda, a British analytics-ready data provider, delves into the impact of using external data to innovate and capitalise on new commercial opportunities.

“So many fintech businesses are now incorporating technologies that encompass complex data analytics,” says Clifford McDowell, founder and CEO of Doorda. “Whether it’s revenue growth, personalisation, assessing and managing risk, security or sustainability considerations, this research shows that businesses are looking to data analytics and data science to thrive.

Fintech companies hoping to compete successfully must look for improvements in the quality of the data they discover and integrate into their analysis in order to drive innovation.”

Key findings

Although 92 per cent of the 300 surveyed data scientists affirmed that external data had substantially enhanced their models (with 29 per cent experiencing significant improvements and 63 per cent noting moderate enhancements), 40 per cent of respondents admitted to encountering issues with unreliable or unavailable data.

The primary hurdles faced when integrating external data included subpar data quality (cited by 34 per cent as one of their top-three challenges), incomplete datasets (33 per cent), and data incompatibility (31 per cent).

The survey also unveiled a diversity of job titles 140 in total – among respondents primarily engaged in data science and data analytics. This raises questions about whether companies truly grasp the widespread and critical role data science plays within their organisations, says Doorda.

Evidently, external data holds significant importance for the surveyed data scientists. A majority of them (70 per cent) indicated that their models incorporated a minimum of 20 per cent external data, while 78 per cent expressed that the impact of integrating new external data had, at times, surpassed their expectations. Almost unanimously, 98 per cent of respondents emphasised the importance of consistently exploring external data sources.


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