Tracking Corona with Transaction Data
The Spanish bank BBVA has used big data technology to track the impact of Covid-19 on Spanish consumption – discovering a 49% average decline in consumer spending.
The study looked at anonymised and aggregated data from 1.4 billion card transactions since 2019, demonstrating dramatic changes in spending habits as the crisis took hold. The data showed a more than 90% year-on-year drop in non-essential consumer goods and services shopping, while spending on food increased by 20% as people panic-bought in the run up to lockdown, and skyrocketed by 95% in the days before the Easter holiday.
As well as highlighting consumer trends, the transaction data can also be broken down by area, demonstrating that Madrid has been hardest hit economically by the coronavirus crisis, with a weekly drop in spending of 70%.
The use of anonymous transaction data has helped BBVA develop a real-time picture of how Spain is reacting from both Covid-19 and the resulting economic interventions. The study is an excellent example of how useful transaction-level big data can be for offering quantifiable analysis of important, time-sensitive decisions – especially during a crisis.
Working with high-volume highly-granular data may well be an important adjunct, or indeed replacement, for other more traditional data sets. Using information of this sort throws up a range of questions around security and data privacy, moral hazard and comparability. In a regulatory context (and for that matter, inside the enterprise) many of these kinds of efforts will be made easier with the imminent release of xBRL-CSV, part of the modernising Open Information Model (OIM). xBRL-CSV will provide the benefits of taxonomy-backed structured data combined with the exceptionally efficient CSV format, in order to effectively handle extremely large XBRL data sets.
Read more and access the study here.
