Magazine Issues » May 2019

Sponsored profile: Big Data for data accuracy

Maxime_AertsData analytics will revolutionise investment funds and their distribution when the industry learns the value of coherent, qualitative datasets. By Maxime Aerts, chief operating officer at Fundsquare.

Qualitative, coherent, compliant and readily accessible data are prerequisites of investment fund digital distribution. Looking forward, fund data shall reach an ever increasing number of digital sales points, multi-segment and multi-market.

The experience of other industries points the way. For example, in the audiovisual sector, movies were originally distributed only in cinemas, but that all changed with the advent of TV, VHS, DVD and now multi-channel streaming services. In this evolution, reliable information was required to attract consumers.

Digital will change the game in similar ways in the funds industry, particularly at the point of sale. Personalised investment products, sophisticated risk analysis in real-time, highly automated regulatory reporting and much more are all exciting possibilities.

Yet until now, the more fundamental question of data quality is too often paid insufficient attention.

Unlike data validation (which leads to the rejection of data at the time of data integration), data cleansing is about removing typographical errors or validating and correcting values against an approved list. However, too much time is spent manually rectifying processing errors due to erroneous or badly classified information.

The amount of data that needs checking can quickly become difficult to manage. For example, a file from one fund company that is updated daily may contain 300 data fields about 1,000 products: 300,000 data points. For each, it is good practice to conduct ten rounds of data controls, adding up to 3 million data checks. Moreover, it might be necessary to process 100 such files each day, requiring a total of 300 million acts of data verification. This requires sophisticated tools that could scan and highlight anomalies in “data lakes” and meta data, enabling the information to be interrogated, reclassified and enriched in a flexible, adaptable fashion. Clearly managing this is beyond human capacity, and teams need a big data approach.

An ongoing iterative approach would embed communication between digital producers and digital consumers, creating a virtual loop that will even further increase data accuracy. Such a process would need to be curated by a neutral, trusted hub.

This is another area in which mutualising these processes enables cutting-edge tools to be efficiently used while minimising cost.

The investment funds industry aims at tailoring products and services to each client’s financial goals. Yet the most urgent data management task is helping to ensure that basic information is accurate and robust. Data quality management will boost services, reduce time to market and cut costs, as well as opening the way for the application of data-analysis tools.

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