Ensuring data quality as part of holistic information management is the foundation of industry-wide value creation and innovation. By Paolo Brignardello – head, product management and marketing, Fundsquare.
The digital transformation of the Asian investment fund industry is gathering pace and it is not just about e-fund platforms, apps for online investment or robo-advice. These offer convenience but the changes must go deeper.
The entire fund distribution chain, from fund producers through distributors and the various intermediaries to fund buyers, is being made much more efficient and productive. Such a change has two main goals: firstly, to streamline operations and secondly, to be in a position to innovate and create value.
Driving this transformation, digital points of sale are increasing, thus giving asset managers the opportunity to be closer to investors and to serve them on an almost individual basis.
Data and information flows underpin all of these changes. A strong foundation built around ensuring industry-wide data quality and delivery is therefore needed. Nevertheless, despite this fundamental requirement, data quality is often overlooked or not prioritised.
In the investment fund industry, there are many types of data and datasets that come from and travel between multiple actors in a complex ecosystem. Data can be structured or unstructured, quantitative or qualitative and, often, because of the many links between actors, there is duplication.
With so many interactions in the chain, it is essential that erroneous, incoherent or incomplete information can be found and improved. Big data analytics offers the answer to this issue.
Applying big data methods to investment fund information on an industry-wide level means that data can be analysed, prepared and cleansed with highly automated processes. Currently, these steps are done partly by people, who are fallible and will not be able to scale up to match the expected levels of data in a modern ecosystem. Indeed, people are not up to the task now. As an example, a daily file from a fund company covering 1,000 products with 300 data points means 300,000 data points in total. Each of these undergoes ten checks for a total of three million controls and this is just one file from one company.
The idea behind applying analytics is to ensure that the data has been optimised for the user, wherever they may be in the investment fund distribution chain. Analytics can also be used in conjunction with data lakes, which are repositories containing all types of data and information and which can be more easily exploited compared to today’s data warehouses.
Preparing quality data before it enters the distribution chain is the first step in a deeper, more comprehensive process known as holistic information management.
This is an ongoing iterative process based upon feedback from the data consumer to the data producer, who can then enrich or enhance the data for the user. As such, a process can run in real time. It therefore offers the possibility of a strategic overview of the interactions between actors, be they transfer agents, distributors, fund administrators, promoters, investors or any others.
It is clear that investment fund distribution in the future will be greatly different from what it is today. But to unlock the potential for innovation in tomorrow’s ecosystem, accurate and robust data in a holistic information process is required.
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