The Data Drag

Posted by Peter Ellis, Director, P.K. Consulting Ltd. on Behalf of BISAM on May 19, 2015 8:58:00 AM
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Anyone who has worked in an asset management organization is well aware of the challenge that these organizations face when it comes to managing data. There are various reasons why data management is a major challenge in asset management:

  • The quantity of data – the investable universe is huge.
  • The range of data – in order to manage and administer portfolios, and service clients and all their agents and representatives, a wide range of different data types is required, often supplied from different data sources.
  • Variable requirements – the different business functions that need the data have different requirements for the data types that need to be supplied, and for the level of integrity at which data needs to be supplied.

Anyone working outside asset management might wonder why this data challenge has remained such an issue for so long. Indeed, it hasn’t just remained a challenge it has become worse over time.

The main reason for this is probably the lack of a strong data management culture within asset management organizations, especially investment managers. Traditionally, the different data needs of different business functions have been met by implementing local databases. This meant that the same type of data was stored in multiple places and updated at different times. Consequently, different systems would use different values for the same data types creating confusion across the enterprise as to which version of the data was the correct one.

Some asset management organisations have attempted to address this issue with enterprise-wide data management initiatives to centralise, rationalise and unify data usage across the business. Most have only achieved partial success, at best; few, if any, have succeeded from the perspective of performance teams. This lack of success, particularly evident in investment management firms, is probably down to the complexity of data usage within asset management organisations. The same data types are used in different ways in different parts of the business; the same item of data may be sourced from different places and have different values, indeed it may need to have different values according to how it is used in different business functions.

Performance Analysts – Caught in the Middle

The business area that has been hit hardest by this culture of data mismanagement is investment performance analysis. Performance functions sit between the back office and the front office, each of which could have very different views of the same data. Back office teams have to reconcile their data with fund administrators and custodians to ensure consistency with the accounting book of record, and tend to refresh their data on a daily, weekly or monthly basis according to the data type. And while front office teams will often use a sub-set of the data used in the back office, they also use completely different sets of data, most of which needs to be refreshed on an intra-day basis. The back office is often the main provider of the source data that performance analysts need, and the front office is one of the main consumers of performance results. So if the back office and front office have different views of the data used to generate performance results, those responsible for calculating performance will often find themselves having to reconcile differing views of the same data and establishing which set is the right one to be used.

Deep Data Requirements

Another problem for performance analysts is that they need high integrity data at a more granular level than most other business functions, in order to produce accurate performance results. So although the data they need is often used in other business functions, it lacks granularity – leaving performance analysts to source data independently of other business functions. This is a particular issue with the index data and the analytics data that is required to provide detailed calculations of performance returns and calculate performance attribution. There are organizations that offer an aggregation and consolidation service in this area, but they are often more focused on servicing front-office teams whose needs are less demanding than those in performance functions.

Economies of Scale: Reducing the Data Drag

This data challenge has been a source of operational drag for performance analysts for many years, reducing their productivity, affecting the efficiency of service delivery, and preventing them from focusing more effort on value-added analyses.

What is needed is a change of focus. There are economies of scale here. The index data and analytics data used in one asset management organization can be used in many organizations. The constituents of investment portfolios will differ across different asset managers, and even within the same organization; but the constituents of an index are the same across all asset management organizations using that index.

Isn‘t it time to leverage these economies of scale, not just for the benefit of front-office teams but for performance functions too? This would remove the operational drag on performance analysts, enabling them to enhance service delivery and to focus more intellectual effort on analyzing and explaining investment performance – which in turn helps everyone to perform better across the firm.

Performance Systems Vendors: A Guiding Light

But who should take the lead here? There are specialist data service providers who consolidate market data from multiple sources and supply it to asset management organisations through a single channel. But this does not always meet the more demanding requirements of performance teams. Performance system vendors are probably in the best place to meet this specific need. They know what index data and analytics data are required within their systems. They understand the level of data integrity that is required and they should be capable of achieving economies of scale across their client base.

The data challenge facing asset management organizations has been around for a long time. Firms have begun trying to tackle it, but usually tackle it from the perspective of the back office or the front office. It’s time we moved the data requirements for performance analysis to the front of the queue. This will enable them to be more efficient, more informed, and able to deliver a better service. Ultimately, this means enabling the asset management industry to deliver a better service overall to their investors. And surely that’s what we should all be striving to achieve.

Topics: Data Management

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