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The three pillars of good data governance

JP Morgan
— 1 minute read

Promoted by JP Morgan

A strong governance regime is the key to exploiting the power of data, according to J.P. Morgan Executive Director, Product Development, Jane Dauparas.

Major Australian super funds and other asset owners are collecting more data than at any point in history. They are building sophisticated data warehouses that not only combine traditional data held by their custodian, but growing levels of data from third parties.

Why? Data promises to deliver greater alpha and operational efficiency. The gains may be small, but in a world where performance is ever more closely scrutinised, the difference they make is crucial.

In the hedge fund world, it is already happening. Data is being combined with machine learning to provide new insights.

For example, one hedge fund is deducing new insights about global battery production by looking at satellite imagery of rivers close to African cobalt mines. Similarly, others are using third-party data, such as shipping information, to reveal potential trends about infrastructure investments such as sea ports.

Data can provide incredible insights and, with a structured data governance approach, can help to resolve real-world business problems.

The secret behind implementing good data governance

But while data can provide powerful insights, it can also deceive when not handled correctly.

Poor data quality cost organisations an average $US15 million a year in 2017, according to Gartner research [1]. The way to avoid much of that pain is for asset owners to institute a well-structured data governance program at the start of the journey to extract maximum value.

Just as good governance can provide the framework for organisations to grow, it can also power new insights. We have identified three key areas that every organisation should address as part of their governance framework, clearing the way for data insights to emerge.

1. Senior management support

Senior management ownership and accountability is critical for any data program to succeed. The best leaders understand that it requires a medium to long-term commitment to consistently generate new data insights that can give their organisation a competitive edge.

The days of complex Excel models understood by few people, with no source control and a high risk of corruption, are slowly being left behind. Many of J.P. Morgan's clients are now setting up dedicated data teams responsible for the quality, lineage and governance of their data.

Having a dedicated data team accountable to senior leadership, allows more control throughout the data ecosystem. These dedicated teams are streamlining their analytics and driving data-driven decision making from the inside. 

2. Clearly define metrics

Asset owners are regularly collecting terabytes of information from disparate sources, raising challenges around data quality. Organisations need to define how they measure data quality and how it is improving if they are to unlock new insights, and this takes dedicated resources and tools.

One of the greatest difficulties is identifying measures and KPIs that truly track data quality improvements. For example, automating the setup of new instruments requires metrics that show whether the number of data quality issues are declining. The metrics must specifically show which areas need the most work, whether by business process area or team. The senior managers responsible for data quality can then allocate resources appropriately.

Data measures must also show if the data quality process is working. A key issue to look for is the number of “aged” issues – if this is increasing then senior managers need to ask why. Is it that the issues are complex and solutions are difficult or is it that the teams responsible are not giving data quality the attention it needs.

3. Adopt a common data language

The importance of a common data language shouldn’t be underestimated. Data insights are too easily lost without a consistent way to tag data, which allows us to ask deep questions and make new observations.

It takes the support of different departments to agree on the same business-friendly words to describe data, which is held in a data dictionary.

The path to data insight: an ongoing journey

Good data governance provides the foundation for new insights but there is no endpoint to the journey. Every asset owner will have their own path, given their disparate needs and goals.

Some will find their greatest insights are about the changing needs and goals of their members. Others, such as those who are internalising their asset management capabilities, will find their greatest insights lead to better investment performance.

The true potential of data is only beginning to be unlocked but, combined with an effective governance framework, will deliver new insights that can power long-term performance.


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