Financial markets around the world have experienced a great deal of stress and turmoil due to the ongoing coronavirus pandemic. Leading to unprecedented levels of risk and significant challenges spanning the middle and back office, the new environment has forced funds to reevaluate and enhance their digital transformation.

Fortunately, those that have already transformed and now leverage the power of data are mapping a road to success while identifying hidden opportunities. Case in point, Bill Ackman’s Pershing Square Capital Management converted $27 million into $2.6 billion during the extreme levels of volatility that trailed the beginning stages of pandemic fears. Imagine if the data being used at Pershing Square Capital Management had been inaccurate, it would have had the potential to hamper profits. Now, iterate the same scenario for a fund that trades in millions of dollars each and every day.

Another aspect that has come to light during this crisis is data’s inherent lack of consistency. Different stakeholders are using different sources of data that, when coupled with different methodologies, have created multiple interpretations of the same situation. For any enterprise, using the same data is critical to ensure fast and correct decision-making.

Thus, having accurate, consistent, reliable, and timely data becomes critical, especially in volatile markets. A holistic data governance module provides an answer to these challenges through the following features:

    1. Exception Dashboards: A single place where users can visualize all of the exceptions in their data across multiple funds and datasets. Users should be able to drill down and analyze exceptions to their most granular level to ensure that the data being consumed is accurate.
    2. Data Correction: Data monitoring would be unproductive unless the users doing the monitoring had the ability to correct the data. Additionally, data correction should support the ability for users to set constraints. Furthermore, an audit of all actions taken on the data must be maintained.
    3. User-Defined Workflows: The corrected data must also go through approval workflows to avoid erroneous alterations. Users should be able to configure n-level workflows from the data governance module itself.
    4. Data Stewardship: There must be a steward of the datasets who will sign off on the datasets as soon as they have gone through all required levels of checks. Data that has been signed off on will be made available for reporting. That way, portfolio managers and analysts don’t have to wait for all the data to be clean and can consume clean data as soon as it is made available.
    5. Standard Data Dictionary & Catalog: This ensures that the data is standardized across the entire firm, allowing for consistency. Read more

Similar to how masks are helping us in the fight against COVID-19, a data governance module can help portfolio managers deal with data breaks and provide reliable and accurate data to make informed decisions during this time of uncertainty.

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