The Value of Quality Data | Union Park Consulting
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Insight: The Value of Quality Data

The idea that data is valuable and useful for decision-making isn’t a new concept among investment offices, but there are some common challenges that often come with the territory.

Data Management & Migration
Jan 17, 2023

Dear Clients and Friends:


We hope you had a wonderful holiday season. One of our resolutions for 2023 is to proactively share best practices with our community.


For our inaugural letter, we’re writing about a theme we’ve seen across many of our projects this past year – a renewed focus on the value of quality data.


The idea that data is valuable and useful for decision-making isn’t a new concept among investment offices, but there are some common challenges that often come with the territory:


  • External reporting is delayed due to errors identified during the review process.
  • Team members are caught off guard by a last-minute investment since the pipeline is not routinely updated.


Many investment offices struggle in implementing data management programs that lead to quality data. While these projects are complex, we’ve found that there are two major components of a data management program that can help you maintain quality data.



Focal Point 1: Assign the Right Ownership and Oversight


When it comes to data management, ownership matters. Regardless of whether you manage datasets internally or use an external provider, your investment office should own the integrity of your data.


If everyone is responsible for the data, no one is responsible for the data. There should be 1-2 persons (deemed as the “data owner(s)”) that own the data integrity for each dataset, such as documents, accounting, exposure/transparency data, pipeline data.


The data owner is responsible for:


  1. 1. Ownership: Owning the review and feedback loop with a service provider or internally – depending on who performs the data entry.
  2. 2. Evaluation: Evaluating, iterating, and documenting the entry and review process so it continues to be as automated and effective as possible.
  3. 3. Leadership: Leading the discussion on what fields should be collected for the dataset. The downstream reporting, search, and analysis will dictate the data you should collect.

Oversight is also needed to achieve quality data. A senior member of the team should meet with the data owner(s) 1-2x/ per month to review the overall process, any errors identified, and any feedback from the team.



Focal Point 2: Optimize Technology to Improve the Entry and Review Process


In addition to ownership and oversight, technology is essential to assist with data management. It needs to be used effectively if you wish to get the most out of your technology investment.


Some best practices when using technology to improve data quality include:

  1. 1. Required fields: Set fields as required to ensure greater completeness at the point of data entry.
  2. 2. Field types: Ensure you structure your data fields in a way where they can be leveraged to achieve the reporting you need/want. Consider whether fields are set up with the correct type such as dropdown, reference, or multi-select and try to avoid the use of free-form text fields where possible.
  3. 3. Push reports: Setup automated reports to be emailed to team members to show incomplete or inaccurate records (e.g., duplicates, missing fields).
  4. 4. Audit log: Use audit log fields to review all new/modified records created in the last X days.
  5. 5. Workflows: Setup scripted workflows to fill in a field based on the value of another field to reduce manual data entry.
  6. 6. Data extraction tools: Eliminate manual data entry where possible by leveraging data extraction tools.


Thank you for your continued trust, support, and friendship. We look forward to continuing the dialogue on investment operations topics in our next quarterly letter. We welcome your feedback on this letter as well as your suggestions on future newsletter topics. We wish you a Happy New Year.



Beth Gilje


Brian Mink

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