Managing data acquisition

Managing data acquisition

Data acquisition is the process of collecting data, including what data is acquired, how, and why.

Data management begins with data acquisition: from the moment that the University is in possession of data, it has a responsibility for managing it appropriately, including complying with laws and regulations that may apply to that data. The same is true for units and individuals; everyone has a responsibility to appropriately manage the data entrusted to their care.

Consider the following when managing data acquisition:

Guidelines for data acquisition

When managing data acquisition, follow these guidelines:

  • Collect only necessary data.
    Only acquire data if it's necessary in order to support the University's missions or operations. Data carries risk; more data, more risk. By reducing the amount of data coming into the University, you can reduce the amount of risk to the University (and simplify University activitieses).
    In particular, avoid collecting personally identifiable information (PII) such as Social Security numbers, driver's license numbers, or other sensitive information unless it is absolutely necessary.
  • Be aware of restrictions on data or its collection.
    Legal, regulatory, privacy, or other restrictions can apply to data collection. This may include:
    • providing notice of data collection
    • obtaining consent to data collection
    • collecting only certain data
    • obtaining a contract or agreement prior to data collection.
  • Consider the source of the data.
    Collecting accurate, authentic data is important. Be aware of the source of the data being collected and whether you can be reasonably certain of the data's integrity.
    For example, it's usually better to get data in a current report from a database rather than relying on a previous report, which may not reflect recent changes to the data.
  • Manage data creation.
    Data acquisition includes data creation. When creating new data, especially by combining existing data, be aware of the sensitivity of the new data sets. Create new data when appropriate, and minimize data duplication where practical.