Data Quality Matters – Improving Data Quality Continued (Part 4 of 4)

Now that we’ve covered the four main components of data quality and the most common causes of poor data quality, this final installment will outline the last three ways to improve data quality. In our previous newsletter, we provided an in-depth look at the first two methods to improve data quality:

  1.   Develop a Data Quality Plan – Agencies should be reviewing data on a regular basis. The data quality plan outlines which data elements will be monitored, the process for pulling and reviewing the data to ensure accuracy, how often the data will be monitored, and the individuals responsible for monitoring and correcting the information. It is important, however, to carry out the data quality plan.
  2.   Assess Current Processes and Procedures – Examining data collection and entry processes may help agencies minimize chances of introducing data errors. For example, having staff members interview clients rather than have the client complete the intake form increases the likelihood of capturing all data elements.

For this final installment, we’d like you to consider these final three ways that you can utilize to improve data quality at your agency:

  1.   Minimize the Utilization of Multiple Management Information Systems

When possible, reducing the number of databases an agency has to enter data into can improve data quality.

  1.   Make Data Quality a Priority

Talk about data quality at staff meetings, and ensure a data quality plan is being followed.

  1.   Avoid Redundancy and Missing Data Elements

Avoiding redundancy and missing data elements are important for agencies that collect many data elements or use multiple intake forms and systems. Redundancy often refers to duplicated information. When similar data elements are captured on multiple forms or with similarly-worded questions, it can increase the likelihood that data is inconsistent. For example, one intake form may capture a client’s date of birth while another form captures his or her age. Streamlining this information makes the data collection process easier for the client and ensures data is consistent on all forms and database. Although some data elements may be mandated for collection, agencies can avoid redundancies by being deliberate about what information is obtained from the client.

Missing data elements can be defined in a variety of ways. One is that the information is left blank in a database. Another aspect of missing data is data not being collected from the client. That can result in an intake worker entering “don’t know” as a response option. While the specific data field has a response, it may not be accurate, leading to a data quality issue. To avoid missing data elements, it is important to capture all questions from the client.  

Here are a few suggestions from your ICA Minnesota team on how you can get started improving data quality today!

  • Read all MN HMIS newsletters to ensure that you are up-to-date on all data collection requirements and developments. Click here to sign up for the Newsletter: http://eepurl.com/b4VFTv
  • Keep up-to-date on the latest HUD Data Standards to understand how to better collect HUD Universal Data Elements. The HUD Exchange website is a great place to find these materials, in addition to other more resources that may be helpful: http://www.hudexchange.info/resource/3824/hmis-data-dictionary (New version published July 2017!)
  • Watch our Summer 2017 Webinar on Documenting Living Situation and Disability Status to Determine Chronic Homelessness (3.917) here: http://hmismn.org/videos/#Summer-2017-Webinar-Series
  • Schedule time to make sure that the staff who are collecting data from clients are able to circle back to read and review what the data entry staff eventually entered into HMIS to confirm the data is accurate.
  • Program supervisors should run aggregate reports  (remove any client names and SSNs!) and share them with direct service staff at meetings to check for accuracy and to recognize your data improvement accomplishments!

We hope you’ve enjoyed this data quality series and that this information leads to improved data quality for your organization!