Learning to love data entry

As boring as the words ‘data entry’ sound, it informs a lot in our society from sporting statistics to government funding. In the homelessness context we do know a lot in Victoria – we know that last year more than 99,000 people sought assistance from specialist homelessness services (SHS). We know that nearly 60% were women, and that people cited escaping domestic violence was the main reason for seeking assistance.  We know all of that because someone somewhere entered the data.

However there is also a lot about homelessness that we don’t know because a lot of crucial data is going uncollected. For example:

  • We don’t know whether 32% of people presenting to specialist homelessness services are homeless or at risk of homelessness;
  • We don’t know whether 31% of people attending a homelessness service have an income, or where it comes from;
  • We don’t know the housing status of 37% of the people at the end of their support

There is no doubt that collecting data in the homelessness sector is hard. Time is usually limited, and clients may not want to divulge certain information about their past and/or current situation. Yet data collection is crucial to improving the homelessness sector for workers and consumers. Missing data makes it hard to track whether the assistance provided has worked, and distorts the information that informs government funding decisions. Looking at the figures above for example, we don’t know the housing status of more than one third of the people who came through the SHS system last year, and we don’t know if they were homeless at the end of their support either. We know that homelessness services are delivering valuable assistance – but the numbers we collect can’t prove it.

When data is collected accurately it can have immediate, practical application. It can be used at an individual level to track outcomes for individual consumers, to apply for funding or to illustrate trends in consumer profiles.

In the coming months we will be blogging about various methods and tips to help workers record better data. Watch this space!