“I’ve got all this data, what can I do with it?” This is a question I hear all the time. In fact, the number of times I hear this question has increased in the “Big Data” world, where the generation of data is easier, storage is cheaper, and processing is quicker.
“The quality of my data is poor, how do I fix it?” Another question that is very relevant – garbage in does mean garbage out, so as a result data projects often involve spending 70-80% of your time on fixing data quality issues before you can do anything useful with it.
So what’s my response when I hear questions like these. “I understand your situation, but tell me about the business outcomes you want to achieve and then we can work out the insights you need, to inform this outcome and then find quality data to generate these insights.” What I am basically saying is we need to flip our thinking i.e. start with the outcome in mind, not the data. This approach is reinforced in KPMG’s 2019 article, Innovating in aged care with good data.
So, how does this play out in the aged care sector? Let’s consider one the challenges outlined in the 2019 report, Innovation in Aged Care Services – rising expectations for more personalised services. The requirement for more personalised services is placing increasing demand on aged care providers, specifically around how they match their workforce to the service delivery expectations of their clients. The need for providers to address this was a key finding in Chapter 9 of the Interim Report of the Royal Commission into Aged Care Quality and Safety.
In this scenario, the outcome we are trying to achieve is the optimal rostering and matching of a provider’s workforce to the service delivery requirements of the client.
Some of the insights we can look for are:
Some of the internal data which could be used includes:
Some of the external data that could be used includes:
As you can see in this example, we started with the outcome we wanted to achieve. From there, we identify what insights might help us achieve that outcome and then, and only then, do we identify the data sources to use. This is what I like to call, Smart Data….data that is linked to the outcome you are trying to achieve. Yes, good quality data is important – but quality data is useless if it’s not relevant to the problem.
So, my recommendation is to “Think Smart Data, not just big data or quality data.”