By now, most of you should be either drowning in data or at least putting together your plans for gathering it. And, while some of you, if you are number geeks like me, may get mighty excited at the thought of collecting and analyzing data, others of you may be thinking, “Hey, wait a minute! Why should we go and collect a bunch of data and statistics? We can see what’s happening in our community with our own eyes!”
And actually, you are at least partially right. Observation and anecdote are two sources of information, also known as – you guessed it – data. When I go into my kitchen and see that the only remaining hint of bread is that little heel at the end that no one wants, that is observational data. The data tells me it’s time to go buy more bread at the store. (Okay, so I am a very lucky woman and my husband does the grocery shopping, but still.) But sometimes observation only tells us part of the story. I may not know, for instance, that my hero of a spouse stocked up on bread at Costco and there are five more loaves in the freezer. And the same goes for observation of what’s happening in a community. We see part of it, but others in the community may see or know things we don’t. So to form a more complete picture, we may need to go and collect data from places other than our own eyes and ears.
Here’s a real-life example. (Thanks for this example goes to Mark Friedman for his book, Trying Hard is Not Good Enough, one of the fabulous books recommended by Diane Casto at our October grantee training.)
In a particular region of one state, Medicaid costs for nursing home care were growing much more quickly than in the rest of that state. Naturally, the question that arose was, Why?
Remember Intervening Variables? A fancy term for asking the question, “Why?”
The “why” in this case turned out to be that many more elders were being admitted to nursing homes in that part of the state. So the next question was, Why here?
Remember Contributing Factors? Another fancy term, this one for the question, “Why here?”
Someone could have remembered some national news story they’d read and suggested that their part of the state simply had more elders than other parts of the state. They could have decided that demand was growing faster than capacity and therefore they must need more nursing facility space in their area. In fact, if they had come to that conclusion, it’s quite possible nobody would have questioned it, and a massive capital campaign might have followed, with lots of people rallying around the need to raise money to build a new nursing facility.
But they didn’t. Instead of jumping into action, they decided to step back and spend some time gathering data. Here’s what they found out: In their area, one of the primary reasons for admission into a nursing facility was hip fractures among elders. Digging even further (“Why here?”) they uncovered a major contributing factor: winter. Elders were experiencing a higher-than-average rate of hip fractures from falls. Many of those falls were happening due to ice and snow during winter months. Instead of building a gigantic, sparkly new nursing facility, the community focused on reducing elder falls. Their efforts included, for example, the much less expensive strategy of organizing a massive effort to shovel the walkways for that community’s elders.
We all want to make our communities better, and when we’re out there doing something, it makes us feel good to know we’re contributing back. When we start by gathering as much information as we can, and then stepping away from our preconceived notions to really consider the information – the data – we’ve collected, then when we begin the doing, we can feel even better, knowing that we’re doing the thing or things that are most likely to make a meaningful, long-lasting difference.
Have you ever had an instance where getting new data has changed your thinking, or altered your course of action? Please share!