The first step in the investigatory process of making data driven decisions starts with a question. We will use Fitbit as an example. Fitbit is a wrist worn device that tracks the number of steps an individual takes.
Our goal in this example is to determine a data-driven decision on, “what activity can I do to increase my number of steps per week?”
Data analytics can be broken down into three levels.
Data, Information and Insight. With each level of the triangle we get closer to being able to make data driven decisions that positively impact our organization.
[av_dropcap1]1[/av_dropcap1] Data – the raw numbers collected while doing some process. The “data” Fitbit provides would be the total number of steps a person took in one week. Although important to know, it’s hard to turn this raw data into action that can make a meaningful impact on your steps each week. Just because you know you took 57,000 steps last week; you can’t tell if the steps are trending up, trending down, what time of the day do we have the most steps, etc.
[av_dropcap1]2[/av_dropcap1] Information – the aggregation of the data points put together in an effort to show trends. Again, looking at Fitbit data, this could be a graph showing which days of the week on average you had the greatest number of steps. It provides an increased level of scrutiny and shows trends but still falls short in answering questions in the investigatory process of determining how to improve number of steps per week. It certainly would be helpful to know that on Wednesdays you averaged an additional 2,000 steps over the other days of the week, but without digging in deeper we won’t know “why” we took more steps on Wednesdays.
[av_dropcap1]3[/av_dropcap1]Insight – This is where we answer the “why” question. In our Fitbit example, we want to look at our routine on Wednesdays and see what we are doing that results in more average steps on Wednesdays. Upon investigating, we find that Wednesdays are the days we walk to our favorite coffee spot during lunch. From this discovery, we make a decision to increase our steps per week by walking to our coffee spot every day of the week rather than drinking the coffee that is made in the office. This is a data driven decision made based from insight derived from looking at historical data.
Actionable insights that will grow your practice
In orthodontic practices, the “Data” we derive would be the raw data you can get from your orthodontic practice management systems such as overall conversion rates, production and starts. These numbers are important to collect but as standalone information, have limited ability to improve systems. “Information” would be your dashboards. It will show you trends in a format that is easier to read then raw data but can still be difficult to turn into actionable insights that will grow your practice.
At the height of the pyramid is “Actionable Insights.” This is where you can turn the data into action that will boost profitability and improve systems. This is where you answer the “why” question.
SmileSuite was created to provide actionable insights and answer the “why” questions. With our three-pronged approach to analytics, our In-depth Reporting System, StartAlytics Data Engine and team of analysts, we provide orthodontic practices all they need to reach the pinnacle of the triangle to make data driven decisions that lead to increased profitability.
Have a “why” question? Contact our team today so we can provide you the data driven solution.