If you’re an early-stage startup, chances are you have setup a simple database to collect relevant data. Not every startup can afford to implement a rigorous data strategy from day one, nor are they required to. All founders see the benefit in storing data for future use and many do a good job in sorting and storing it. Many, however, overlook the benefits of implementing those datasets for current use.
At GROM, we generated a plethora of Prescription Data – the data which clinicians use to prescribe bespoke orthoses – within the first year. After ensuring that the data is secured and that patient’s identity is kept anonymous, we went through different ways in which we could make use of it.
The most important step is to sort and clean the data. Only Kaggle is where you can obtain an immaculate dataset. Data cleaning can be performed manually or by using Python if one has enough fluency in this programming language. It’s a straightforward process, albeit a time-consuming one. Cleaning the prescription data helped us find ways to improve prescription forms as well as data storage procedures. These improvements eventually helped in reducing time for future data cleaning processes.
Once we familiarized ourselves with the dataset, we started noticing possible additional data points that we could gather. These ideas were then translated into new features in our software. Over the following iterations, we not only optimized our workflow system but also diversified our dataset. All because we had a basic but solid data collection foundation to start with.
Beyond enhancing internal processes, we have seen the value of our data to showcase the company’s performance. Data visualization is an extensive field but with right tools, it can be performed rather easily. PowerBI and Tableau Public are common softwares that are free and relatively easy for data visualization. Matplot library for Python is another free tool to produce quick visuals. These visualizations are useful to showcase GROM’s growth and performance indicators for both marketing and fundraising purposes. .
You don’t need to hire a Data Analyst or Data Scientist to start working on analysis and predictions. While it may not be a high-priority in most cases, utilizing data from an early stage enables founders to lead their product in a better direction and draft out company’s data strategies well ahead in time.