Have you ever come home after a long day’s work and after looking in the refrigerator and pantry wondered, “What should I make for dinner?”
I have done that quite a few times because I don’t really have a weekly meal plan. I work with what I have to turn it into a dinner my family will appreciate.
Similarly, the 3M Healthcare Data Dictionary (HDD) team performs terminology development and mapping work to turn our customer’s raw data into something useful to meet their organizational needs.
I’m fortunate to have seen the many processes that must occur before, during and after the completion of this type of project. To give you the flavor of what goes on, I’m going to liken the project process to preparing dinner in my household.
Ingredients for a Meal: Raw Data
To start, I ask myself, “What ingredients do I have on hand?” Just as a meal starts with ingredients, many of our terminology projects start with data provided by our customers. Most of the data that comes to us from customers is reported in the form of spreadsheets. We consider this “raw data.”—unprocessed computer data that has been entered by a user or generated by the computer. Although some sets of data may come in manageable sizes, others may not be as easy to handle.
Cookware and Utensils: Analysis Tools
“Make sure that you always have the right tools for the job. It’s no use trying to eat a steak with a teaspoon and a straw.” – Anthony T. Hincks
To cook a good stir-fry, I need a good pan or wok, a nice set of bamboo cooking utensils and of course a good and trustworthy source of high heat.
When handling data, the right tools help you manage large sums of data by repeatedly cleaning, rearranging and transforming data, making it possible to comprehend its use and to recognize patterns and trends. Tools provide different functions:
1. Reporting tools: What happened?
2. Analysis tools: Why did it happened?
3. Monitoring tools: What’s actively happening?
4. Forecasting tools: What might happen?
5. Predictive tools: What will probably happen?
6. Prescriptive tools: What actions should be taken?
It is up to you to decide which tool is the best fit for the job!
“A good tool improves the way you work. A great tool improves the way you think.” – Jeff Duntemann
My Family’s Palate: Customer Interviews
Every night like clockwork, my nine-year-old son asks “Mom! What are we having for dinner?” I have a family of picky eaters, so I try to cater to their preferences. Knowing the customer’s needs can help you get the best results when analyzing data. Keep these questions in mind when you interview your customer:
- Who owns the data? Where did it come from? How was it generated? What quirks have you noticed about the data?
- Who uses this data?
- Who makes the decisions?
- What would you like to know that you don’t know today?
- What story would you be able to tell?
So, with the raw data, the right tool(s) for the job, and an understanding of what the customer wants or would like to see, what comes next?
Food Prep, Cooking and Sampling: Data Analysis and Prototyping
Here is my culinary phrase of the day: Mise en place is a French phrase meaning “putting in place” or “everything in its place.” This means that all your ingredients are cleaned, cut, peeled, sliced and ready to go before you start cooking. Cookware is prepped and utensils, bowls and plates are at the ready. This technique allows chefs or even home cooks like me to prep and assemble meals proficiently and, dare I say, “masterfully.” Once all the ingredients have been cooked and the seasoning and spices have melded, it’s time for the taste test! Hmmm…needs more salt…a pinch of sugar maybe…needs to be cooked a little longer…something is missing.
The whole process of going from raw data to final results is iterative:
It involves interviewing the customer, sorting through the data that was given, and providing sample results (prototypes) to the customer. Prototypes are valuable tools used to iron out any potential issues in a project before too may resources are pushed forward into full development. Prototypes can be on either paper or screen and are the closest simulation to the final product as can be. This makes feedback from the customer more accurate allowing for improvements/enhancements. Like improving on a recipe! The process comes full circle with another interview, but this time you get feedback on the examples provided. It turns out, one really good way for people to determine whether the task they’ve been describing makes sense is by seeing real examples. Hence, having a “taste” of what is to come.
The key to this whole process is to take raw data and cook it up in a way that produces results compelling enough for even the most discriminating palate!
The post What’s for dinner? Creating something meaningful from raw data appeared first on 3M Inside Angle.