We all know data is important. It's the fuel for our business operations. The opportunity to collect and store data about our customers, inventory, sales, etc. is limitless. And, everywhere we turn, we encounter new ways to translate that data into useful information that helps us make informed decisions about and for our customers.
This data, however, can't just be heaped up in a pile and kept forever; it needs to be properly curated to help us overcome our challenges and remain competitive. Given how heavily we rely on data, you would think we would store it in such a way that would make the most efficient and practical sense, right? Not always! Most of us retain data the same way we keep our tax-related receipts — a disorganized jumble in some computerized version of a shoebox under the bed.
How we handle data is also important. We have to strategically plan how much data we need, how we store it and how long we keep it. This isn't always easy because we have to think about our current needs as well as what we might need or want in the future. How much effort do you think it took for legacy databases to eventually accommodate email addresses and multiple phone numbers? These new data points weren't on anyone's mind fifty years ago, but today they're pretty standard fields in many database systems. Even though we might not be able to anticipate specific fields that haven't been conceived yet, we still want to make our systems flexible and scalable.
Now, let's say we have a simple list of contacts. We could create a field/column in our data table and call it Name. Then every time we need to enter a name of a person, organization or company regardless of whether it is Smith Bill, Springfield Big Box Store or a popstar named Sparkle, we place it in the single Name column. This is all well and good until we need to find or sort this data by the last name, then it gets a little tricky. What's even considered to be the last name, the first word, the second word or even either of these, if it's a company?
Data Hygiene Tips
You can see from this simple example that something that might look like a small issue early on in data collection can quickly snowball into a real problem unless we spend some time looking over what data we have and how we store it. Here are a few data organization guidelines...
- When possible, break the data up in the smallest components possible, taking care not to make your workflows too complex in the process. Remember using today's data management platforms like FileMaker, it's often easier to combine elements (first name and last name) than it is to tease them apart.
- Try not to mix attributes. For example, keep company names in their own column and people's names in another. Better yet, keep them in different tables, like a People table and a Company table.
- Make sure what you're gathering is useful and consistent. Most database-driven platforms have some tools in place to make sure that your data is valid, like keeping numbers from being entered into text fields, so use them.
- Keep it clean and tidy. Just like periodically going through your sock drawer and getting rid of worn and mismatched socks, you need to purge your files of outdated data. Keep in mind, many countries are now mandating that you can only keep information about people if you need it, no hoarding allowed, especially when it comes to people's personal information.
This time of year is a great time to take inventory of all the stuff in our garages, basements, closets and yes, even our databases.