Data hygiene isn’t a phrase you hear often, but it should be.
All too often, when data changes or becomes out of date, marketers will simply delete lists and start all over again. But is that really necessary?
This may come as a surprise, but the answer is “no”. By implementing a simple normalization and data hygiene step to your data management process, you can save your lists from extinction and keep up your valuable progress.
Collecting, managing, and processing your marketing data has become an increasingly complex ordeal. Many marketing teams and data specialists use the same general steps – collecting, de-duping, normalizing, and segmenting – but they don’t always conduct these processes in the same order.
As it turns out, putting your data operations in a proper order is the only way to achieve true success without missing out on key data portions. Take de-duping, for example. Many marketers choose to de-dupe their data first — and you might not know this, but by choosing that order, you could be missing out on 15-20% of your data that would have been perfectly usable if you had just normalized it first.
For years at DF Labs, we’ve studied data operations and how to get the most out of our database without deleting lists or missing out on huge portions of contacts. During this time, we’ve discovered a tried-and-true data operations order:
- Nail down your business strategy
- Complement the business strategy with your financial strategy
- Standardize and normalize your data on both new and existing lists
- De-dupe databases
- Segment audiences and supplement contact data with social IDs
- Conduct hygiene checks
After getting your business and financial strategies nailed down, you’ll notice that the very first data step you need to take is to get both existing and new lists in your database normalized and standardized. Marketers who choose to take one of the other steps before normalization risk missing out on huge chunks of their data.
This is because when data is normalized, any new set of data that comes afterward that’s processed in the same order will match. Essentially, everything becomes simple. Now, imagine that you don’t normalize your data first, and you copy and paste data from a European record that uses a different character set. You may search for a record that looks the same – but because it uses a different character set, your search tool misses it. This is an extremely common mistake, and a costly one at that.
These small but deadly mistakes can easily be avoided just by normalizing your data first – each and every time. This makes de-duping and segmenting afterwards a breeze, and you can rest easy knowing you haven’t missed out on any key datasets. Finally, by adding a hygiene check to your data operations, you can ensure that your database remains normalized and up to speed.
Meet the Data Spotlight Tool
Data is not a one-time issue that can be solved with one simple step or solution. Every marketer knows how fast data can change, but by normalizing first and adding a hygiene step at the end, you can create a smooth data operation in which you have a recurring cycle of reassessing the market and ensuring your data keeps up with the times.
Our Data Spotlight tool can help you easily normalize your data and keep it clean. The tool utilizes AI to identify errors in over 800,000 data rules to clean up your data and standardize it in seconds. The Data Spotlight tool allows you to process all data into a standardized format, build more accurate data sets from the start, and create the foundation for a healthy data strategy.
To learn more, watch our full video below:
