How to Tell If Your Data Is Transformation Ready

The era of digital transformation is officially underway, leading more and more brands to adopt a hair-on-fire approach to change. And sure, that nonstop fire hose of new data is exciting, but there are a few painstakingly boring and potentially frustrating steps that will help you on your journey to bigger and better things. Be warned: your hands will get dirty, but it will be worth it.

1. Determine what your data is and where it is.

Understanding the relevance of your data is critical, both in how it affects your customers and in how it affects your business. Subject matter experts within your enterprise can be very helpful in uncovering where the data originates, how it’s maintained, how it flows from one end of your digital landscape to the other, and finally, how it’s used. Product data, customer data, inventory data: these are not exactly the most exciting data items, and probably not what you picture when you think about building your transformation machine. But they are the foundation of the data strategies that will be part of your transformation.

2. Make sure your data is complete and accurate.

Transformation is all about the ability to serve your customers more effectively and streamline your business processes. If the data building blocks that make up your basic business functions are not complete, accurate, and up to date, there will be problems migrating that data and testing the migration of the data (being able to set and test against a baseline for what’s true). Being able to transform the data for use in new systems and new functions will be off the table until completion and accuracy have been achieved.

Data cleanup will be a laborious process and will require constant human intervention. While many issues can be corrected “automagically” by a good data architect, small things like special characters and inconsistent formatting can really slow the process and lead to inconsistencies in how data is transported to new systems.

3. Take small steps.

As frustrating as it seems, taking on small batches of data and testing, reviewing, and testing again will be your best bet for creating consistency. The larger the dataset, the more difficult it will be to get it under control. Batching data for quality review will allow you to double-check your assumptions and root out the kinds of issues that will cause trouble later in the process.

Once you have batches under control and you have developed a consistent set of checks, you can start to automate some of the processes, but only where you can be absolutely sure that the automation will carry on the consistency of your batch reviews.

4. Participate.

Most importantly, participate. While subject matter experts and data architects can solve much of the above, the business owns the data and needs to be heavily involved in decision-making and consistency checks along the way. Play a role in the process and in verifying the results. Accept that unexpected issues will arise and must be corrected. Don’t be afraid to ask uncomfortable questions. With full attention to getting it right, you will be able to start to think about where the next best advantage for your business can be built with your data.