It takes a lot for a “tech news” article to grab my attention these days, but I was astounded to read that an estimated 83 million Facebook accounts are either fakes or duplicates. That’s one in every 12 accounts! A sobering thought for those that use Facebook to connect with new contacts, prospects and acquaintances.
It’s easy to point the finger at companies like Facebook. You can imagine media commentators lambasting the Social Media group: “How on earth could they have so much incorrect data!” It’s a valid question, but these figures do need to be taken into context.
Let me ask you this question: If you work for a corporate organisation, how “clean”, accurate and up-to-date is your data? How much would you estimate is outdated, incomplete or just plain wrong? Research by IBM shows the average is close to 23%. This is mind boggling! Are you better or worse than average, and more significantly, could your data be so wrong it is deceiving you?
Banding around statistics is a fools’ game. The smart question is “how on earth have so many organisations allowed their data to become so unclean”. A smarter question is “how can they prevent this from happening in future”. Of course, the specific answer will vary by organisation, but the key is undertaking good quality analysis when implementing and changing their processes or IT systems.
If you want to increase your data quality, four areas to consider are listed below:
- Understand processes: Generally, data only exists because a business process creates it. As they old saying goes… “garbarge in, garbage out”. Often the best sustainable way to ensure new data is correct, is to understand and fix the processes that create it.
- Define the right rules: Generally, there are a set of business rules (either implicit or explicit) that determine what the value(s) of specific data items can be, or whether a particular data item can be recorded at all. For example “An account can only be saved if an e-mail address is provided”. Creating the right rules—those that reflect the policies of the organisation—will ensure cleaner data.
- Understand data: Defining and understanding the data itself is important. What does the data actually mean in business terms? Does “Sales price” mean before or after sales tax? Subtle differences can be significant, and it’s important that users and staff have a common understanding.
- Watch out for interfaces: Whenever data is extracted, transformed and loaded into a different system, there is the risk of errors. Ensure that interfaces are fully defined and tested.
Data is a main artery of organisations, and it’s important that it’s both accurate and actionable. If you’re organisation doesn’t know the quality of its data, it’s time to find out!