Big data, small data, unstructured data, silo-ed data – there is so much data about data that it’s quite difficult to know exactly where to focus your efforts in order to leverage the best possible business outcomes. Doing so requires getting down into the data dirt and scrubbing clean the imperfections that lead to poor business decisions. Yes, at the risk of being cliché – it is time for a data Spring Clean.
We know you already know all the benefits associated with clean, unified data – but this article is about more than giving you a mop and broom and sending you on your way. It’s designed to give you the support to bring the clean data argument to other team members, bosses and business units, based upon our 18 years of experience in this game.
The cost of “bad” data can have a dramatic negative impact on your bottom line, losing money as a result of wasted spend, wasted resources and staff time. What happens when you take that shiny new technology and pour dirty data through the pipes?
Marketers and analytics teams begin by deriving inaccurate insights, personas and segments from your data. From this flawed foundation, they develop flawed campaigns, built on inaccurate creative content that pushes out to customers that are irritated at your messages or worse still… opt to purchase from your competitor.
Taking the time to improve the quality of the marketing data alone can’t solve your data woes- but can quickly impact the quality of leads that move through to Sales, the records of customer activity and history that are important to Customer Service or even the value of reliability of that customer for Billing.
If you have a clear view of your customers and prospects, you know who to care about and how to find them. If you add segmentation to your list (which is easy – just ask us), then you can get over 100% gain in sales. From there, you can take what you know and accurately draw parallels as you look across different types of data to see the larger stories that data might be telling.
You need to be cleaning your data now. You need to continue to clean your data on a regular basis – forever. Cleaning your data needs to come as naturally as jumping in the shower to wash off the dirt from the previous day. Maintaining clean data is critical because as you collect not only new data but new TYPES of data, you will continue to build methodologies for ingesting and analysing that data. Keeping your data continuously clean takes time, discipline, effort, innovation and hustle – and the reward is well worth the effort.