Let’s be honest: nearly every organization struggles with data challenges. Ranging anywhere from ineffective data management solutions to patchy data security to, well, just plain incorrect and outdated data. It’s such a common problem that the phrase “bad data” itself (or it’s edgier cousin “Dirty Data”) is now a widely accepted term that covers every data inconsistency under the sun. Like clean eating, clean data has become the aspirational ideal that everyone claims to want, but few put the effort and resources into achieving.
Now, we’ve already talked at length about the importance of good data, and in previous blogs we’ve walked you through the key steps required to clean up bad data. Today we’re taking a deeper look at the implications of ignoring your data problems, and the big financial cost that these small inaccuracies can lead to.
Cost #1: Ineffective Budgeting
Any organization worth their salt relies on data to form their strategic decisions. But as the craze of big data dies down, companies stuck in the resulting inaccurate-field wasteland have had to reckon with the drastic impact it’s had on their reporting, and therefore their budgeting. When you’re dealing with incomplete (at best) or incorrect (at worst) reports, stakeholders are going to end up making poorly informed budget decisions. Think incorrect customer information doesn’t add up? The Harvard Business Review has bad data costing the U.S over 3 trillion per year. Ouch! That’s well worth investing time in a proper data-normalization effort.
Cost #2: Bad Sales & Marketing
When you’re dealing with duplicates, missing fields, and invalid information it’s nearly impossible to say if you are reaching the correct people, at the correct time. Terrible bounce rates, wasted staff time hunting for the correct information, and overall decreased efficiency as you struggle to reconcile inconsistencies hurts both your productivity and your campaign strategy as a whole. This should matter to both Sales and Marketing, as it can impact your customer service, and even damage your reputation.
New Global Regulations
Beyond the scope of just our individual businesses, the EU’s new legal framework the GDPR, or General Data Protection Regulations, is going into effect on May 25th, 2018 and will change things in a big way for many US-based companies. These new laws will impact all members of the EU, as well as any company (regardless of country) that offers goods or services to individuals in the EU. These new data protection laws were meant to standardize data policy and change the ways personal data can be used. New rules like requiring the right to be forgotten and consent for data processing with require a data governance system the likes of which many companies haven’t seen before.
Now, the GDPR is a huge topic that we’ll unpack more but for now suffice to say that failure to comply with these specifications can cost your company up to 4% of annual global turnover or 20M Euros—whichever is more. In short: your bad data architecture could cost you millions (or trillions!) of dollars.
So, while the process of data management might not be the sexiest topic to broach at the company meeting, it’s absolutely a conversation you should be driving until you’ve taken real steps to shore-up your data inconsistencies, before the repercussions hit your bottomline in a big way. Not sure where to begin? Give us a holler, we know exactly where to start and we’d love to help you get there.
Meet Alice Walker
Alice Walker is an endlessly positive Marketer with a passion for content, campaigning, and education. Throughout her career, she has worn many hats: Technical Writer, Content Lead, Project Manager, and Marketing Consultant. Her work with Marketemy combines her love of both teaching and tech, and she is passionate about converting as many people as possible into MarTech enthusiasts like her. A Bay Area native, in her spare time Alice can be found spending time with her family in Davis, CA, cheering on the San Francisco Giants, or working on her latest crochet project (often all at once).