We all know that data quality issues can cause frustrations, delays and hinder decision making. What we don’t always appreciate is that there a cost associated with these data quality issues. In order to elaborate this point, I’ve described 5 ways in which this cost manifests itself along with some examples of what happens in real life:
1. Cost of fixing data
Your team is always in fixing mode, ensuring all data issues reported by users are being resolved ASAP. That’s great news for the users!
Whilst this is a common situation in many organisation that I have come across, what these organisations (or their managers) fail to see is that they’re investing their technical resources to fix data problems instead of focusing on value creating activities within the organisation. In effect, these technical resources are merely expenses to the organisation. The cost to the organisation manifests itself through the hiring of new resources to perform the value creating activities or through delays to these projects due to their reliance on resources with other, more important, priorities.
2. Fines & Penalties
Let’s go back to the first example. Now, what about all those data issues that haven’t still been identified or reported by users?
What if your organisation is legally obliged to respond to customer correspondence, warranty claims, etc within a certain period? If no one notices that something is missing, it won’t be reported, and won’t be addressed. Just the simple action of doing nothing because nothing appears to be wrong in itself is likely to lend your organisation to being fined by the authorities or regulatory bodies.
Again, a simple solution to this might be a series of checks and balances to ensure that everything that comes through the door, regardless of medium, is accounted for, and actioned.
3. Reputation Damage
4. Opportunity Cost
Imagine a situation where a customer order was inadvertently updated with wrong items during the fulfilment process, resulting in the customer not receiving exactly what she ordered. When she calls to complain about it, the order shows the incorrect information, and the customer service rep on the phone responds to the customer saying there is nothing they could do about it as that’s what the system shows.
The issue itself can be addressed in a number of ways, but for the sake of this discussion, let’s say the customer ends up being unhappy with the response and openly shares her displeasure with her circle of family and friends. As a consequence, the organisation would have lost a potential customer base. In the world of social media, as you may already relate to, this is an everyday occurrence.
If on the other hand, the organisation responded differently or even had the right checks and balances in place on their data, this issue may never have arisen.
5. Duplication of systems & data
Having systems with poor quality data means users are constantly frustrated. It is not unheard of for individual business units to go out and source their own IT systems because they run out of patience and can’t wait any longer to do what they should be doing. The organisation will be in an even bigger mess with multiple systems holding the same data in different states of their life cycle, and probably out of date information. Good intention, but with a worse outcome.
It’s not just about the $
This article talks mainly about the financial costs associated with poor data quality and lack of suitable data governance processes. However, as a friend of mine recently put it, if you don’t have the right patient information at all times in the health system you would be putting patient safety at risk. That is a type of cost you wouldn’t want anyone to bear, especially the patients and their families.
If you would like to know more about determining the extent of your data quality issues or need help to address them, contact me via the Adaptive Consulting website stating your current issues and we can have a conversation around how these issues can be reviewed, as well as resolved and prevented.
We have put together an easy to use Data Quality Self-Assessment that anyone could use to determine if they have data quality issues, as well as a Data Quality framework that we use as part of assisting clients with their Data Quality initiatives.