In a recent post on Linkedin, my colleague at Standard Bank South Africa, Kaiser Manasoe, asked the following:
An interesting question to answer.
Whilst we may view data as an organizational asset, the reality is that some data is actually a liability especially where the quality is poor or the data collected isn’t fit for purpose and obsolete. The Infonomics of the Data throughout the value chain needs to be understood in order to best leverage it for Digital Transformation efforts. #WestAfricaDataConference #Ghana #HealthyData
I could not agree more!
While well governed, good quality data delivers trusted results – bad data is a liability.
According to IBM bad data costs the US economy $3.1trillion per annum (that’s $3,100,000,000,000,000,000)
This is almost identical to the current US government spend of $3.98 trillion per year.
In other words, if we could clean up our data quality issues the US could run for a year without taxes. That’s got to sting.
Customer data degrades at 30% per annum
We like to believe that our data is accurate.
But, even if we capture data correctly (which is statistically highly unlikely), customer data degrades by between 30% and up to 70% per annum.
People move, change jobs, update their banking account, or get a new improved mobile phone from a different service provider.
Very often, they don’t tell us.
Or they call our call centre but the updated details do not trickle through to other business areas or systems.
The lack of a single customer view hinders the ability to maintain customer data quality.
The 1:10:100 rule
A commonly used rule of thumb to calculate the cost of poor data quality is the 1:10:100 rule.
This rules states that every R1 saves R10 in recovery costs.
More worryingly, ignoring poor data quality costs R100 based on lost profits due to rework or process failures.
Quality data requires maintenance
Many of us in South Africa are experiencing periodic outages to the delivery of basic services, such as electricity and water,
In many cases these outages are due to a lack of maintenance on aging infrastructure, as budgets are allocated to other priorities.
Like our infrastructure, data requires maintenance in order to remain valuable.
Without a data quality strategy, and appropriate data quality tools, data can quickly become a liability