Data quality is a topic I have touched on a few times in the past, and so it will come as no surprise to you that I am focusing on this critical component of data, once again. While it was not initially my intention to discuss data quality for this monthly post, I simply could not resist it, having come across a whitepaper that speaks to The 7 Cs of Data Quality, by Melissa, that sparked my interest.
And with 7 aspects to address, I’ll jump straight into it. While not all data is of worth to a business, we know that healthy data can be of great profitable gain. As a result of this, much business focus has been placed on building the organisation around its data – looking at the data foundation, strategy and processes – to ensure value can be reaped.
This whitepaper notes that these identified 7 Cs are the building blocks of data quality and they provide a quick reference “scorecard” to help businesses assess the health of their data. These 7 Cs provide a solid basis for a data quality dashboard or framework and while this whitepaper focuses on contact data specifically, I feel that these stipulated 7 Cs are relevant and can be implemented across any business in any industry, that is focused on making data a core asset.
Below I have outlined my views to the first four of the 7 Cs to support the measurement and management of the data quality process. (Enjoy!)
C1: Certified accuracy
Over and above building a solid data foundation, along with investing in viable data strategies for sourcing, managing, processing and the storing of data perceived to be of quality, businesses can also invest in tools that can certify data’s accuracy to ensure its quality. A business making decisions on data that is of poor quality, or that is outright incorrect, can run into a number of challenges – from making poor decisions to damaging brand reputations when trying to sell to a potential customer who is already a customer or who already uses that product/service.
Data accuracy plays a critical role to the overall success of any data driven business and going the extra mile to ensure accuracy will benefit a business’s data focus in the long-term.
C2: Confidence
The accuracy of data is closely linked to the confidence in said data. If the data is accurate then a business should be confident in the data, right? While this almost seems obvious, businesses are operating in a digital world that is riddled with cybercriminal activity.
Identity data fraud, for instance, is a growing concern, with fraudsters posing to be someone they are not becoming a growing reality for businesses. Data confidence is more than just accuracy – it is about confidence in identity verification – ensuring that the person is who they claim to be against other recorded authentication data.
Confidence can also be perception related. I’ve dealt with customers where we knew the data was correctly processed and presented, from source to data warehouse to management report, but they had no confidence in it, because of previous experiences with management reports. We had to go to great lengths to first prove that the data is accurate before they would trust it. So it is good to measure and manage users’ confidence in addition to the actual accuracy.
C3: Cost savings
The whitepaper by Melissa notes that U.S. businesses lose $600 billion a year due to bad data. That is a large sum of wasted money. The idea of cost savings here is to ensure that bad data does not enter the business or database to begin with. Any bad data can cost the business something and if the bad data is not filtered out of the system, it also has the potential to consistently impact business processes, indefinitely.
Solid planning and set data foundational strategies therefore are key to ensuring that only relevant and viable data are stored and managed within a business’ data capacity and processes. There is also no benefit in performing analytics on data considered to be bad as the results will prove to be ineffective, if not inaccurate too.
C4: Campaign intelligence
I believe this should be referred to as ‘customer intelligence’, as not every organisation runs campaigns, but every one has customers of some sort, be it patients or constituents. Anyway, this C is centred on knowing the customer better. And of course, customer experience remains fundamental to competitive distinction.
A business must have processes in place that allows customer data to be analysed effectively to ensure the business is targeting the right customer with the right message, product or service. Building customer intelligence means utilising the power of available data to better understand and target customers – creating an experience the customer will not only appreciate but be more inclined to attach their loyalty to.
To be continued…
Before I get too carried away, let me stop there for the time being. The remaining three of the 7 C’s are just as insightful and will provide a well-rounded close off to this topic in the next post. Keep a look out for part 2 next month.
