It all comes back to Data Quality – part 2


In picking up the discussion from where I left off last month, before I introduce the fifth to seventh Cs of the 7 Cs of Data Quality by Melissa, I felt it important to emphasise a very pertinent point to the overall success of data to a business’s growth strategy.

The modern digital world has not only made data more applicable to all types of businesses, but it has also presented businesses of all sizes and across geographical locations with the ability to capitalise on opportunities that may have only been applicable to certain types of entities in the past.

And so, when digesting these 7 C’s in totality, bear this in mind. Whether a large corporate or a small to medium enterprise, the 21st century has made data and its quality critical to business operations and success. In fact, a business in the modern world will likely not survive long if priority is not placed on data – and that’s why quality of data is always such a focal point.

So, lets get stuck into the last of the 7 C’s.

C5: Consolidated

Data duplication can be a massive challenge for a business that doesn’t have a dedicated data strategy that places enough attention on effectively consolidating the business data base.

A single, quality view of data is an imperative. The Melissa whitepaper refers to creating one ‘golden record’ of data and I absolutely agree. With a wide variety of data sources, it can become easy for a business to have duplicated sets of data scattered around the place – and duplicated data can have an impact on data quality.

C6: Completed

It is rare to come across a database or data sets that are fully ‘completed’. While data can’t be 100% accurate 100% of the time, ensuring that data sets have as much detail as possible and are therefore ‘near completed’ is important to ensure quality and effective results from analysis.

A sound data foundation and data collection process play an important role in ensuring data completion. Many organisations use data completion KPIs for data capturing staff. There are also a variety of data quality experts who can assist a business in filling in any gaps that may exist within their datasets, to amplify data quality.

C7: Compliant


Digital progression has brought with it a myriad of factors that businesses must take into consideration if they are to benefit from the value of technology – and one of these is data privacy.

Any business operating without data compliance and security top of mind as part of their data strategy or initiatives is playing a dangerous game. There are a number of data related regulations that are being enforced to ensure privacy, and while for some these may seem like a hindrance, such regulations in fact support the notion of data quality. A data compliant business likely holds good quality data that can prove very valuable in the short, medium and long term.

Those who already have data quality top of mind can use the above to further guide a successful journey ahead. Those reviewing these and noting the importance of a data strategy built around quality can use the 7 C’s as the building blocks of a successful business data strategy.

What is important to note however is that these 7 C’s focus on a data quality scorecard or framework. Under each of these categories is a list of other various data related key performance indicators that are relevant to each category and that need to be considered in achieving this scorecard success. Each category must be clearly defined and understood – and then populated by solid accurate data from various systems to ensure overall data quality is achieved.


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