Data quality management for SMEs – Part 1
Posted on by experian
Estimated read time: 4 mins
In this blog, I will be delving into the world of Data Quality Management in small to medium enterprises. In my many years of data management experience, necessary processes are not always seen as a “must have” and this seems to be rife in smaller organisations. These are less likely to be governed by stringent guidelines and rules or simply don’t have the IT or marketing resources to formally manage this. They soldier on, collecting data, maybe using the business model and processes they have been following since they were established. It works so why change it? I have often heard this reaction, especially in fast paced transactional businesses…
Businesses realise that profits aren’t what they used to be and acquiring new customers isn’t as easy as it historically was. We all know that customers are now savvier with their money and aren’t necessarily providing the repeat business that they previously blindly provided as loyal customers. Times are changing, as are the strategies businesses need to employ to acquire and keep their customers.
The growth in the use of digital capabilities and the volumes of data that can be accessed has made this a more important consideration. Active data gathering and the utilisation of that data for communications and interactions with their customers is becoming more and more important. This needs to be coupled with effective and essential management of the data quality. The data needs to be fit for purpose.
Organisations either capture data or catch it. The two are very different. Capture of data to create new customers or update existing ones is the recording of key information. You know what you need and why.
Catching data is the process of recording anything and everything, essential or non essential. It is a greedy process, rather like using a net whilst fishing on a large industrial scale – catch what you can. It’s unnecessary, time consuming and wasteful.
Time and time again I’ve seen the classic unstructured approach where a business catches data with a few scattered rules. This data is anything that the customer may be telling them. The essential information like name and postal address are rightly captured, but the non essentials like “number of children” and so on, may be useless if selling a low cost, fast moving good. These can all be obtained externally if needed.
Businesses hunt and gather this data and throw it all onto their database with hopes that it will bring them more opportunities and revenue. However, this is more detrimental to a business. The business introduces more complexities to their already wasteful processes, and creates a mess that continues to spiral out of control. More worrying they may not be compliant and don’t work by the rules of the 1998 Data Protection Act (DPA) and the 2003 Privacy and Electronic Communications (EC Directive) Regulations.
There are two areas to focus on – customer data (i.e. what you collect) and data processing. For the purposes of an initial data strategy and collection programme, you need to focus on the data being relevant and genuine for the purposes of running the business and only kept for as long as required.
So with the rules established, the next priority is data quality. Data quality begins at source. This is the cheapest form of data quality management which is easily controlled and monitored. Any changes are easily implemented and data is relevant, useable and rich.
In Part 2, I will look at the 5 main points for achieving higher levels of data quality.