Last time I discussed Data Quality Management and trends that seem to be more prevalent in smaller organisations. I will be giving tips on ways to implement cheap or free quality assurance processes and this week I’m looking at data collection.
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 my next post I’ll explore the top five key areas to address to achieve higher levels of data quality at the source.