With companies processing more data than ever, it is increasingly important to keep it clean. The costs associated with data decay can be hard to quantify.
Today, everybody seems to be talking about “big data” – this refers to the sheer scale of data that is being created every day in the digital world. In general, however, the conversations around big data revolve around the extra benefits it can bring in terms of trend analysis and business insights.
The flip side to an ever-growing amount of data is the inevitability that some of it will be worthless and more of it will become irrelevant or obsolete over time. The more people and systems that are feeding data in, the more likely that data will become dirty, with errors, missing fields and duplicates. This is why data cleansing can make such a difference to both your marketing campaign and your business as a whole.
We have all heard of the phrase “garbage in garbage out,” and it is a problem that affects most companies to a greater or lesser extent. When you have data arriving into systems from various sources, such as spreadsheets, email lists, etc, and there are various people in sales, administration and management support all involved, then your data can start to decay into garbage in a matter of weeks.
Dirty data’s effect on your marketing campaign can be likened to weeds wrapped around a ship’s propeller. It means you are putting in more time, effort and resources, but achieving less. Dirty data can actually work against you, for example by causing you to send out duplicate mailshots to the wrong people in a company, and achieving the double-whammy of increasing your costs and at the same time making your business look unprofessional.
We can see that as data has become bigger and more complex, the need to keep it clean and organised has also grown. However, the mechanisms for data cleansing have also had to advance at the same rate in order to keep pace with the demands of big data.
The phrase data cleansing is sometimes used synonymously with data purging, in which old or redundant data is removed from a data set. Although this is certainly a part of data cleansing, it goes a lot further than that. The main aim of data purging is usually to clear space for new data, while data cleansing seeks to maximise the accuracy of the data in your system.
The steps in data cleansing
Data processing experts use a four-stage approach to cleansing your business’s data:
The first step is a data quality audit – your systems will be examined and evaluated, and you will be presented with a personalised report, telling you how your data can be improved.
Step two is the process of identifying, consolidating and removing duplicate records. The specialist software used can look at your data across multiple files. You will be amazed to see the amount of duplicates you have on record.
Step three is to either update or suppress data that has become obsolete or irrelevant. This might relate to companies that have gone out of business, or individuals who have moved or passed away.
Finally, your clean, up-to-date data can be enhanced with additional and better contact information, improving both the data you have and the communication channels with your customers and suppliers.
It is possible to get the ball rolling with a free data quality audit, so why not give it a try?