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If your organisation does not recognise the effects of entropy on data, it is time to step back and consider the possible consequences and costs.Your objective is to deliver appropriately accurate data quality on a continuous basis, rather via periodic data cleaning projects, which may leave you vulnerable between times.
When you send an email and receive a delivery failure message, there could be several reasons.
We've listed the most common error messages below, along with suggested resolutions.
Try this fix: These types of errors should fix themselves over time.
Given the fact that data cleansing and integrity checking involves effort and, therefore, costs, managers should bear in mind that not all data is of equal business value.
Which data belongs in which category (and why) should be a business decision, not an IT one. Impure data is like impure blood – not good for the system.
Fortunately, tools to improve data cleansing have advanced dramatically in recent years.
In addition, there are service providers, including cloud suppliers, which specialise in this area.
The starting point for improving data quality and integrity is deciding just what data source, or sources, should be regarded as holding the "truth" when considering any data record. Alas, it is rarely straightforward, not least because so few organisations maintain all essential information in a single data store or database.
Organisations also need to recognise that elements of internal politics may come into play.
This is quite different to occasional attempts to cleanse data sets.
At heart, the objective is to build an IT environment which is supported by operational processes, where important data is verified and, where suitable, enriched at the time it is created, captured or updated.
Given that many of these factors are down to human error, there is an opportunity for automated IT solutions to mitigate or remove such errors and data degradation.