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From our experience on data quality projects, there is always a discussion about how data quality can be improved in the company.

The model of the Japanese Ishikawa, a leading figure in the field of quality management, whose methods have influenced the car industry in particular, helps us to illustrate and train.

The idea behind this is that the most important problems to eliminate are listed at the beginning. "The fish stinks at the head first".

There are different dimensions depending on the problem. We would like to list them briefly here using the example of incorrect recipient addresses:

  • Missing standards

Here it is a question of the standardisation of addresses, how to write a street (short or long version), how to write down telephone numbers, but also the question of the language to be used in the CRM, in the database or also according to which standard data should be recorded (DIN versus AFFNOR).

Furthermore, there are no training programmes on why data should be recorded in this way.

  • Employees

In many cases, there is simply no documentation of the data capture rules at the workplace, employees are not instructed enough, the volume to be captured overwhelms the employees (this is often the case in the contact centre) and, more importantly, data quality is not given sufficient priority by the management.

  • Processes

Are the processes in your company capable of dealing with the requirements for clean data quality? Many errors occur here alone, because processes have to be initiated manually or take too long to become visible at the workplaces. Errors are usually pre-programmed.

Data quality needs to be monitored, because invalid addresses interrupt communication with the customer. Whether postal returns or hardbounces, they keep the quantity low.

  • Tools

What often happens is that duplicates are identified. Unfortunately, this usually happens after and not with a purchase, which means that the communication is often already wrong. Keyword: welcoming an existing customer as a new customer. Reasons for this are incompatibility of systems, field length limitations, unclear field structures, but also regulatory problems.

DQ software in the market cleans up data errors by filtering out bad addresses in most cases. Depending on the value of the customer/client, a quality cockpit must be set up here in order to be able to continue to play these contacts.

  • Data sources

In recent years, the sourcing of external data to verify one's own data sources has been increasing, which can lead to errors of a different kind. Reference sources are subject to the phenomenon that these also have to be maintained, whether private customer, postal address, creditworthiness or company databases, they all have errors because essential updates can be missing here as well.

Bear in mind that all providers here often crawl or shuffle online data to keep the databases a jour.

By the way, they maintain this universe with every query.

Used over and over again, the model can help improve data quality in the short, medium and long term and save costs, but also generate higher turnover. In some projects, the turnover with "saved" contacts" increased by 5 - 10 percent.

If you are interested, we would be happy to help you improve the data quality in your company.

TP/ml 28.12.2020


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