4 steps to ensuring high data quality - establish and sustainably keep up high quality standards

How can you find out about the level of data quality in your company? What criteria should be observed? And how can you improve data quality in the long term? We have summarized the most important steps you should take to establish and sustainably keep up high quality standards.

Poor data quality results in a variety of symptoms: mailings arriving twice at the recipient while e-mails and invoices are not received at all, service staff who are unable to locate the appropriate customer data and cannot reply to inquiries in a professional manner – to name just a few examples. In order to prevent such situations and ensure outstanding customer service, it is vital that the data stored in your CRM system are correct and complete. Follow the four major steps below and address your customers in a personalized and targeted manner during the entire customer journey:

1. Take a look and get an overview

A comprehensive analysis will help you get an overview of your current data situation – allowing you to derive measures based on needs to increase quality. Check, for instance, whether postal addresses, e-mail addresses, salutation options or telephone numbers have been entered correctly and completely. Sending advertising materials to incorrect addresses on a regular basis will not only fail to have the required impact, it will also cost you a lot of money – by analyzing your data you will be able to identify shortcomings in your data stock and apply targeted improvement measures.

2. Cleanse incorrect data sets

In the second step, you eliminate the shortcomings in data quality you have identified. There are appropriate tools to compare and update addresses using international databases. Have your customers changed their address? By performing such a comparison, you can be sure that any new addresses will be entered in your systems immediately. In the process, it is also possible to compare business partner data against sanction lists such as blacklists or blocklists and eliminate the risk of violating legal provisions. Any double entries found may also be combined in one data set according to defined output formats, thus avoiding redundancies.

3. Maintain high quality

Once you have managed to obtain a clean data stock by applying a cleansing process, you should make sure that this high standard is maintained. The most efficient and cost-effective way to achieve this is to allow only data that are suitable for the intended use to be entered into the system, and to establish so-called “first-time-right” mechanisms. This ensures that company and contact addresses are automatically analyzed and cleansed directly when they are entered in the system. Thus you can identify poor data quality already when the information is collected or edited.

4. Run checks on a regular basis

Even after having implemented suitable mechanisms following the initial cleansing process, you should check the quality of your data stock at regular intervals. Data sets need to be adjusted following mergers, insolvencies or changes of legal form, but also after name changes as a result of marriages or divorces. Recurring analyses allow you to keep an eye on the quality of your data and find out how you may optimize them even further.

Relevant improvement of data quality

By repeating these four steps on a regular basis, you will ensure superior data quality so that you can address and assist your customers to optimum effect. Where you should start primarily depends on the intended use of your customer data. For instance, if you frequently send printed mailings, impeccable postal address data are a must – while successful e-mail marketing campaigns require correct e-mail addresses. When providing advice to our customers and implementing suitable measures we therefore not only focus on the implementation of tools, but we grasp the underlying processes and structures as well. Together we will get an overview of your current situation and derive need-based measures, ensuring that your data stock is cleansed where it matters most so that you may implement the most important steps first.

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