What is data quality?

Data quality is about the quality of data; in our context, the quality of customer and prospect data. Customer and prospect data is data that is related to customers and prospects, i.e. first name, surname, street, house number, zip code and city. It also includes an e-mail address, telephone number and bank details. Purchase and order histories and interactions also add to the customer data. And the quality? We speak of high-quality customer data when it meets the following criteria: Timeliness Completeness Correctness Uniqueness

What role does Data Governance guidelines play in long-term data quality?

Data governance guidelines define who is responsible for the maintenance, use and security of data. They define standards for data structures, integrity and up-to-dateness. Without clear governance, inconsistencies, redundancies and security risks can occur. An effective governance framework ensures continuous quality assurance and prevents incorrect or outdated data from affecting business processes.

How can companies sensitise their employees to data quality?

Training and clear guidelines are essential to create an awareness of data quality. Employees should understand the impact of incorrect data and how they can help to maintain quality. Gamification approaches, regular audits and incentives for error-free data entry can also help to establish a data-conscious corporate culture.

Which KPIs are suitable for measuring data quality?

Typical key figures for data quality are completeness (how many data records are complete?), consistency (does the data fit together in different systems?), accuracy (does the data correspond to reality?) and up-to-dateness (how often is the data updated?). Dashboards and monitoring systems help to continuously monitor these KPIs and derive measures for improvement.

How can duplicate or redundant data be avoided?

Duplicate data records are often caused by manual entries or a lack of integration between systems. To avoid redundancies, companies should use deduplicating algorithms and master data management (mdm). Standardized identification numbers for customers, products or suppliers also help to prevent duplicates.

How often should companies check their data quality?

The frequency depends on the type of data. Customer and transaction data should be checked continuously, while master data should be audited regularly - quarterly, for example. Automated checking mechanisms and alerts help to detect and correct errors in real time.

Securing data quality in long term

Customer master data is only useful in the long term if its data quality is guaranteed.

Customer data is constantly changing. New information is added. People move or get married. Streets and places are renamed or added to. These changes must be constantly monitored and regularly updated, otherwise the data will eventually become unusable.

Why you need to ensure data quality in long term?

To achieve data quality, it is necessary to clean the entire database. Therefore, an initial data cleansing is carried out at the beginning of a project or initiative. However, data quality is not a “one-time action”; it is much more a long-term process. This is because customer data is subject to constant change. Therefore, on the one hand, it is important to ensure quality directly during data entry with first-time-right measures. On the other hand, anti-ageing measures must be used to prevent the ageing of customer data. Otherwise, poor data quality is likely to recur.

But where do these constant changes come from? What are the causes and causal relationships? And how do you realize that you have not got the long-term aspect of data quality assurance under control?

Overview

Moving: on average, 25,000 people move house in Germany every day. However, not everyone provides information about their new address, such as banks, insurance companies and other business partners. Without an up-to-date address, however, customer contact is lost, for example for sending important mail.  
Renaming: every year, 45,000 streets and 2,000 place names change in Germany due to incorporations, renaming or new development areas. These changes must also be continuously tracked in databases, otherwise the correct address is no longer available.  
Duplicates: they are like a 'bad penny' and, despite all precautions, sooner or later they will reappear. It is therefore advisable to periodically search the entire database for duplicate and multiple records and then consolidate them so that no valuable information is lost.
Incorrect data entry: spelling mistakes, abbreviations, typos or hearing errors – when creating or modifying customer data, errors can quickly creep in, reducing data quality and affecting processes. This must be avoided.

"Those who think about data quality in the long term and prevent contamination ensure sustainable added value for their customers and their company."

Sebastian Krebs, Head of Customer Success at Uniserv

"Those who think about data quality in the long term and prevent contamination ensure sustainable added value for their customers and their company."

Sebastian Krebs, Head of Customer Success at Uniserv

Data Quality "First Time Right". Clean data right from the start.

Databases are not static collections of information, they are alive. There are constant changes, for example new data records are added or supplemented by adjustments and extensions. For this reason, it makes sense to check the data during operation after the initial cleansing. This principle is called first time right or data quality firewall. It prevents initially cleansed inventories from gradually becoming contaminated by dynamic data traffic because incorrect, incomplete or duplicate data re-enters the system.

The aim is to ensure data quality in the long term. Nothing enters the system unchecked. In concrete terms, this means that when a new data record is entered, it is first checked to see whether it already exists and whether it is even correct. This is done with a fully automatic, highly precise, error-tolerant duplicate detection system. If it is not clear whether it is a duplicate or not, the data quality firewall can display possible alternatives. This is an invaluable advantage in a call centre or service department.

Errors and impurities are often flushed into the system during data entry. Especially if several departments are involved that have different requirements and expectations or if the end customer himself is involved in the input. Spelling mistakes, typos, missing letters or whole words, hearing errors and typo abbreviations often cause problems. By implementing automated data quality checks at the points where data enters the system, the "creeping" contamination of data can be prevented.

First Time Right – High data quality from the beginning

Customer data is a valuable asset. A lot is invested in bringing it up to date. And then? Often nothing happens. Poor quality data spreads more and more throughout the company and hinders the entire business operation.

Implement your data quality firewall - up-to-dateness, completeness and accuracy right from data entry. Download the Uniserv white paper on First Time Right now free of charge. Perfect, high-quality data straight away!

Data Maintenance. Prevent data ageing at the expense of data quality.

Data is subject to a daily ageing process. It is therefore important to ensure data quality in the long term. Regular maintenance measures should be carried out for this purpose. This maintenance includes, for example, checking for outdated information and performing data cleansing tasks at set intervals. This includes checking the accuracy of addresses, telephone numbers and email addresses. This makes it possible to determine whether errors have been caused by external or internal changes to the data. It is also important to regularly check whether data records have been inadvertently created twice, for example. This ensures the quality of customer data in the long term and thus ensures anti-aging.

Why Uniserv ensures sustainable data quality