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

How does a lack of data quality affect the work of individual departments?

The effects are very different, but clearly noticeable. As a rule, they have an impact on sales and revenue: Marketing cannot personalize its campaigns based on data. Every mailing is followed by a huge number of returns. Sales lack a basis for precise target group selection. This has a negative impact on sales figures. Call centers can only take inquiries and orders with a lot of effort and queries. This is inefficient and annoys customers and prospects. Finance lacks the data for direct invoicing. The business intelligence and data science departments are unable to create reliable evaluations. Company management lacks the basis for data- and therefore knowledge-based decisions. On the last mile, the delivery of goods often involves multiple journeys or a lot of searching and therefore loss of time.

What do typical use cases look like in different industries?

The usability of customer data for various purposes depends on the fulfillment of the central criteria for data quality. Without high-quality customer data, companies cannot make money. In other words, wherever you do something for data quality, you will immediately feel the positive effects on productivity, efficiency and flexibility - in projects and initiatives of individual departments as well as in the entire company. Typical example industries are: How is a book ordered in e-commerce supposed to reach the recipient if the zip code is missing from the delivery address? How should the employee of a publishing house record a subscription change if several similar data records appear under the same name? How is an energy supplier's newsletter supposed to reach customers and interested parties if the email addresses are incorrect? How is an insurance company supposed to send the renewal of a car insurance policy to the insured person if they have moved and the new address is missing? How is a bank supposed to give its customers life and situation-related recommendations for investments and asset accumulation if the master data is incorrect? You can find more industry examples here .

Why do you need long-term data quality management?

Data is subject to constant change. Relocations, marriages, deaths, births, new development areas, town renaming and incorporation as well as street renaming are all causes of data ageing. If the resulting changes are not tracked, the data will eventually no longer meet the aforementioned criteria for data quality. The datasets, i.e. the data records summarized in databases, are also subject to constant change. The data is constantly being accessed, for example by the service, data is changed, deleted, new data records are created and so on. Errors creep in, such as hearing errors, typos or transposed numbers. There are also digital touchpoints, on a website or mobile, e.g. in e-commerce. Goods are ordered. New customer data is constantly being added. If there are no suitable services for data quality assurance, for example to determine whether a customer is already in the system, total data chaos will break out sooner rather than later.

What other use cases are there for data quality?

Migration projects: High data quality accelerates CRM migrations or migration to SAP S/4HANA, for example. Introducing a unique customer ID: As a uniform, persistent identifier , the introduction requires high data quality. Infrastructure change: The move from on-premise to the cloud includes structures and processes as well as customer data. Their data quality is essential. Mergers & acquisitions: If two or more companies merge, the databases must also merge if potential is to be leveraged. This requires data with high data quality. 360-degree view: Without high data quality, no golden records can be created from existing data records as the basis for a holistic view of each individual customer.

Data Quality

Tailor-made solutions for your business challenges

Companies should take a step-by-step approach to checking and ensuring their data quality. Two areas that are closely connected need to be evaluated: The quality of the data and the associated processes. We advise you on how to get the best out of your data.

Taking a long-term and differentiated view of data quality

Today, data is used in a variety of business processes. These processes describe the intended use of the data and specify the necessary data format and quality requirements. For example, if qualified leads are available in an Excel spreadsheet and are to be used for mailings, the format must be adapted to the new process. This means that data fields must be supplemented, enriched and coded in such a way that they can be imported into the mailing tool. Accordingly, each process has an influence on the data quality.

But which steps are necessary in which order to build up long-term and sustainable data quality? At Uniserv, we have a flexible process model for this. Together, we optimize your data for your business requirements. The goal: your data has exactly the required quality at the end. In other words, your data is optimized for your specific use case. Contact us and we will advise you on how to achieve the best possible data. With this process model in combination with our all-in-one Data Quality Hub solution, you can implement your data quality projects efficiently and flexibly in your system landscape.

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"As experts in customer master data, we get the most out of your data and help you understand your customers."

Sebastian Krebs, Head of Customer Success at Uniserv

"As experts in customer master data, we get the most out of your data and help you understand your customers."

Sebastian Krebs, Head of Customer Success at Uniserv

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!

Why Data Quality with Uniserv?

Uniserv has been on the market for over half a century with a focus on data quality. During this time, it has successfully implemented hundreds of different data quality projects in a wide range of industries. This has resulted in a wealth of experience that our customers have greatly appreciated for decades. As well as the special Uniserv mindset. The Uniserv Customer Data Experts always focus on your customer and prospect data. These are your advantages:

Work directly on and with your data. Fast results instead of lots of paper.

Individual solutions and comprehensive customizing options, depending on the individual use case.

High-performance software for data collections comprising several thousand to millions of data records.

Can be used internationally on the basis of country-specific knowledge bases.

Cloud, on-premise or hybrid.

Fault-tolerant, scalable, industry-neutral.