How you can benefit from data quality

How you can benefit from data quality

Companies are storing more and more data. According to an ongoing IDC study, data growth is between 31 and 60 percent per year for almost a third of the companies surveyed. However, the increasing amount of data also has a downside: as the variety of data increases, so does the quality of the data. To ensure that your business does not have an abundance of incorrect or outdated data in the first place, all employees who come into contact with customers and prospects must ensure data quality.

What is data quality?

Data quality describes the quality of databases. This indicates the extent to which the data is suitable for the intended applications. If the data is required, for example, to send a newsletter, it is important that a correct e-mail address is registered with the contact person. When it comes to personal conversations with customers, the right phone number is necessary. Data quality is therefore highly context-dependent and varies with specific use cases.

Why is data quality important?

No matter how much data you can collect and store, if the quality of those datasets isn’t good, there’s not much you can do with them. Incomplete, incorrect and outdated data records therefore pose a problem for your entire customer management and lead to inadequate or completely incorrect analyzes and forecasts. Marketing activities and campaigns cannot reach all interested parties in a targeted manner if there are incorrect data records, sales cannot optimally support their interested parties and customers receive the same information several times. there are duplicates.

Sales or sales forecasts are also distorted by poor data quality. It is therefore important to pay attention to high data quality and invest time in data maintenance. If contact persons, e-mail addresses or addresses change, you must update the stored information immediately. Various data quality criteria help you achieve your goals.

What are the criteria for good data quality?

There are different criteria that indicate good data quality. Depending on the database, different quality criteria may be useful. Here is a selection of good data quality criteria using customer data as an example:

  • opportunity: The addresses and telephone numbers correspond to reality and are updated, for example, when you move
  • completeness: For example, no house number is missing in the addresses
  • precision: for example no umlauts in e-mail addresses
  • uniqueness: for example no duplicate companies or contact persons
  • reliability: Data is retained and cleaned when contacts change
  • uniformity: The data is stored according to a uniform structure, for example the date of birth is always 02/12/76 and not also 02/12/1976 or 02/12/76
  • relevance: Unnecessary data is not saved
  • accessibility: Data is available for people who need it in their daily work

What problems arise from poor data quality?

Increased workload

Poor master data quality means that your employees cannot optimally serve your customers. If data stocks are incorrect, offers are quickly created incorrectly, deliveries do not arrive at the correct address, and emails are often undeliverable. The result: your employees spend valuable time updating databases.

financial losses

Poor data quality also has a direct impact on your company’s potential revenue. According to a recent study by the MIT Sloan Management Review, the cost of poor data quality represents 15-25% of revenue. If lead management data is not carefully researched, customer data is not retained, or existing data is not used for analysis, sales will quickly be lost.

Incorrect scans

Artificial intelligence and machine learning algorithms are used today to analyze databases and make predictions. Analyzes are based on available data. If these are incomplete or incorrect, this also affects analyzes and forecasts, which in turn can lead to wrong strategic decisions or an inaccurate view of business processes.

Dissatisfied customers

In the worst case, poor data quality can also affect your image and your customers. Wrong addresses lead to poor customer experience in online shipping. If a customer is in your system/database several times, he will also receive newsletters, for example, several times, to which the customer may react angrily.

What are the benefits of high data quality?

Paying attention to different data quality criteria brings you many benefits:

  • Reliable databases and analyzes
  • Accurate forecasts and predictions
  • Effective collaboration between departments
  • saving time and money
  • Securing the future of your business

How to ensure data quality?

In many companies, data is managed in parallel in several systems (eg ERP, CRM, PIM systems, etc.), depending on the type of data. These data silos often result in poor data quality because the information is stored in one system, but the systems do not communicate with each other. Here it can be useful to use a CRM system with the appropriate interfaces to keep data from all systems up to date with little effort.

With a CRM, you have a structured and central contact management, with which you guarantee data quality. The system also helps you to increase transparency in the company. You can use it to optimize your customer management, for example. But beware: the mere fact of introducing or using a CRM system does not automatically lead to improvement. You should also maintain your data carefully and above all continuously in order to increase data quality.

How to increase data quality in CRM?

1. Define the appropriate data structure

A CRM gives you many ways to enter data. So decide what information you really want to store or what is absolutely necessary. Don’t dwell on the details. This also facilitates the management of individual data.

2. Focus on all relevant data

The information you actually want to save depends on your specific needs. At the very least, correct baseline data is essential. Even if the origin of the word is rather reminiscent of something solid and constant, the basic data can often change. There are basically two different types:

  • B2B master data: This is basic information such as company name and exact address
  • Contact person basic data: This is all information about a specific person, including, for example, name, department, position and personal email address.

In this context, high-quality and well-maintained master data is not only the basis for successful communication with your customers. They also make it easier for you to gradually automate all sales and marketing processes and are therefore the driving force behind the digital transformation of your business. You can read more about this in our series on marketing automation.

3. Rely on direct communication

In order to keep the data up to date in your business, it is important that you rely on as much transparency as possible. It starts with creating a new contact. Discuss input fields with all your employees. As a result, everyone knows where to enter what. The more you talk to your company’s CRM users and make them understand the importance of data to business success, the more conscientiously the data will be entered and updated.

4. Integrate a duplicate check in CRM

If several employees are in contact with a customer, it can happen that new contact persons are accidentally created twice. This does not happen with a duplicate check built into the CRM. The system informs the user that the contact already exists. Important information is not lost because phone calls, conversations or visit reports are still only assigned to the existing contact.

Why should you rely on conscientious data maintenance in the CRM system?

The quality and timeliness of data in the CRM is of great importance to the entire company and has an impact on all departments. For example, it guarantees

  • that the sales staff can always find the right contact person in the system,
    to the marketing department their high-quality content in the form of newsletters, blog articles or
  • white papers sent to the right email addresses,
  • the service can access the correct addresses and histories so that it can react quickly to customer requests.

Conclusion: with careful data maintenance for better quality

The subject of data quality in companies is currently often neglected. Starting with an appropriate storage location for datastores, you must also carefully and systematically maintain the systems in which the datastores are contained. So keep them always up to date, it’s the only way to work efficiently and successfully. The more complete and well-maintained your data is, the better your basis for strategic or profitable decisions.

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