How long does an initial Data Cleansing take?

The duration depends on the amount and condition of the data. Small data sets can be cleansed in a few hours, while large and complex data sets can take several days or weeks. In some cases, it can also make sense to divide a large dataset into subsets or to test different parameterizations against each other. Especially in the case of Identity Resolution , multiple iterations are typically required to achieve the optimal result. Then it also depends on how high the proportion of automatically cleansed data is or how many data records a data steward may have to rework.

What are the challenges of initial Data Cleansing?

The challenges primarily include identifying and correcting errors, standardising data from different sources and ensuring data quality throughout the entire process. In the case of internationally mixed stocks, the data must first be separated by country. This is because different address structures apply for each country. Think of German addresses with a field-by-field structure compared to French addresses with a line-by-line structure. Then it also depends on whether databases contain only private or only company addresses, or are mixed. This is because company addresses have different elements in their names, such as legal form, compared to private addresses with academic or aristocratic titles, for example.

How can you ensure the quality of the data after cleansing?

After data cleansing is before data cleansing. Because data is alive and constantly changing. Regular checks, validations and the implementation of data quality metrics can ensure the quality of the data after initial cleansing. Find out more here...

Which sectors benefit from initial Data Cleansing?

The industry is actually irrelevant. Almost all sectors benefit from cleaner data, especially retail/e-commerce, energy, banking and insurance. The respective use case for which clean data is required is much more important.

How often should the initial Data Cleansing be performed?

This depends on the amount of data and the frequency of data updates. Ideally, measures are taken to permanently back up data quality , as well as regularly cleansing the overall inventory to prevent the data from ageing and maintain quality in the long term.

Mastering data cleansing successfully

Clean customer master data is an important building block for efficient processes and satisfied customers.

Data Cleansing describes the process by which entire databases are corrected for incorrect, incomplete or duplicate data records. In projects, e.g. for data quality, master data management and data migration, it is necessary to initially cleanse the relevant database at the start of the project.

Why Data Cleansing?

In the context of Data Quality, Master Data Management and Data Migration, initial data cleansing plays an important role in many scenarios: when cleansing legacy systems or master data, in migration projects, in risk management or when it comes to compliance. Previously cleansed data is also extremely important for customer loyalty initiatives, in dialogue and direct marketing and in logistics. Cleaned data is the key to project success.

Without cleansed customer data, companies not only risk making strategic mistakes. These also have a negative impact on operating costs, customer satisfaction and, in particular, project progress. Data-based projects simply take significantly longer, fall short of expectations or fail completely. Investments are jeopardised or lost altogether.

Overview