Data Cleansing
How Data Cleansing can help you
At the time where your data is analyzed and it is demonstrated that your company data is not up to your standards, or even when entire data management projects are in the pipeline, it would be imperative to immediately start looking into data quality and solving it. Data Cleansing is one of the top products of Wolf Solutions, and with it, you can easily put in place cleansing, in batch processing. These cleansing tools take out the needed customer data from the source systems with connectors, cleanses postal data, augments prior records with even more useful information, and identifies duplicates.
By utilizing data cleansing, you are able to cleanse your data stocks however you wish, or you could use the Best Practice settings. You could cleanse the conspicuous or unnecessary items that you have discovered from Data Analyzer or via automatic discovery by Data Governance. By using the Data Quality Control Center from Wolf Solutions, you can take charge of the data consistency needed for your processes and applications.
The main Features of Data Cleansing
Cleanses data and procures customer data from a great range of source systems.
Verification and adjustments of customers in any customer data that is needed, by making an individual rule set (i.e., utilizing the integrated rule editor).
Converting data formats and dataset structures into a desired format by the user.
Loading cleansed, paired, compiled and converted customer data into the target system(s) – concentrating on quick and efficient mass data processing.
Data Cleansing provides the following Advantages
- Appropriate for smaller cleansing projects that have little data, also appropriate for business-wide cleansing of large scale data. In other words, an extremely diverse tool.
- Data Cleansing’s Unicode abilities with data cleansing translate into the fact that the many diverse and different written characters/geographical-specific characters could be solved and worked with.
- Adaptable integration of multi-national address log-on by the company’s application: by way of a conventional installation solution, alternatively with the Software as a Service, or even by utilizing the Hybrid Model, which allows the customer to enjoy the benefits of both worlds.
Success Factor - Data Quality
Data cleansing is a big step in the process of data quality management, regardless of whether it is used to cleanse obsolete systems of master data in connection with data migration projects, or if it’s for risk management/compliance. The cleansed customer data is vital for the actualization of customer orientation with the primary objectives of data use in operative/specialist fields. For example, to retain customer loyalty, or in dialogue and direct marketing.
Data cleansing makes the best data quality by being up-to-date, reliable and detailed. Hence, data cleansing creates the foundation for greater information content, high-quality analyses results, and high performing business processes.