Technical solutions for data quality and integration

Technical solutions for data quality and integration

There are many different technical solutions to improve data quality and integration. Some of these solutions are also already widely deployed, according to our research on data quality and data integration. We list the most important solutions for you.

ERP & EPM systems as technical solutions for data quality and integration

Enterprise Resource Planning Systems (ERP) are already prevalent within organizations. They help organizations digitally integrate their operational processes to optimize these processes. Enterprise Performance Management (EPM) systems convert operational data into information about the organization's performance. Especially operations, analysts and controllers use these systems.

ERP and EPM systems also have their benefits for data quality and data integration. They centralize data from different sources and processes, they help automate processes (leading to a reduction in human error), and they help visualize data streams. ERP systems are integrated "by design" when the ERP system supports all business processes. Increasingly, specific processes are removed from the ERP and replaced with a specialized solution for that part of the process. The consequence of this decision presents a data integration challenge. With the development of Cloud ERP products comes the opportunity to return to an integrated solution.

EPM systems have functionality to use data from different sources, for example, to facilitate integrated planning. Within the solution, a data model is created on top of the various sources (data integration). This structure provides guidance for achieving higher data quality by requiring unambiguous data definitions across sources. Data quality problems are also identified when using the solution.

ETL as a technical solution to improve data quality and integration

ETL (Extract, Transfer & Load) is a process of exporting data from a source in order to load it back into another source or the source itself. Usually, this is very difficult because the source and target location use different definitions for data. ETL transforms the data so that it fits into the target location. Especially professionals with a technical background, such as data architects or data engineers, deal with this. Think of ETL as the dictionary of two languages and the data engineer as the translator.

ETL solutions help integrate your systems and their data. They ensure that the data from system A fits like a puzzle piece to the data from system B. In addition, they can also help improve data quality. For example, in the transformation process, you can implement data quality standards, such as removing known spelling errors or indicating zip codes with fewer or more than 4 digits.

Master data management (MDM) solutions to improve data-driven operations

Master Data Management solutions are digital solutions that help your organization keep your master data in order and fall under the wide range of tooling to improve data quality. Through a Master Data Management system, you can quickly modify master data within all the systems the organization uses.

Where is the difference between Master Data Management systems and ETL systems? Both MDM and ETL work by integrating systems with each other. There are many similarities in the purpose of both systems, but the way they arrive at the purpose is different.

In fact, ETL systems focus on moving data between systems and transforming it. This is primarily a technical process. An MDM system is more focused on creating and maintaining a single source of truth that people in operations, such as an employee in the accounts receivable department or account manager, can use to quickly troubleshoot problems. If the account manager makes a change to a customer's master data, the MDM system automatically updates it in all systems. For example, the shipping department also immediately has the new delivery address.

Strength in combination of organizational and technical solutions

These technical solutions help your organization solve data quality & data integration problems. Yet the causes of these problems are not only in the technology, but also in the people and processes within your organization. This is clear from our research on data quality and data integration. The power lies in combining these technical solutions with organizational solutions for improving data quality, such as data ownership.

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