Data Quality and Master Data Management

Overview of Data Quality and Master Data Management

Our data engineers are big proponents of the fact that every organization requires appropriate data quality processes in place to make the right decisions, serve its user community and achieve mission and economic success.

• Incorrect data leads to false facts and bad decisions in data-driven environments
• If data quality guidelines are not defined, multiple data copies are generated that can be expensive to clean up
• A lack of uniform concepts for data quality leads to inconsistencies and threatens content standards
• For data silo reduction, uniform data is necessary to allow systems to talk to each other
• To make Big Data useful, data needs business context; the link to the business context is master data (e.g., in Internet of Things use cases, reliable product master data is absolutely necessary)
• Shifting the view to data (away from applications) requires a different view on data, independent from the usage context so there are general and specific requirements for the quality of data

• Organizations must be able to react as flexibly as possible to dynamically changing market requirements. Otherwise, they risk missing out on valuable business opportunities. Therefore, a flexible data landscape that can react quickly to changes and new requirements is essential. Effective master data management can be the crucial factor when it comes to minimizing integration costs.
• Business users are demanding more and more cross-departmental analysis from integrated datasets. Data-driven enterprises in particular depend on quality-ensured master data as a pre-requisite to optimize their business process and develop new (data-driven) services and products.
• Rapidly growing data volumes, as well as internal and external data sources, lead to a constantly increasing data basis. Transparent definitions of data and its relationships are essential for managing and using them.
• Stricter compliance requirements make it more important than ever to ensure that data quality standards are met.

To make matters worse, the analytical landscapes of organizations are becoming more complex. Companies collect increasing volumes of differently structured data from various sources while at the same time implementing new analytical solutions.

This drastically increases the importance of consistent master data. Companies can only unlock the full potential of their data if the master data is well managed and provided in high quality in a timely manner.

Typical Roles to Support Data Quality and Master Data Management in an Organization

Our data engineering team believes these are the typical roles, responsibilities that support Data Quality and Master Data Management efforts in an Organization:

Data owner – is the central contact person for certain data domains. S/He defines requirements, ensures data quality and accessibility, assigns access rights and authorizes data stewards to manage data.

Data steward – defines rules, plans requirements and coordinates data delivery. S/He is also responsible for operational data quality, for example checking for duplicate values.

Data manager – is usually a member of the IT department. S/He implements the requirements of the data owner, manages the technological infrastructure and secures access protection.

Data users – from business departments or IT have access to reliable and understandable data in a timely manner.

Our team will work with you to understand your organization, identify the individuals who can play the above roles, train them on the best practices, to become effective and efficient in playing their roles.

Processes for Data Quality and Master Data Management

We believe the best practice process for improving and ensuring high data quality follows the data quality cycle.

The cycle is made up of an iterative process of analyzing, cleansing and monitoring data quality. The concept of a cycle emphasizes that data quality is not a one-time project but an ongoing undertaking. The data quality cycle is made up of the following phases:

Data quality goals or metrics – needs to be defined according to business needs. These goals should form part of the overall data quality strategy as well. There should be a clear understanding of what data should be analyzed. Does a lack of completeness for some data really matter? What attributes are required for data to be complete? How can a data domain (e.g., a customer) be defined?

Analyzed – questions like “which values can the data have?” or “is the data valid and accurate?” need to be addressed.

Cleansing – is normally done according to engineered individual business rules.

Enrichment – can help with systems and business processes.

Continuous monitoring and checking – to ensure (master) data quality is reached. This can be done automatically via software by applying defined business rules. So at the end of the cycle, there is a fluent transition of the original data quality initiative to the second phase: the ongoing protection of data quality.

The different phases are typically assigned to the aforementioned roles.