There is a basic understanding in your department or other departments of the definition of Master Data.

There have been discussions between departments about Master Data with the purpose of creating a common understanding.

There is a shared definition of some departments in the organization of Master Data.

There is one official definition of Master Data in the organization which is communicated to everyone and the employees all know where to find it.

There are standard interfaces for exchanging data between companies belonging to the same group.

There are some initial - possibly incomplete and isolated - attempts to get an overview on Master Data.

Some departments who are highly related to Master Data can present a Master Data model relevant to their daily work. It covers their daily work but does not regard the other units.

There are some models from different departments. There is already some knowledge about Master Data objects in other key topics and how the data relates.

There is an enterprise-wide Master Data model which was developed from all relevant departments.

The Master Data model is maintained regularly and the responsibilities for the maintenance are clear.

An overview exists with information on systems that use or access Master Data.

The overview is complete.

It has been investigated if there are redundancies in the storing and accessing of data.

There is an overview of all data sources and systems and their interaction. Redundancies can be mapped to systems and sources.

The overview gets maintained on a regular basis and redundancies are resolved if possible. Superfluous systems are substituted.

There is a common rating system about the quality of the Master Data within the organization.

The organization has formalized quality criteria that are important and need to be measured.

There are quality requirements defined taking into account the requirements of different business units.

A quality assessment has taken place in the organization and it is known which quality the data has.

There are defined intervals in which quality assessment is conducted and changes in quality are monitored.

There are defined intervals in which quality assessment is conducted and changes in quality are monitored.

The organization is aware of the repetitional impact on the business if data quality is insufficient.

The organization knows how much money gets lost due to insufficient data quality (e.g lost sales opportunities).

The organization knows how much insufficient data impacts the firm from a non-monetary perspective (e.g. reputational customer-retention).

The organization can classify the impact of bad Master Data quality from both monetary and reputational aspects into financial arguments and can state how much money is lost.

The organization knows about different reasons for quality issues in Master Data.

The organization knows which reasons for bad quality are relevant in the organization.

The organization can precisely state which reasons for bad quality are involved at which source of data entering.

The employees are aware of reasons and sources of poor Master Data quality and the consequences for the business.

The organization can precisely state the weak spots in data setup (e.g. entering information manually - especially foreign words or numbers - results in spelling mistakes).

The organization precisely knows in which areas the data quality is not sufficient according to the defined quality requirements.

The organization is aware of the increasing efficiency and effectiveness in daily work if the quality adheres to the requirements. This is relevant for both employees setting up data and those using the data.

There is a company-wide benchmarking system in place to measure data quality objectively.

Improvement measures are in place to increase data quality.

There is a constant loop of monitoring and improving quality to ensure it has the required quality.

The organization knows who is using i.e. has access to what data In the organization.

The employees know where to get required data. It is assessed if the employees use the provided data sources.

For every source of Master Data it is communicated to the appropriate users that they have access and that the data contains relevant information.

Data repositories get regularly maintained and do not get outdated.

The employees are aware of the sources they have access to and are not reluctant to use any of them (e.g. because of ignorance of the usage).

Data elements have an owner who is either an individual or a department.

The data elements are logically owned by related roles/departments. The data owner defines purpose, usage and content.

The responsible persons for Master Data are communicated throughout the organization. The persons have documented responsibilities.

Data stewardships are established for data areas.

Data stewardship is promoted within the organization and fixed in role descriptions of jobs.

There is a protocol to obtain access to data for the employees.

Unauthorized personnel are not given access to sensitive data.

Every employee is given access to necessary data beforehand. He is automatically equipped with the main sources.

The employees have efficient data sources and have access to the data they need and not much more.

The employees know their sources and have a good overview about what they can find where.

The data is secured against external or default threats with up to data solutions.

Data access is only activated on request which was granted by the responsible authority.

There are clear rules communicated for granting access for certain roles.

Access to data (especially sensitive data) is restricted with passwords that need to be changed regularly and adhere to common standards.

There is awareness about data protection among the employees, e.g. the employees do not leave their computers unlocked when leaving their desks.

The data is stored in an efficient way. Loading does not take too long.

The data logic is displaying the real world situation.

Automated tools regularly check for redundancies and duplicates.

The data base logic is regularly compared to the real world situation it is meant to depict.

The data is stored with innovative solutions to enable data analysis and forecasting (BI solutions).

Data is considered as an object that is undergoing a lifecycle and changes over time.

Data is valued as an organizational asset that brings value to the organization.

The data logic is scalable to treat data according to its position in the lifecycle.

For every data item, a single source of truth is established.

Maintenance labor like entering and updating is automatically logged by the systems.