DataCanvas

What is the DataCanvas?

Visually structure and manage complex master data projects

The DataCanvas is a visual strategic management tool for perfect data quality.

Digital, high-quality data has become a prerequisite for meeting customer requirements and business goals. With master data management, you can ensure high data quality in your company – making your data more efficient.

Together with the University of Duisburg-Essen, we have developed the DataCanvas workshop. In the workshop, we systematically structure your master data management, classify it using the maturity model (MDM3) and derive concrete tasks.

DataCanvas provides an overview of your master data landscape.
In the workshop we fill out the DataCanvas together.

  • a better enterprise-wide understanding of master data management activities
  • a cross-departmental overview of all master data
  • a coordinated, binding strategy for master data management and digital transformation
  • concrete measures to improve your data quality
  • tools for measuring the success of master data projects

WHO IS DATACANVAS FOR?

The DataCanvas allows data quality managers a visual strategy development for the management of all master data projects.

Chief Information Officers, Chief Digitization Officers, BI and data management specialists as well as IT, marketing, sales and purchasing managers rely on data every day. However, if digital information is not just operationally processed, but strategically analyzed, structured, and processed, the DataCanvas workshop will help.

Improvement of Data Quality

Data is essential - integrated, comprehensible and high-quality data is priceless

Data quality is the benchmarking of data. It depends on how well the data is suitable for a purpose to serve in a particular context. In companies sufficient data quality is essential to operational and transactional processes. Keep in mind that the reliability of analyses and reports is based on the data.

Data quality is affected by the way data is entered, saved and managed. The verification process of reliability and effectivity of data is referred to as data quality management.
Preserving data quality implies checking and cleaning databases regularly.

Common data quality problems include updates, standardizations, validations, plausibility checks and duplications of records.

Data quality can be measured using specific criteria! Some, or none, of the following criteria might apply to improve your data quality:

The data is up to date when you see the features of each item described appear as they are at that moment.

Up-to-dateness

The data adds value when using it leads to a quantifiable increase in a monetary function..

Creation of value

The information is complete when nothing is missing and it is available for the uses that have been set at each stage of the process.

Completeness

The data is of an appropriate size when the amount of information available is enough for the needs that have been set.

Appropriate Size

Data is relevant when it provides the user with the information needed.

Relevant

The data is comprehensible when the user can understand it immediately and use it to fulfil their needs.

Comprehensibility

The data is clear when the exact information needed is available in the right format, in an easily comprehensible manner.

Clarity

The information is presented consistently when the information appears in the same manner constantly.

Consistent Presentation

Information is clearly explained when it appears in a manner which is correct in a technical sense.

Clear Explanation

The data is credible when certificates show a high standard of quality or significant efforts are made in information gathering and dissemination.

Credibility

The data is objective when it strictly factual and free of value judgment.

Objectivity

The data is error-free when it corresponds with the situation as it is in reality.

Error Free

The data has the user’s trust when the source of information, method of transmission and processing system are well-regarded for their credibility and competence.

High Regard

The data is malleable when it is easy to change and use for different purposes.

Malleability

The data is accessible when the user can access it through a simple process and direct method.

Accessibility

The value of data quality for enterprises has to be customly defined early on in MDM-projects (Data Screening). Data Rocket offers preconfigured rules in addition to an editor (Data pipelines), where you can define individual quality criteria and calculation paths to get the most out of your data. DataRocket standardizes the data structure, enables automated detection of data problems and all in all a measurable improvement of your data quality.

OUR CONSULTING SERVICE

Join us in paving the way for perfect data quality in your company!

We will gladly introduce DataCanvas to you and use the data management tool at your location. In an interactive workshop, the experts of the respective use cases jointly come up with a master data management strategy.

The DataCanvas workshop includes two 5-hour moderated days with 4-6 participants.

Our master data management experts guide you through the workshop.

Day 1: Survey of the data landscape status quo
Day 2: Strategy development for master data management

At the end of the workshop, all data controllers will be given a coordinated, binding strategy and a concrete set of measures.

Usually tasks in the following areas are developed through a DataCanvas Workshop

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