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:
Creation of value
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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