Data Management Maturity Scan
The Data management Maturity Scan gives you an opportunity to assess the readiness of your company to get in control of the data you manage. What it means is, providing required data to whomever needs it, at the required place and time and of expected quality.
The scan is designed to be used by data management professionals and means to provide you with:
- A high level estimation of the current status of Data Management in your company
- A recommendation for actions that will promote growth and improvement of you Data Management framework in the next 3 months
- An estimation of required resources.
Latest articles
Knowledge Graphs, Data Lineage, and Metadata Management: Similarities and Differences
By Irina Steenbeek|2022-12-29T13:53:51+00:00December 29th, 2022|
Metadata management (MM), knowledge graphs (KG), and data lineage (DL) are data management capabilities that have a lot of similarities and some differences. In any case, they intersect each other to a great extent, [...]
Knowledge Graphs, Data Lineage, and Metadata Management: Use Cases
By Irina Steenbeek|2022-12-11T20:50:20+00:00December 19th, 2022|
Business drivers that we discussed earlier lead to the corresponding use cases. In some situations, business drivers and use cases are similar. Metadata management (MM) MM assists in performing the following business tasks: Document [...]
Knowledge Graphs, Data Lineage, and Metadata Management: Architecture and Technology
By Irina Steenbeek|2022-12-29T13:43:17+00:00December 12th, 2022|
Now, we will look at the similarities and differences in data architecture and technology required to realize these three capabilities. Metadata management (MM) We have previously discussed that metadata can be of various types. [...]
E-books

MAKE YOUR DATA WORK FOR YOU
A quick guide to data management for financial executives
This book will show you what you can do with your data in order to enhance the efficiency and success of your company.
Find out what data management is and what it means for you and your business.
Discover how data management will help you increase efficiency and reduce the cost of your daily operations.
Learn how to set up data management in your company in 5 steps.

DATA QUALITY NAVIGATION
For finance professionals
Learn how the quality of data influences your daily tasks and how optimizing it can help you save time and money.
Calculate financial implications of poor data quality.
Create an action plan for improving the quality of your data.
Follow 7 steps that will lead you to results.
Whitepapers

DATA MANAGEMENT MATURITY ASSESSMENT REVIEW 2021
Show you how other companies worldwide do in data management.
Help you get a clear view on the results of data management activities in your company and provide you with tips for future development.
Discuss general developments in data management maturity worldwide between 2019 and 2021.

DATA MANAGEMENT MATURITY ASSESSMENT REVIEW 2020
Show you how other companies worldwide do in data management.
Help you get a clear view on the results of data management activities in your company and provide you with tips for future development.
Discuss general developments in data management maturity worldwide from 2019 to 2020..

DATA MANAGEMENT MATURITY ASSESSMENT REVIEW 2019
Show you how other medium-sized companies do in data management.
Help you get a clear view on the results of data management activities in your company.
Provide you with insights and tips for future development.

GDPR MEETS DATA MANAGEMENT
ind out how GDPR effects your business and what data management has to do with all of it.
Learn about different short-term and long-term strategies and how to choose which one is right for your company.
Get to know what actions you need to undertake on short term. We will guide you through this step-by-step and help you make an effective action plan.
Presentations
VIDEO
DATA MANAGEMENT MATURITY TRENDS
2020 VS 2019
MAPPING AND COMPARING METAMODELS:
DAMA-DMBOK2, DCAM 2.2, TOGAF 9.2
DATA LINEAGE FROM BUSINESS PERSPECTIVE
SERIES:
PRACTICAL APPLICATION OF THE “ORANGE” MODEL OF DATA MANAGEMENT