Would you like to know more about Data Management Maturity Review 2020?
Data Crossroads has published its Data Management Maturity Assessment Review, second year in a row. The comparison of the results in 2020 versus 2019 has shown positive developments. Therefore, in this series of articles, we discuss the general trends in data management maturity and will review the following:
- Data management (DM) maturity assessment method
- General trends
- Trends in DM sub-capabilities:
- Data governance/data management framework
- Data modeling
- Information systems architecture
- Data quality
- Data chains
Data Management Maturity 2020: Methodology and General Trends
This series of articles demonstrates Data Management Maturity Trends 2020 based on the Data Management Maturity Review 202o published by Data Crossroads. In this article, the first one of the series, I would like [...]
Data Management Maturity 2020: Data Governance
Would you like to know more about data governance maturity 2020 trends? Data Crossroads has published the Data Management Maturity Assessment Review for the second year in a row. In the previous article of [...]
Data Management Maturity 2020: Data Modeling
Data Crossroads has published the Data Management Maturity Assessment Review for the second year in a row. In the previous article of this series, we explained the methodology for measuring maturity. Then we demonstrated [...]
Data Management Maturity 2020: Information Systems Architecture
Data Crossroads has published the Data Management Maturity Assessment Review for the second year in a row. In the first article of the series, we have explained the methodology to measure maturity. Then, we [...]
Data Management Maturity 2020: Data Value Chains
Data Crossroads has published the Data Management Maturity Assessment Review for the second year in a row. In the first article of the series, we have explained the methodology to measure maturity. Then, we [...]
Data Management Maturity 2020: Data Quality
Data Crossroads has published the Data Management Maturity Assessment Review second year in a row. In the first article of the series, we have explained the methodology for measuring maturity. Then, we demonstrated the [...]
Older articles on this topic:
Data Management Maturity 101: What is a Data Management Maturity Assessment and Why Does a Company Need It?
Data management maturity is a widely debated topic. The word 'maturity' can have different meanings put into different contexts. For this article, I will use the following definition: 'Maturity is a measurement of the ability [...]
Data Management Maturity 102: Benchmarking Your Results in 2019
Are you interested in benchmarking your data management progress? Then take a look at the data management maturity 2019 review results. In the first article of the Data Maturity 101 series, I demonstrated how a [...]
Data Management Maturity 103: Data Management Framework (Data Governance) Maturity
In the previous articles of this series, we have discussed how to build a company-specific data management maturity assessment and the way to benchmark the results. Now it is time for data governance maturity. In [...]
Data Management Maturity 104 : Data Modeling
Are you interested in data modeling maturity? In the previous articles of this series, we have discussed how to build a company-specific data management maturity assessment and how to benchmark the results for data management framework [...]
Data Management Maturity 105 : Information Systems Architecture
In the previous articles of this series, we have discussed how to build a company-specific data management maturity assessment and the way to benchmark the results for data modeling sub-capability. Now we will focus on information [...]
Data Management Maturity 106: Data Quality
Are you interested in comparing data quality maturity in your company with peers in the industry? In the previous articles of this series, we have discussed how to build a company-specific data management maturity assessment and [...]
Data Management Maturity 107 : Data and Information Value Chain
In the previous articles of this series, we have discussed how to build a company-specific data management maturity assessment and how to benchmark the results for Data Quality. Now, it is time to look at the [...]
