To assess the current state of data maturity, the assessment framework needs to evaluate the Producer and Consumer
of the data, their usage patterns (including SLAs), availability, and overall data management framework. It also
needs to assess the maturity from People, Process, Technology, Policy, and Outcome basis.
The framework comprises of capability areas such as stakeholder (internal/external), strategic business goals, use case,
data sources, and score. These capabilities will be evaluated against specific metrics and criteria for each level.
This can help identify areas for improvement and develop a roadmap for enhancing data maturity across the organization.
Create a capability view for the assessment phase, which includes identifying stakeholders, strategic business goals,
use cases, data sources, and assigning a score for each capability.
To assess the current state of data maturity, each stakeholder's data maturity can be evaluated against this framework,
using specific metrics and criteria for each level. This can help identify areas for improvement and develop a roadmap
for enhancing data maturity across the organization.
Evaluate each stakeholder's data maturity against the framework to identify areas for improvement and develop a roadmap
for enhancing data maturity across the organization. Focus on the data engineering, data value realization dimensions,
and on data governance.
Assess the following dimensions of data strategy using the evaluation criteria listed below:
- Level 0 - Non-existent: No formal data management practices in place. No data management policies, procedures,
- Level 1 - Initial: Ad-hoc data management practices with limited scope. Limited data management policies,
procedures, and practices in place, but inconsistent or not widely adopted.
- Level 2 - Managed: Formal data management practices established and consistently applied. Clear data management
policies, procedures, and practices in place, with some automation and standardization.
- Level 3 - Standardized: Enterprise-wide data management practices and processes in place. Standardized and
integrated data management practices across the organization, with emphasis on data quality, metadata management,
and data governance.
- Level 4 - Controlled: Data management processes are optimized and integrated with other business processes.
Robust data management practices with automated and integrated data management processes and systems.
- Level 5 - Optimized: Continuous improvement and innovation of data management practices.
Follow a standard and a framework driven structured approach to conduct this assessment where
each dimension of a data strategy will be closely examined.
The assessment is conducted for Current vs Target Maturity Level across multiple dimensions.
We will assess the following dimensions of data strategy using the evaluation criteria listed below:
- Metadata Management: Defines and uses metadata to maximize the business value of information assets by
providing a comprehensive, unified view of business context, tagging, relationships, data quality, and usage.
- Data Stewardship: Covers accountability for data assets and provides authorized users with high-quality
- Master Data Management: Defines and uses Master Data Management to ensure that critical data to business
is readily and consistently available.
- Data Security Management: Data Security Management protects data from unauthorized access, use, change,
- Data Policy Management: The organization has clearly defined data policies with ownership rules and
processes in place.
- Data Strategy: Data Strategy encompasses the organization's approach to managing and leveraging data
to meet business objectives.
- Data Architecture: Evaluating the overall architecture of data storage and processing within your Organisation,
including the choice of data storage technologies (e.g. relational databases, NoSQL databases, data lakes),
data integration technologies, and data processing technologies (e.g. ETL tools, batch processing, stream processing).
- Data Integration: Assessing the mechanisms and processes for integrating data from disparate sources, such as
internal data, external data, 3rd party data, social, sensor data, and other sources. This includes evaluating
the effectiveness of data pipelines, ETL processes, and data transformation techniques.
- Data Quality: Assessing the overall quality of data within your Organisation, including data completeness,
accuracy, timeliness, and consistency. This includes evaluating the effectiveness of data validation rules,
data cleansing processes, and data quality monitoring.
- Data Modelling: Evaluating the process of creating data models, including conceptual, logical, and physical models.
This includes assessing the effectiveness of data modelling tools and methodologies.
- Data Warehousing: Assessing the effectiveness of data warehousing strategy, including the choice of data warehousing
technologies (e.g. on-premises, cloud-based), data warehousing architecture, and data warehousing processes
(e.g. data loading, indexing, querying).
- Data Virtualization: Evaluating the use of data virtualization technologies to enable real-time access to data
across multiple systems and sources. This includes assessing the effectiveness of data virtualization tools and
- Data Lineage: Assessing the ability to track the movement of data throughout its lifecycle, including the sources
of data, the transformations applied to data, and the ultimate use of data. This includes evaluating the
effectiveness of data lineage tools and methodologies.
- Data Governance: Assessing the mechanisms and processes for governing the use and management of data within your
Organisation. This includes evaluating the effectiveness of data policies, data access controls, and
data management processes.