Data management functions are the building blocks of an organisations data foundation upon which data is systematically collected, stored, shared, and analysed. These functions encompass a wide range of activities, including the development of data strategies, the establishment of governance and policies, and the implementation of systems that ensure data quality and security. Each function plays a critical role in the lifecycle of data, from its initial creation and storage to its ultimate use for decision-making and value creation.
By effectively managing these functions, organizations can harness the full potential of their data assets, leading to improved performance, competitive advantage, and informed strategic direction.
Our Services
Maturity Assessment
We conduct a comprehensive maturity assessment to evaluate the current state of data management capabilities within an organization and identify areas for improvement.
Data Management Organisation
We help organisations to build their data management organisation, including defining roles, competencies, and change management processes.
Data Quality
Our focus on data quality involves implementing processes and standards to ensure accuracy, completeness, and reliability of data.
Metadata Management
We help organisation to manage metadata to provide context for data, making it easier for organisations to locate and use their data effectively.
Business Intelligence
Leveraging data for business intelligence, we enable organisations to make informed decisions based on data-driven insights. By helping them to build dashboard and create insights for them to improve decision making.
Reference and Master Data Management
We help organisations to ensure the consistency and integrity of reference and master data across the organisation, which is crucial for operational efficiency and analytics.
Document and Information Management
Our services can include managing documents and information to ensure they are organised, accessible, and secure.
Navigating Data Management Functions
Navigating the intricate world of data management often involves a sea of specialized terminology that can be overwhelming. To demystify this domain, we offer below overview of the diverse components, subjects, and terms that form the basis of data management. We have used the following framework of functions and explanations for each element, to create clarity in the complexity of data management. Recognising the value this framework might hold for others, we’ve decided to share it on our website as a resource for all those who may benefit – so enjoy.
Note that this framework is not of our creation; it is directly sourced from the DAMA International’s Body of Knowledge, who deserve all credits.
Data Management Functions
OVERSIGHT : Data Governance
Lifecycle Management
Plan & Design
Use & Enhance
Enable & Maintain
Foundational Activities
DAMA Data Management Function Framework
Data Management Functions
By effectively managing these functions, organizations can harness the full potential of their data assets, leading to improved performance, competitive advantage, and informed strategic direction.
These functions encompass a wide range of activities, including the development of data strategies, the establishment of governance and policies, and the implementation of systems that ensure data quality and security.
Oversight : Data Governance
Strategy
A high-level plan developed to align data initiatives with business objectives and outcomes.
Data Valuation
The process of ensuring that data is correct, reliable, and used in accordance with standards and requirements.
Principles & Ethics
Fundamental guidelines that influence how data is to be managed, focusing on ethical considerations and best practices.
Policy
Formal rules and guidelines that govern how data is to be handled within an organization to ensure compliance and best practices.
Stewardship
The ongoing process of responsibly managing data resources, ensuring data quality and accessibility throughout its lifecycle.
Culture Change
Efforts made to shift organizational attitudes towards the value and importance of data and its management.
Lifecycle Management
The comprehensive management of data throughout its lifecycle, from creation and storage to archiving or deletion.
Plan & Design
The initial phase in data management that involves setting up the blueprint for the data architecture and systems.
Architecture
The design of the overall structure of a data system, including its components and the relationships between them.
Data Modeling & Design
The process of defining the structure, format, and relationships of data for storage and use in database systems.
Data Storage & Operations
The activities related to securely storing data and ensuring its availability for operations.
Data Integration & Interoperability
Combining data from different sources and providing users with a unified view of these data.
Master Data Management
The practice of defining and managing an organization’s critical data to provide a single point of reference.
Data Warehousing
The storage of large volumes of data in a way that is secure, reliable, and easy to retrieve and manage.
Big Data Storage
Techniques and technologies for capturing, storing, and managing large datasets that cannot be handled by traditional databases.
Reference Data Management
Managing data which is used to classify or categorize other data within a business or enterprise.
Business Intelligence
Techniques and tools for transforming raw data into meaningful and useful information for business analysis purposes.
Master Data Usage
The utilization of key enterprise data assets (master data) across multiple business processes and systems.
Document & Content Management
Systems and processes used to manage, store, and track documents and digital content throughout their lifecycle.
Data Science
The field that uses scientific methods, processes, algorithms, and systems to extract insights from structured and unstructured data.
Data Monetisation
The process of using data to increase revenue, typically through the sale of data, insights derived from data, or data-based products.
Predictive Analysis
The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
Data Risk Management: Security, Privacy, Compliance
The practices of managing risks related to the security, privacy, and compliance of data across its lifecycle.
Metadata Management
The administration of data that describes other data, making it easier to retrieve, use, or manage information. Providing context for data, making it easier for organizations to locate and use their data effectively.
Data Quality Management
The process of ensuring and maintaining the quality of data throughout its lifecycle.