Our data management framework extends DAMA DMBoK2 to show dependencies and relationships between data management capabilities.
Our Enterprise Information Management Framework
Table of Contents
- What is an Enterprise Data Management Framework?
- Why implement a data management framework?
- Enhance the DAMA-DMBOK2 data management framework
- Our "Where to start" Data Management Framework
- Identify gaps with our Data Management Framework
- How are business operations dependent on data management?
- What are the critical EIM capabilities?
- Data governance defines responsibility for data
- Data Quality delivers trusted data
- Master Data Management creates consistent master data
- Data Architecture delivers supporting technology for data
- Data Modelling and Metadata management provide context for data
- Data Security manages access to data
- Data Integration consolidates data
- Big Data leverages unstructured data for decision making
- Data Warehousing enables trend reporting
- Business Intelligence supports decision making
- Data science uncovers unknown insights and predicts the future
What is an Enterprise Data Management Framework?
An enterprise information management framework (or data management framework) is a model that includes people, processes, and policies necessary to manage enterprise data efficiently. It serves four purposes, including design, implementation, measuring maturity, and assessing the performance of a data management function. Data management comprises all disciplines related to handling data as a valuable resource.
The concept of data management arose in the 1980s as technology moved from sequential processing to random access storage. Companies use data or information management frameworks to ensure they have all the right elements to deliver great data to their business
Why implement a data management framework?
The Carnegie Mellon School argues that an organization's ability to process information is at the core of organizational and managerial competency. Therefore, Information Management frameworks should be implemented to improve information processing capability.
These data management frameworks are designed to help organizations establish and maintain effective information management practices, and can be used to assess current capabilities, identify areas for improvement, and guide decision-making related to data governance and data management.
Overall, information management frameworks are essential for organizations to effectively manage and use information. These data management frameworks provide a set of guidelines, principles, and processes that ensure that information is captured, stored, retrieved, and used effectively. Information management frameworks also help organizations manage risk and reduce vulnerabilities.
Research by Irina Steenbeek suggests that DAMA-DMBOK2 is the most commonly referenced data management framework, although she notes that most respondents using DMBOK2 "make significant adjustments" to fit their needs, whilst 25% of respondents to her survey indicated that they develop their own framework. Another commonly used framework is DCAM, which you can explore through our course.
The reasons are myriad, but it is clear that off-the-shelf frameworks do not meet business needs and that experience is required to adapt and implement each framework to meet the needs of each enterprise.
Enhance the DAMA-DMBOK2 data management framework
As founder members of DAMA South Africa back in 2006, our approach has been heavily influenced by DMBOK, which is widely accepted as a “best practice” approach to data management.
However, although we agree that DMBOK contains much useful information and examples it suffers from two major flaws.
- DAMA views data management as an IT function. While we agree that some of the knowledge areas identified in DMBOK are in IT’s domain, we firmly promote a “business-first” approach to data management, with our focus on areas, such as data governance, data quality, and master data management that must be business driven. We recommend that your data strategy be defined as an extension of your business strategy and that your EIM roadmap and framework are prioritised on that basis
- DMBOK ignores the reality that data management is a series of interconnected and interdependent capabilities, much like a subway system. This issue is described by Steenbeek below, and is the primary focus of this article:
“We know that DAMA-DMBOK2 identifies 11 Knowledge Areas that constitute data management.
The challenge is that these knowledge areas are logically connected and interrelated with each other. A company needs to take these dependencies into consideration when planning and scoping the implementation of a data management framework. You can’t implement data quality without implementing several other data management capabilities such as data governance, data modeling, information systems architecture, and so on.
The following statement: “None of the pieces of the existing DAMA Data Management framework describe the relationships between the different Knowledge Area” completely devalues the practical application of DAMA-DMBOK2”. – Irina Steenbeek
Without an understanding of data management interdependencies and impacts it is impossible to implement a data management framework in a practical way
Our "Where to start" Data Management Framework
Here are some of the data management puzzle pieces that must be in place to deliver a trusted report:
For many, data management is all about analytics and reporting.
In order to deliver a trusted report, several data management dependencies need to be in place.
These dependencies are critical for ensuring that data is reliable, accessible, and up to date.
Here are some of the data management dependencies that must be in place to deliver a trusted report:
Understanding data management framework dependencies
Data governance is a set of standards and business processes which ensure that data assets are leveraged effectively within an organization.
This generally includes processes around data quality, data access, usability, and data security.
Data Quality Improvements
Data quality is the most important output of a data management program.
Finding and fixing data errors delivers efficiency savings, and it helps protect your brand.
To improve data quality, you first must define your expectations for data quality.
Raw data is ingested from a range of data sources, such as web APIs, mobile apps, IoT devices, forms, surveys, and more.
It is then usually processed or loaded, via data integration techniques, such as ETL or ELT.
The data is usually filtered, merged, or aggregated during the data processing stage to meet the requirements for its intended purpose.
The type of data and purpose of it will usually dictate the storage repository that is leveraged.
For example, data warehousing requires a defined schema to meet specific data analytics requirements for data outputs, such as dashboards, data visualizations, and other business intelligence tasks, where a data lake provides more flexibility at the cost of optimisation.
Data security sets guardrails in place to protect digital information from unauthorized access, corruption, or theft.
Data security teams can better secure their data by leveraging encryption and data masking within their data security strategy.
Identify gaps with our Data Management Framework
Leveraging our data management framework allows organisations to identify data management gaps in their processes that must be prioritised to improve results.
These, and other, dependencies are illustrated in the diagram above, by following the arrows from left to right (business-driven) and bottom to top (more IT driven). If these areas are poorly defined and managed in an ad hoc manner the business will struggle with a lack of quality and consistency, poor time to value, and a lack of trust in reporting outcomes
Overall, delivering a report requires a range of data management capabilities that go beyond simply collecting and analyzing data. By ensuring that these capabilities are in place, organizations can ensure that their reports are accurate and reliable, and provide valuable insights to their stakeholders.
How are business operations dependent on data management?
What are the critical EIM capabilities?
DAMA fundis will recognise that our model does not include a number of knowledge areas included in the DMBOK.
Our focus is on those that have more of a business focus and impact, with Data Architecture being the bridge to IT.
What are these critical capabilities that should be prioritised in your road map?
Data governance defines responsibility for data
Data governance is the business foundation of data management - changing behaviour to ensure the delivery of trusted and valuable information.
We assist customers to create and enable practical data stewardship foundations to empower advanced analytics; reduce costs and deliver cost-effective solutions to regulatory challenges
Data governance ensures that the right people are involved at every step of the data management process - making decisions, understanding impact, supplying context, prioritising deliveries, and staying informed.
Data Quality delivers trusted data
Data Quality ensures that data used for operations and for decision-making is fit for purpose.
We help organisations ensure that data is of good quality irrespective of where it enters and how it flows through the enterprise.
Ultimately, high-quality data reduces errors and rework, enables accurate planning and revenue enhancement, and is necessary to meet regulatory drivers
Master Data Management creates consistent master data
Master Data Management is a management discipline supported by technology that combines elements of data governance, data quality and data integration to ensure that the right master data is presented to the right applications at the right time.
We help you to design and govern your master data, identify and consolidate duplicates, and ensure consistent and high-quality master data across your enterprise
Our approach is based on a foundation of sound data governance and data quality to ensure that your master data management initiative is more than just an expensive data consolidation project.
Data Architecture delivers supporting technology for data
Data Architecture is the technical foundation of Enterprise Information Management - allowing data management specialists to plan and design the technical platform to deliver on EIM and broader business requirements.
We have experience in the delivery of master data architectures and provide data architecture training and support for a variety of disciplines, such as data modelling, that are core to data architecture
Data Modelling and Metadata management provide context for data
Data Modelling and Metadata Management are disciplines that provide context and documentation to data.
We support organisations to deliver governed metadata management that helps them to find and understand data, along with training and certification to build capacity in this emerging discipline.
Data Security manages access to data
Data Security protects enterprise information from malicious insiders and hackers
We help companies to define and manage the governance processes necessary to define data security requirements, automate processes and principles necessary to support GDPR, PoPIA and similar data privacy requirements, and protect sensitive data.
Our policy-driven approach to data security ensures the right access, to the right people, at the right time.
Data Integration consolidates data
Data Integration combines data from multiple separate business systems into a single, unified platform (sometimes known as a single view of the truth)
We assist companies to define a data integration approach, delivering high-performance data integration and data preparation solutions, and providing data integration training and certification
Big Data leverages unstructured data for decision making
Big Data is the analytics discipline that takes advantage of both structured and unstructured data to deliver previously unknown insights about your customers, your markets, your products and your infrastructure.
We assist companies with the critical challenges of governing the Big Data stack, data engineering ( delivering a quality data pipeline), and training and certification.
Data Warehousing enables trend reporting
Data warehousing consolidates and stores data for high-performance trend reporting of (typically) historical data. Data warehouses store data to allow known questions to be answered quickly and efficiently.
We assist by helping organisations to document, find and secure data in the data warehouse, understand the lineage of data flowing into and through the warehouse; optimise the warehouse through the delivery of quality, high-performance data pipelines and training and certifying data warehousing specialists.
Increasingly, we provide solutions to modernise the data warehouse - particularly with a view on modern cloud platforms.
Business Intelligence supports decision making
Business Intelligence and Analytics presents information for analysis and decision making
We help organisations enable secure self-service BI and DataOps and offer training and certification of BI Professionals
Data science uncovers unknown insights and predicts the future
Data science uses statistical models, machine learning and AI to support decision-making by predicting future trends and outcomes.
We help you to trust the results by ensuring the quality and lineage of the data underpinning your advanced analytics model, help your data scientists and decision-makers to find the data they need, and provide training and certification