Enterprise Data Management
Encyclopedia
Enterprise Data Management or EDM is:
EDM should not be viewed as dependent on a specific technology strategy or related to an explicit data type definition. It arose to address circumstances where users within organizations independently source, model, manage and store data. These uncoordinated approaches by various segments of the organization can result in data conflicts and quality inconsistencies – making it difficult for users to trust the data as it is incorporated into models, mapped to applications, used to perform calculations, shared among supply chain participants and relied upon for decision-making.
The goal of enterprise data management is trust and confidence in data assets. Effective EDM has multiple components including:
. EDM is often a challenge for organizations because it requires alignment among multiple stakeholders (including IT, operations, finance, strategy and end-users) and relates to an area (i.e. data content management) that has not traditionally had a clear “owner.”
The governance challenge can be a big obstacle to the implementation of an effective EDM strategy because of the difficulties associated with providing a clear business case on the benefits of data management. The core of the challenge is due to the fact that data quality has no intrinsic value. It is an enabler of other processes and the true benefits of effective data management are systematic and intertwined with other processes. This makes it hard to quantify all the downstream implications or upstream improvements.
The difficulties associated with quantification of EDM benefits can translate into challenges with the positioning of EDM as an organizational priority. Achieving organizational alignment on the importance of data management (as well as managing data as an ongoing area of focus) is the domain of governance.
, data dependencies and the tolerance of the organization for operational disruption. Many organizations use formal processes such as service level agreement
s to specify requirements and establish EDM program objectives.
s, data cleansing and normalization, data stewardship
, security constraints and precedence rules. In many cases, these policies and procedures are documented for the first time as part of the EDM initiative.
and cross-referencing. Normalization of all the terms and definitions at the data attribute level is referred to as the metadata
component of EDM and is an essential prerequisite for effective data management.
Enterprise data management as an essential business requirement has emerged as a priority for many organizations. The objective is confidence and trust in data as the glue that holds business strategy together.
- A concept – referring to the ability of an organization to precisely define, easily integrate and effectively retrieve data for both internal applications and external communication.
- A business objective – focused on the creation of accurate, consistent and transparent data content. EDM emphasizes data precision, granularity and meaning and is concerned with how the content is integrated into business applications as well as how it is passed along from one business process to another.
EDM should not be viewed as dependent on a specific technology strategy or related to an explicit data type definition. It arose to address circumstances where users within organizations independently source, model, manage and store data. These uncoordinated approaches by various segments of the organization can result in data conflicts and quality inconsistencies – making it difficult for users to trust the data as it is incorporated into models, mapped to applications, used to perform calculations, shared among supply chain participants and relied upon for decision-making.
The goal of enterprise data management is trust and confidence in data assets. Effective EDM has multiple components including:
Strategy and governance
EDM requires a strategic approach to choosing the right processes, technologies and governanceGovernance
Governance is the act of governing. It relates to decisions that define expectations, grant power, or verify performance. It consists of either a separate process or part of management or leadership processes...
. EDM is often a challenge for organizations because it requires alignment among multiple stakeholders (including IT, operations, finance, strategy and end-users) and relates to an area (i.e. data content management) that has not traditionally had a clear “owner.”
The governance challenge can be a big obstacle to the implementation of an effective EDM strategy because of the difficulties associated with providing a clear business case on the benefits of data management. The core of the challenge is due to the fact that data quality has no intrinsic value. It is an enabler of other processes and the true benefits of effective data management are systematic and intertwined with other processes. This makes it hard to quantify all the downstream implications or upstream improvements.
The difficulties associated with quantification of EDM benefits can translate into challenges with the positioning of EDM as an organizational priority. Achieving organizational alignment on the importance of data management (as well as managing data as an ongoing area of focus) is the domain of governance.
Program implementation
Implementation of an EDM program encompasses a myriad of processes – all of which need to be coordinated throughout the organization and managed while maintaining operational continuity. Below are some of the major components of EDM implementation that should be given serious consideration:Stakeholder requirements
EDM requires alignment among multiple stakeholders (at the right level of authority) who all need to understand and support the EDM objectives. EDM begins with a thorough understanding of the requirements of the end users (and the organization as a whole). Managing stakeholder requirements is a critical, and ongoing, process based in an understanding of workflowWorkflow
A workflow consists of a sequence of connected steps. It is a depiction of a sequence of operations, declared as work of a person, a group of persons, an organization of staff, or one or more simple or complex mechanisms. Workflow may be seen as any abstraction of real work...
, data dependencies and the tolerance of the organization for operational disruption. Many organizations use formal processes such as service level agreement
Service Level Agreement
A service-level agreement is a part of a service contract where the level of service is formally defined. In practice, the term SLA is sometimes used to refer to the contracted delivery time or performance...
s to specify requirements and establish EDM program objectives.
Policies and procedures
Effective EDM usually includes the creation, documentation and enforcement of operating policies and procedures associated with change management, data modelData model
A data model in software engineering is an abstract model, that documents and organizes the business data for communication between team members and is used as a plan for developing applications, specifically how data is stored and accessed....
s, data cleansing and normalization, data stewardship
Stewardship
Stewardship is an ethic that embodies responsible planning and management of resources. The concept of stewardship has been applied in diverse realms, including with respect to environment, economics, health, property, information, and religion, and is linked to the concept of sustainability...
, security constraints and precedence rules. In many cases, these policies and procedures are documented for the first time as part of the EDM initiative.
Data definitions and tagging
One of the core challenges associated with EDM is the ability to compare data that is obtained from multiple internal and external sources. In many circumstances, these sources use inconsistent terms and definitions to describe the data content itself – making it hard to compare data, hard to automate business processes, hard to feed complex applications and hard to exchange data. This frequently results in a difficult process of data mappingData mapping
Data mapping is the process of creating data element mappings between two distinct data models. Data mapping is used as a first step for a wide variety of data integration tasks including:...
and cross-referencing. Normalization of all the terms and definitions at the data attribute level is referred to as the metadata
Metadata
The term metadata is an ambiguous term which is used for two fundamentally different concepts . Although the expression "data about data" is often used, it does not apply to both in the same way. Structural metadata, the design and specification of data structures, cannot be about data, because at...
component of EDM and is an essential prerequisite for effective data management.
Platform requirements
Even though EDM is fundamentally a data content challenge, there is a core technology dimension that must be addressed. Organizations need to have a functional storage platform, a comprehensive data model and a robust messaging infrastructure. They must be able to integrate data into applications and deal with the challenges of the existing (i.e. legacy) technology infrastructure. Building the platform or partnering with an established technology provider on how the data gets stored and integrated into business applications is an essential component of the EDM process.Enterprise data management as an essential business requirement has emerged as a priority for many organizations. The objective is confidence and trust in data as the glue that holds business strategy together.