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Home Page for the Information Technology (IT) Discipline

"Data Mapping Management"


Table of Contents

Introduction: Introduction to Data Mapping Management
Framework: Using This Artifact as a "Data Mapping Management Framework"
Key Terms: Key Terms for Data Mapping Management
Glossary: The "Data Mapping Management Glossary"
Capabilities: Data Mapping Management as an Enterprise Capability
Ownership: Clearly Defined Data Mapping Management Ownership is Critical for Success
Verbs and Actions: Understanding Why Verbs and Actions are Important to Data Mapping Management
Roles: Key Verb and Action Driven Roles For Data Mapping Management
Taxonomy: Understanding Data Mapping Management Classifications or Categorizations
Ontology: Data Mapping Management Ontology as a Means for Language Standardization
Life Cycle (Lifecycle): Lifecycle Phases for Data Mapping Management
Inventories: Data Mapping Management Inventories
Environments: Data Mapping Management Environments
Metrics: Data Mapping Management Metrics
Services: Data Mapping Management as a Set of Services (a.k.a. Data Mapping Management Services)
Service Paradigms: Centralized Data Mapping Management vs. Federated Data Mapping Management
Principles & Best Practices: Common Principles and Best Practices for Data Mapping Management
Further Reading and Reference Material for Data Mapping Management


Introduction: Introduction to Data Mapping Management

This document represents an aggregated, ordered and contextualized view of the material we've been able to compile and publish that is related to the topic of "Data Mapping Management." The goal is to make this page a landing and launch point for all things related to this topic. As our content becomes more complete and more accurate, this page should become a very useful and powerful knowledge base for this topic and all parties interested in it.

You'll find that the content for this document is consistent with that of other discipline related documents. This is intentional. The consistency is based on a knowledge pattern that helps individuals learn more about different topics, quicker and more efficiently. We hope you find the material useful and easy to learn.

It's important to realize that content in this document and any related sub-documents are constantly evolving. Therefore, we recommend you check for updates, regularly, to keep up with the latest material.

The Foundation always welcomes your feedback and suggestions for improvement, as we're always looking for ways to improve our solutions and offerings to the general community.

All solutions published by the Foundation are subject to the terms and conditions of the Foundation's Master Agreement.


Framework: Using This Artifact as a "Data Mapping Management Framework"

This document or artifact, along with everything in it, is intended to act as a "Framework" that addresses various aspects of Data Mapping Management.

The readers will notice that most sections in the Table of Contents (TOC) use a format where the TOC entry is prefixed with a topic name, followed by a short descriptive title (i.e. "TOPIC_NAME: TOPIC_RELATED_SECTION_TITLE"). This is intentional and represents a format by which the Foundation may achieve things like the identification of appropriate topic areas, the segregation of distinct topic areas from each other, the appropriate ordering of topic areas, and achieve the maintenance of consistency, both, within and across different IT Disciplines.

To elaborate, this artifact is intended to:

  1. Organize different areas of the discipline known as Data Mapping Management into clear and compartmentalized areas that allow the Foundation to more effectively and productively collect, document and publish information that pertains to this discipline.
  2. Decompose each area of Data Mapping Management into smaller and, therefore, more digestible units for more efficient learning and understanding.
  3. Document common industry wisdom about each area, piece or subcomponent of Data Mapping Management
  4. Act as a set of Data Mapping Management related best practices and guidelines that have been collected, documented, and published for the benefit of IT Professionals, regardless of their specific industry, line of business, or area of expertise.
  5. Act as a consistent and repeatable pattern for documenting, publishing and learning, both, within this Discipline and across "all" Disciplines.

From the Foundation's perspective, if done correctly, all of the above will allow the Foundation to properly decompose, document and publish content related to each sub-area or sub-topic for each IT Discipline, including this specific discipline (i.e. "Data Mapping Management").

From the reader's perspective, if done correctly, all of the above will allow him or her to easily find and learn about specific areas of interest associated with this and all other IT Disciplines in a manner where the reader may effectively consume and digest material in small atomic segments that act as repeatable and more effective learning units.

As this artifact evolves and progresses, the reader will see it address key areas of the professional IT Discipline "Data Mapping Management" that range from its detailed definition through closely related terms, phrases and their definitions, to its detailed specification of Data Mapping Management Capabilities, and all the way through to defining, delivering, operating and supporting Data Mapping Management Services.

As mentioned previously, this document will continue to evolve and the Foundation recommends the reader check back, regularly, to stay abreast of modifications and new developments. It is also important to understand that the structure of this artifact may change to meet the needs of such evolution.


Key Terms for Data Mapping Management

Before moving on to learn more about the rest of the Data Mapping Management framework, we suggest that you take some time to familiarlize yourself with the following very basic term(s)...

Data Mapping Management:

"1. The professional discipline that involves working with, in or on any aspect of planning, delivering, operating or supporting for one or more Data Mapping Items or any and all solutions put in place to deal with such Items.

2. The solution set that a person or organization puts in place to manage one or more Data Mapping Items.

3. The process or processes put in place by a person or organization to assist in the management, coordination, control, delivery, or support of one or more Data Mapping Items.

4. The Enterprise Capability that represents the general ability or functional capacity for a Resource or Organization to deal with or handle one or more Data Mapping Items. Such a term is often used by Information Technology (IT) Architects when performing or engaging in the activities associated with general Capability Modeling."

In addition to the above basic term(s), you can also learn a great deal about Data Mapping Management by familiarizing yourself with the broader spectrum of terms that make up the Data Mapping Management Glossary...


Glossary: The "Data Mapping Management Glossary"

IT Glossary

Language between IT professionals and the businesses we serve is often a significant barrier to success, as we often spend countless hours trying to interpret each other's meanings. This is often also true between IT professionals who are taught to use certain terms and definitions as part of the organizations and industries they serve. It's when you start to jump from organization to organization, from enterprise to enterprise, and from industry to industry that you realize how much time and effort is wasted on just getting language and meanings correct. For these reasons, the Foundation puts a great deal of focus on terms and phrases, as well as their corresponding definitions. We highly recommend you spend time learning and understanding all of the related terms and phrases, along with their meanings, for all areas of "Data Mapping Management."

Data Mapping Management Glossary
Centralized Data Mapping Management Data Mapping Management Program
Data Mapping Data Mapping Management Project
Data Mapping Automation Data Mapping Management Reference Architecture
Data Mapping Capacity Management Data Mapping Management Release
Data Mapping Catalog Data Mapping Management Report
Data Mapping Catalogue Data Mapping Management Reporting
Data Mapping Configuration Data Mapping Management Roadmap
Data Mapping Configuration Item Data Mapping Management Role
Data Mapping Configuration Management Data Mapping Management Rule
Data Mapping Cost Data Mapping Management Schedule
Data Mapping Data Entity Data Mapping Management Security
Data Mapping Database Data Mapping Management Service
Data Mapping Decommission Data Mapping Management Service Assurance
Data Mapping Delivery Data Mapping Management Service Contract
Data Mapping Dependency Data Mapping Management Service Level Agreement (SLA)
Data Mapping Deployment Data Mapping Management Service Level Objective (SLO)
Data Mapping Document Data Mapping Management Service Level Requirement (SLR)
Data Mapping Document Management Data Mapping Management Service Level Target (SLT)
Data Mapping File Plan Data Mapping Management Service Provider
Data Mapping Framework Data Mapping Management Service Request
Data Mapping Governance Data Mapping Management Software
Data Mapping History Data Mapping Management Solution
Data Mapping Identifier Data Mapping Management Stakeholder
Data Mapping Inventory Data Mapping Management Standard
Data Mapping Item Data Mapping Management Strategy
Data Mapping Lifecycle Data Mapping Management Supply
Data Mapping Lifecycle Management Data Mapping Management Support
Data Mapping Management Data Mapping Management System
Data Mapping Management Application Data Mapping Management Theory
Data Mapping Management Best Practice Data Mapping Management Training
Data Mapping Management Blog Data Mapping Management Vision
Data Mapping Management Capability Data Mapping Management Wiki
Data Mapping Management Center of Excellence Data Mapping Management Workflow
Data Mapping Management Certification Data Mapping Metadata
Data Mapping Management Class Data Mapping Migration
Data Mapping Management Community of Practice (CoP) Data Mapping Plan
Data Mapping Management Course Data Mapping Portfolio
Data Mapping Management Data Data Mapping Portfolio Management
Data Mapping Management Data Dictionary Data Mapping Processing
Data Mapping Management Database Data Mapping Record
Data Mapping Management Demand Data Mapping Records Management
Data Mapping Management Dependency Data Mapping Repository
Data Mapping Management Discussion Forum Data Mapping Reuse
Data Mapping Management Document Data Mapping Review
Data Mapping Management Documentation Data Mapping Schedule
Data Mapping Management File Plan Data Mapping Schematic (Schema)
Data Mapping Management Form Data Mapping Security
Data Mapping Management Framework Data Mapping Software
Data Mapping Management Governance Data Mapping Strategy
Data Mapping Management Knowledge Data Mapping Support
Data Mapping Management Lessons Learned Data Mapping Taxonomy
Data Mapping Management Metric Data Mapping Termination
Data Mapping Management Operating Model Data Mapping Tracking
Data Mapping Management Organization Data Mapping Tracking Software
Data Mapping Management Plan Data Mapping Transaction
Data Mapping Management Platform Data Mapping Unique Identifier
Data Mapping Management Policy Data Mapping Verification
Data Mapping Management Portfolio Data Mapping Version
Data Mapping Management Principle Data Mapping Workflow
Data Mapping Management Procedure Decentralized Data Mapping Management
Data Mapping Management Process Enterprise Data Mapping Management
Data Mapping Management Professional Federated Data Mapping Management
Data Mapping Management Program Regional Data Mapping Management

Please refer to the IT Glossary for other terms and phrases that may be relevant to this professional discipline.

Readers may also refer to the Taxonomy of Glossaries for terms and phrases that are semantically grouped according to IT Disciplines or enterprise domains.

This Data Mapping Management Glossary is a contextual subset of the master IF4IT Glossary of Terms and Phrases. The master glossary can be used by you and your enterprise as a foundation for broader understanding of Information Technology and can be used as a teaching and learning tool for those you work with, helping to ensure a common and more standard language.


Capabilities: Data Mapping Management as an Enterprise Capability

A Capability, as it pertains to Information Technology (IT) or to an enterprise that an IT Organization serves, is defined to be "A manageable feature, faculty, function, process, service or discipline that represents an ability to perform something which yields an expected set of results and is capable of further advancement or development. In other words, a Capability is nothing more than "the ability to do something" or, quite simply, a Feature or Function. Therefore, when applied to an enterprise, a Capability represents a critical Enterprise Feature or Enterprise Function.

When it comes to Capabilities, there are multiple types that an enterprise needs to be aware of. Examples include but are not limited to:

As can be seen above, there are Capabilities that are associated with Resources, Organizations, and Assets such as Systems. All are important to an enterprise.

In the case of this IT Discipline (i.e. Data Mapping Management), we use the word Capability in the context of an Enterprise Capability or an IT Capability, which are both equivalent to Enterprise Disciplines or IT Disciplines, respectively. In short, the Capability of Data Mapping Management represents the ability to deal with any and all Data Mapping Items and anything relevant that is related to or associated with any Data Mapping Items.

If you think about it, a capability is really nothing more than a "verb" or "action that represents "the ability to do something." Understanding this allows us to derive a consistent and highly repeatable set of sub-capabilities for any Noun we're dealing with. For example:

In summary, the implication is that the Enterprise Capability or Enterprise Discipline known as Data Mapping Management is the superset of all the above Sub-Capabilities, as they pertain to or are applied to the discipline-specific Noun: "Data Mapping." This now translates more specifically to:

For a more complete list of very specific Capabilities/Disciplines, refer to the Foundation's Master Inventory of IT Disciplines. It is important to note that this inventory is in a flat or non-hierarchical form, specifically because "hierarchy" is almost always a matter of personal preference or context (what hierarchy is important to one Resource or Organization may be unimportant to another's needs or requirements). Therefore, the Foundation has published its inventory of Capabilities in a non-hierarchical, flat form.

This now brings us to a very obvious problem that surrounds Capabilities, which is the fact that there are simply too many "granular" or "specific" Capabilities to document and publish in any single Capability Model. The end result is that a Capability Model may become unwieldy because of trying to incorporate so many different specific Capabilities. Also, Capability Modeling "Purists," who all have their own (and very differing) opinions about how Capability Models should or should not be represented, almost always refuse to get into the details. To address this, we recommend using a generic set of Capabilities that map to and are driven by the Systems Development Life Cycle. For example:

As you can see from the above, we now have a very limited, controlled and manageable set of Discipline-specific Capabilities for the Discipline Data Mapping Management.

As a reminder, the above Capability representations are "suggestions" for baselining or initializing your own Enterprise Capability Model (ECM). It's recommended that you take the time to work with your enterprise stakeholders to improve upon and/or customize your own ECM so that you can help meet their needs. However, with that being said, it's always a better idea to go in with a baseline that you can modify rather than building your own solution from scratch, especially if your goals are to standardize, not reinvent the wheel, and not deviate too far from what other enterprises are doing to model their own environments. This is especially true if you've never had any experience building ECMs that have gained and maintained full adoption.

Why do enterprises perform Capability Modeling? Enterprises most often build Capability Models that are associated with Data Mapping Management for the following reasons...

Capability Modeling Recommendations: Some things to consider and keep in mind when working on or creating your Data Mapping Management and Enterprise Capability Models...

Learn More About Capability Models: Taking the time to learn about and understand Capability Models, what they're for, and how they're used may help you learn how Data Mapping Management better fits into the broader enterprise. Therefore, we suggest you spend some time reviewing and understanding the IF4IT Enterprise Capability Model...

Enterprise Capability Model

Ownership: Clearly Defined Data Mapping Management Ownership is Critical for Success

IT Discipline Ownership

Here's a very simple fact... If an enterprise does not establish and enforce clearly defined Ownership (i.e. a Resources and his or her Organization are assigned as accountable ownership) for Data Mapping Management, the enterprise has automatically set itself up for failure in its implementation of that discipline. Therefore, if you and your enterprise want to implement and maintain a successful solution for Data Mapping Management, there must be a clearly defined Owner that can and will be held accountable for getting work done, providing transparency, helping with strategy setting, and coordinating implementation of Data Mapping Management as a fully functional and mature enterprise Service.

Having clearly defined Ownership should not be confused with having fully dedicated Resources that spend one hundred percent of their time working on Data Mapping Management. In fact, smaller enterprises can rarely afford to dedicate full time Resources, like larger enterprises can, to all enterprise IT Disciplines. This being the case, all IT Disciplines, including Data Mapping Management, should "always" have clearly defined Owners so that there is always a clear point of accountability and contact for any issues or work that need to be addressed.

In addition to the common best practice of having clearly assigned Ownership for Data Mapping Management, it is also considered a best practice to clearly publish and socialize Data Mapping Management Ownership details to a centralized location (often referred to as a "Service Catalog" or an "Enterprise Service Catalog"), along with Ownership details for all other IT Disciplines, so that the entire enterprise has constant access to it.

Canonical Ownership of an Enterprise Capability

Figure: How Ownership of the Capability Data Mapping Management fits into the Canonical Model for IT

The above figure helps us understand how Capability or Discipline Ownership fits into the Canonical Model for Information Technology (IT) (i.e. "Think," "Deliver," and "Operate"). Owners are assigned to individual Disciplines or Capabilities, such as Data Mapping Management, and are instantly made accountable to the enterprise for the results of all Data Mapping Management Thinking activities (i.e. Strategy, Research, Planning and Design), all Data Mapping Management Delivery activities (i.e. Construction, Deployment and Quality Assurance), and all Data Mapping Management Operations activities (i.e. Use, Maintenance and Support). Done correctly, Data Mapping Management Ownership is constant and ongoing. It's important to understand that such assigned Ownership should "never" end so that there is clear and constant accountability and transparency for all aspects of the Canonical Model to the enterprise.

Not having clear Ownership for Data Mapping Management means that there is no clear understanding of who is accountable for it, who can provide understanding of what's going on within it, who can help the enterprise provide short term and long term descriptions of work being performed within the Discipline area to improve it over time for its customers, and who can help with getting work done that's associated with it. It means your or your enterprise's implementation for Data Mapping Management will be highly incomplete and erratic because no one is constantly (or even partially) watching over the Discipline and its needs for maintenance and evolution. Not having clear Data Mapping Management Ownership is a recipe for confusion and, sometimes, even chaos.

In summary, if you and your enterprise truly want to be successful with your implementation of Data Mapping Management, ensure that a clear and highly accountable owner is identified and assigned to the Discipline. Publish those ownership details, preferably in an enterprise's Service Catalog, and socialize it so everyone knows whom to go to for answers and for help with Data Mapping Management related work. In other words, if you want to implement Data Mapping Management as an enterprise Service, then you absolutely must start with clearly defined, published and socialized Ownership.


Verbs and Actions: Understanding Why Verbs and Actions are Important to Data Mapping Management

Throughout the Foundation's documentation, you will continuously run into the references of "Nouns and Verbs." These concepts are key to consistency and standardization, throughout the IT Industry, down to each and every IT Discipline. Given that we've discussed the impact of "Nouns" on the discipline of "Data Mapping Management," this section will start to discuss the importance of "Verbs" or "Actions" that can be performed with or against the key Noun or Nouns associated with this Discipline. To reiterate, Verbs or Actions allow us to clearly understand what can be performed on or with the Noun in question. As will be discussed in the next section, Verbs or Actions will also help us clearly identify whom it is (i.e. the "who" or more specifically the Roles) that performs or executes such Verbs or Actions against a Discipline and its associated Noun or Nouns. As will be discussed later, Verbs or Actions will also help identify key Attributes (i.e. Field Names) that are necessary for the very data definition of the Noun or Nouns for this Discipline and will even help identify which Verbs or Actions can be automated for this Discipline.

As a reminder, the base Noun for the discipline known as Data Mapping Management is: "Data Mapping," which is sometimes referred to as a the Noun: "Data Mapping Item."

By now, it should be becoming apparent that verbs represent a baseline for defining solid functional requirements and sub-capabilities for what would be a part of any good Data Mapping Management System or Service. What this means is that if you and/or your Organization is looking for a solution in this space (e.g. the purchasing or building of a software solution or the implementation of a Service to address the needs of Data Mapping Management), you could use discipline-related verbs to drive the foundation of what the solution should or shouldn't do, as mapped to specific stakeholders that will use or provide the solution.

Examples of the types of Verbs or Actions that are important to this Discipline include but are not limited to:

The above list represents a very small subset of all Verbs or Actions that are relevant for this Discipline. The more complete set can be found in the Roles section of this document, where readers can see the direct correlation of Verb to Noun and to, both, Generic Role and Discipline Specific Role.


Roles: Key Verb and Action Driven Roles For Data Mapping Management

An "action" or a "verb" is something that can be performed on or with a specific "noun." The reason it is important to itemize all relevant verbs is because we can now start to determine what we can or cannot do with the noun in question, where in this case the noun is "Data Mapping."

Actions/Verbs Example as Applied to "Data Mapping" Generic Roles Discipline-Specific Roles
Administrate Administrate Data Mapping Administrator Data Mapping Administrator
Approve Approve Data Mapping Approver Data Mapping Approver
Architect Architect Data Mapping Architector Data Mapping Architector
Archive Archive Data Mapping Archiver Data Mapping Archiver
Audit Audit Data Mapping Auditor Data Mapping Auditor
Bundle Bundle Data Mapping Bundler Data Mapping Bundler
Clone Clone Data Mapping Cloner Data Mapping Cloner
Code Code Data Mapping Coder Data Mapping Coder
Configure Configure Data Mapping Configurer Data Mapping Configurer
Copy Copy Data Mapping Copier Data Mapping Copier
Create Create Data Mapping Creator Data Mapping Creator
Decommission Decommission Data Mapping Decommissioner Data Mapping Decommissioner
Delete Delete Data Mapping Deletor Data Mapping Deletor
Deploy Deploy Data Mapping Deployer Data Mapping Deployer
Deprecate Deprecate Data Mapping Deprecator Data Mapping Deprecator
Design Design Data Mapping Designer Data Mapping Designer
Destroy Destroy Data Mapping Destroyer Data Mapping Destroyer
Develop Develop Data Mapping Developer Data Mapping Developer
Distribute Distribute Data Mapping Distributor Data Mapping Distributor
Download Download Data Mapping Downloader Data Mapping Downloader
Edit Edit Data Mapping Editor Data Mapping Editor
Educate Educate Data Mapping Educator Data Mapping Educator
Export Export Data Mapping Exporter Data Mapping Exporter
Govern Govern Data Mapping Governor Data Mapping Governor
Import Import Data Mapping Importer Data Mapping Importer
Initialize Initialize Data Mapping Initializer Data Mapping Initializer
Install Install Data Mapping Installer Data Mapping Installer
Instantiate Instantiate Data Mapping Instantiator Data Mapping Instantiator
Integrate Integrate Data Mapping Integrator Data Mapping Integrator
Manage Manage Data Mapping Manager Data Mapping Manager
Merge Merge Data Mapping Merger Data Mapping Merger
Modify Modify Data Mapping Modifier Data Mapping Modifier
Move Move Data Mapping Mover Data Mapping Mover
Own Own Data Mapping Owner Data Mapping Owner
Package Package Data Mapping Packager Data Mapping Packager
Persist Persist Data Mapping Persister Data Mapping Persister
Plan Plan Data Mapping Planner Data Mapping Planner
Purge Purge Data Mapping Purger Data Mapping Purger
Receive Receive Data Mapping Receiver Data Mapping Receiver
Record Record Data Mapping Recorder Data Mapping Recorder
Recover Recover Data Mapping Recoverer Data Mapping Recoverer
Register Register Data Mapping Registrar Data Mapping Registrar
Relocate Relocate Data Mapping Relocator Data Mapping Relocator
Reject Reject Data Mapping Rejecter Data Mapping Rejecter
Remove Remove Data Mapping Remover Data Mapping Remover
Replicate Replicate Data Mapping Replicator Data Mapping Replicator
Report Report Data Mapping Reporter Data Mapping Reporter
Request Request Data Mapping Requestor Data Mapping Requestor
Restore Restore Data Mapping Restorer Data Mapping Restorer
Review Review Data Mapping Reviewer Data Mapping Reviewer
Save Save Data Mapping Saver Data Mapping Saver
Search Search Data Mapping Searcher Data Mapping Searcher
Split Split Data Mapping Splitter Data Mapping Splitter
Sponsor Sponsor Data Mapping Sponsor Data Mapping Sponsor
Store Store Data Mapping Storer Data Mapping Storer
Strategize Strategize Data Mapping (or Set Data Mapping Strategy) Strategizer (or Strategy Setter) Data Mapping Strategizer (or Data Mapping Strategy Setter)
Support Support Data Mapping Supporter Data Mapping Supporter
Test Test Data Mapping Tester Data Mapping Tester
Train Train Data Mapping Trainer Data Mapping Trainer
Upgrade Upgrade Data Mapping Upgrader Data Mapping Upgrader
Upload Upload Data Mapping Uploader Data Mapping Uploader
Verify Verify Data Mapping Verifier Data Mapping Verifier
Version Version Data Mapping Versioner Data Mapping Versioner
View View Data Mapping Viewer Data Mapping Viewer

At a minimum, the above list of Verbs can be used to help identify, track, and manage the basic "Features" required by and associated with Data Mapping Management, even if your enterprise doesn't maintain a Capability Model that lists specific Data Mapping Management Capabilities. Application designers, developers, and architects often find such Verb Lists or Feature Inventories to be invaluable.


Taxonomy: Understanding Data Mapping Management Classifications or Categorizations

IF4IT Taxonomies

A Taxonomy, in its noun form, is defined as:

...a documented and orderly set of types, classifications, categorizations and/or principles that are often achieved through mechanisms including but not limited to naming, defining and/or the grouping of attributes, and which ultimately help to describe, differentiate, identify, arrange and provide contextual relationships between the entities for which the Taxonomy exists.

From this general definition, we can derive that the definition for a Data Mapping Management Taxonomy is:

...a documented and orderly set of types, classifications, categorizations and/or principles that are often achieved through mechanisms including but not limited to naming, defining and/or the grouping of attributes, and which ultimately help to describe, differentiate, identify, arrange and provide contextual relationships between Data Mapping Items, Entities or Types.

In short, what this means all means is that a Taxonomy is nothing more than a classification or typing mechanism and that a Data Mapping Taxonomy is nothing more than a classification or typing mechanism that helps people and systems distinguish between different Data Mapping Items, Entities, Types, Records or any other Data Mapping Management element you can think of.

It's important to understand that Taxonomies can be as simple as a list of relevant terms or phrases with respective meanings or definitions or they can take on more complex forms, such as hierarchical and graphical model structures that can be homogeneous and heterogeneous in nature. More complex Taxonomies include examples such as "Visual Taxonomies" and "Audible Taxonomies" but, expect in the case of very special technologies, are typically out of scope for general Information Technology (IT) Operations.

The Foundation directs readers to its ever-evolving Inventory of Taxonomies for Standard Taxonomy suggestions. Specifically, readers may want to start with the Taxonomy of Taxonomies, which helps make it clear that the IT Industry is composed of many hundreds if not thousands of Taxonomies, Classifications, Categorizations or Types.


Ontology: Data Mapping Management Ontology as a Means for Lanagugae Standardization

While Taxonomies represent organized classifications or types, you can think of Ontologies as the design and representation of entire lanaguages, with the specific intent to control things like structure, behavior, representation, and meaning. Without getting into a theoretical conversations about Ontologies, you can view this entire article as a foundation for the ontology of Data Mapping Management. Or, in other words, a Data Mapping Management Ontology.

Throughout this artifact/framework, you will find things like Data Mapping Management related terms, phrases, definitions, roles, responsibilities, nouns, verbs, classifications, and so on, all as a means of definining a standard representation for and interpretation of the language of Data Mapping Management.

It is only through the definition, communication, and establishment of such Ontologies that we can standardize language and communication associated with Data Mapping Management, whether it be between humans and/or systems.


Life Cycle (Lifecycle): Lifecycle Phases for Data Mapping Management

When we talk about Life Cycle (or lifecycle) for Data Mapping Management, it's important to keep in mind that there are two different types of Life Cycles that apply. The first is a Data Life Cycle, which addresses Data Mapping Management data or entities, and the second is associated with delivering Data Mapping Management Assets like Systems or Software solutions.

Data Mapping Management Data Life Cycle Phases:

Data Lifecycle (or Life Cycle) for any and all data is the period from the "inception" of data through to its ultimately being "purged" from existence. This is no different for Data Mapping Management related data.

Like the data associated with any other professional IT Discipline, Data Mapping Management related data adheres to the following common Data Lifecycle Phases:

Data Lifecycle Phases

Figure: Data Mapping Management Lifecycle Phases

  1. Inception: Data is in it's raw idea-like form and is not ready for consumption by the general population because it has not been documented or registered, anywhere, in a formal manner.
  2. Creation and Registration: Data is formally put into existence for day-to-day use by appropriate stakeholders.
  3. Iterative Maintenance: Data is in a mode of constant use and is updated and modified, as needed, to meet the needs of daily use by various stakeholders.
  4. Decommission and Deletion: Data is prepared for deletion and eventually deleted from daily operational use but still exists for administrative or organizational purposes, such as historical auditing. It can be restored to any one of its relevant last states and, therefore, can be brought back into existence for day-to-day use.
  5. Purged From Existence: Data is completely removed from an environment with no means to restore or reconstruct it, without recreating it from scratch and with no guarantees that it will match it's previous state.

The above Life Cycle Phases represent the high level transitions that occur from the inception of Data Mapping Items or Entities all the way through to their complete elimination from existence. A more detailed breakdown of these transitions or phases represents what are referred to as "Data Mapping Management States."

Data Mapping Management Systems Development Life Cycle (SDLC) Phases or Data Mapping Management Software Development Life Cycle (SDLC) Phases:

The SDLC is a means for facilitating and controlling how IT Professionals deliver Assets, such as Data Mapping Management Systems and Software. In this case, you should default to the master SDLC, which is used to deliver any Asset of any type, including those associated with the Data Mapping Management discipline.

Data Mapping Management SDLC Diagram

Inventories: Data Mapping Management Inventories

There are probably no greater or more important tools for providing Data Mapping Management transparency and direction than the collection, ordering, categorizing, grouping, and maintenance of all related Data Mapping Items. In other words, Data Mapping Management Inventories.

In short, an Inventory represents a list of individual things or instances of things that are typically all of the same Noun Type or Data Type, where these instances are described and detailed by their Attributes, along with the Data and Information that act as values for such Attributes.

At a minimum, Data Mapping Management Inventories are used for the establishment of solid Data Mapping Configuration Management practices, as the Data Mapping Instances tracked within such Data Mapping Inventories act as Configuration Items (in Target and/or Dependency form) for key Configurations (Data Mapping Management Configurations or otherwise).

Inventories are also used for solid decision making. Good decisions, either strategic or tactical, are made based on having good Data and Information. And, good Data and Information only come from taking the time to follow best practices associated with Inventory Management. It's only through building such Inventories that an enterprise can achieve solid Data Mapping Management Business Intelligence and Reporting.

Also, it's these very same Inventories that act as the foundation for understanding and managing Total Cost of Ownership (a.k.a. "TCO") for Data Mapping Management. Without such Inventories, trying to understand your costs can be nothing more than uneducated guessing.

The obvious place to start is with Data Mapping Inventories and then move on to surrounding Inventories that are directly and indirectly related to Data Mapping Management.

Additionally, there are many other types of Inventories that are common and important to Data Mapping Management, which include but are not limited to examples such as:

  1. People and Organizations related to Data Mapping Management
  2. Roles, Responsibilities, and Skills related to Data Mapping Management
  3. Products and Services related to Data Mapping Management
  4. Capabilities related to Data Mapping Management
  5. Contracts, Agreements, and Licenses related to Data Mapping Management
  6. Processes related to Data Mapping Management
  7. Tools and Technologies (e.g. Systems/Applications/Software/Computers) related to Data Mapping Management
  8. Data Types and Instances related to Data Mapping Management
  9. Data Interfaces related to Data Mapping Management
  10. Environments related to Data Mapping Management
  11. Facilities and Locations related to Data Mapping Management

If you and/or your enterprise are not collecting and maintaining such Inventories, you're probably considered to be very low on the efficiency and effectiveness maturity scale.

It's important to keep in mind that collecting and managing Data Mapping Management Inventories is something that should be performed across all phases of Data Mapping Management Lifecycle and across all Environments (i.e. Data Mapping Management Environments). Both are considered to be very important Best Practices. For example, you and/or your enterprise cannot get a complete understanding of Data Mapping Management costs or impacts without knowing all related Inventory Items in all environments. And, tracking across all lifecycle phases gives a temporal perspective that is important for things like problem analysis, historical reporting, and the reconstruction of state (i.e. Configuration Management).

NOTE: Data Mapping Management Inventories are also important for other enterprise functions, such as Architecture and Design. Such Inventories represent the foundation for understanding an enterprise's Current State and are critical for planning Future State and any related strategies, roadmaps, and transition plans for facilititating change.


Environments: Data Mapping Management Environments

Building environments that are specific to and for the discipline known as Data Mapping Management is no different than doing so for any other discipline area. The reader should, therefore, refer to the IT Environment Framework to understand such environments.

IT Environment Framework for Data Mapping Management

Metrics: Data Mapping Management Metrics

As with any professional Discipline, the place to start with when dealing with Data Mapping Management specific metrics is with standard metrics categorizations. Standard Metrics Categorizations, or what are commonly referred to as "SMCs," include but are not limited to...

Data Mapping Management Quantitative Metrics: Quantitative metrics for Data Mapping Management often revolve around the "counting" of key constructs that are associated with the Discipline. For example, the number of Data Mapping Items or Entities that have been Created, Edited or Modified, Copied or Cloned, Destroyed, Archived, Restored, etc. (Note the correlations to key Data Mapping Management Verbs!). Also, the counts for things like the number of Data Mapping Management Stakeholders, such as but not limited to Paying Customers, End Users, Employees, Consultants, etc. are also very useful.

Data Mapping Management Qualitative Metrics: Qualitative metrics for Data Mapping Management often revolve around concepts such as Data Mapping Management Defects, Failures, Problems, Incidents, and/or Issues. So, for example, if we were to capture the number of Data Mapping Management Defects (i.e. their counts) over time, we could do things like see if Defect quantities are going up or down, over time, allowing us to explore that area for things like correlating Causes and Effects.

Data Mapping Management Time Metrics: When dealing with Data Mapping Management Time Metrics, there are usually two forms. The first was introduced in the previous paragraph, which has to do with capturing and measuring things like Quantitative or Qualitative Metrics, over time. In this case, we capture other metric categories, over time, with the intent to see how they change and perform, based on modifications to the Data Mapping Management Operating Environment. The second form of Time related metrics has to do with system or operational performance, such as in the case of how long it takes to process a Data Mapping Management Request, from the time it is created to the time the Requester gets a satisfactory deliverable that allows him or her to move on with his or her work.

Data Mapping Management Utilization Metrics: Utilization Metrics specifically have to do with the consumption of Data Mapping Management specific solutions or deliverables. For example, tracking the number of Data Mapping Management Service Requests, over periods of time, along with their corresponding Data Mapping Management Deliverables, allows one to measure how active Data Mapping Management Services are against other Services that may exist within the Enterprise.

Data Mapping Management Financial Metrics: As is always the case for any single Discipline, Financial Metrics for Data Mapping Management always revolve around things like revenue, expenses, and profits, both, for operators of the Service or Services and for consumers of the Service or Services. For example, if a Data Mapping Management Request is invoked by a Data Mapping Management Customer (acting as the "Requester"), it becomes important to be able to identify and understand what the cost is to that Customer who is invoking the Request, and it also becomes important to understand why that cost is what it is. In the case of Services that do not yield revenue or profits, measuring costs is a strong way to, at very least, help understand the costs associated with each Service being performed by, within, external to, and for the Enterprise and its Customers.

Note: It's important to understand that, when it comes to metrics, enterprises should take a "Crawl," "Walk," "Run" approach to collecting, working with, and understanding them. That is, you cannot get to complex metrics collection, dissection, analysis, and understanding until you start with basic metrics and slowly work your way to more complex metrics representations.


Services: Data Mapping Management as a Set of Services (a.k.a. Data Mapping Management Services)

One of the most important concepts you will learn about Data Mapping Management (or any Discipline, for that matter) is the notion of implementing the Discipline as an accountable, planned, controlled, transparent, and managed "Service."

In short, Services represent a logically "bounded" and repeatable sets of work types, activities or tasks that are performed by humans and/or machines, with the specific intent to provide outputs or deliverables, in the form of solutions for the requesting Stakeholders who are commonly considered the customers of such Services. In other words, we perform and/or provide a Service to deliver very specific solutions to very specific Stakeholders who are looking for a means to solve a certain problem they have.

A Data Mapping Management Service is defined as:

"1. A set of solutions, either transactional (i.e. Transactional Data Mapping Management Services) or dial-tone (i.e. Dial-Tone Data Mapping Management Services), that are being or have been put in place to yield an intended, controlled, expected, repeatable and measurable set of results or deliverables for Data Mapping Management specific Customers, Consumers or Clients.

NOTE: Data Mapping Management Service Consumers or Clients can be either Human Resources or Systems."

All Services, including Data Mapping Management Services, can be performed manually (i.e. by people), automatically (i.e. by machines such as Computers), or by a combination of the two (i.e. a hybrid that is both manually and automated).

Also, all Services, including Data Mapping Management Services, can be either transactional or dial tone, in nature.

In the case of Transactional Services for Data Mapping Management, a Service Request is submitted and that Request is fulfilled as part of a process that is either manual, automated, or a hybrid of both (e.g. a Service to perform maintainance on your Data Mapping Management System).

In the case of Dial Tone Services for Data Mapping Management, a Service is expected to be up, running, available, and accessible to an End User so that he/she/it may perform some controlled and highly repeatable function (e.g. a "Data Mapping Management System" that is up and running all the time).

Data Mapping Management Service Components: The successful implementation of Data Mapping Management as a set of Services for your enterprise usually implies that a number of key components have been established to support it. These components are:

  1. A clearly documented and socialized Data Mapping Management Service Owner that is held accountable for Service performance, quality, and cost.
  2. A clearly documented and socialized Data Mapping Management Service Provider, Organization or Group who is performing the Service or work.
  3. A clearly documented and socialized inventory of all Data Mapping Management Service Inputs, including Data Mapping Management Service Requests and any artifacts necessary to support such Requests so that consumers of the Service know how to engage and request or take advantage of them.
  4. For every Data Mapping Management Service Input, a clearly documented and socialized inventory of Data Mapping Management Service Outputs, making it clear to consumers what they can expect to receive as a result of a successful Service Request.
  5. For every Data Mapping Management Service Input, a clearly documented and socialized inventory of the work being performed by the Service Provider to achieve such Outputs or Deliverables.
  6. For every Data Mapping Management Service Input, a clearly documented and socialized inventory Service Level Agreements (e.g. Service Availability, Service Duration, Service Guarantees, etc.) that can be used to set expectations and measure actuals against for said Service Outputs.
  7. Clearly specified Data Mapping Management Service Costs that help set expectations for Service Requesters (i.e. the cost of a request) and that provide clear transparency to the organizations that fund and sponsor such Services (i.e. the Total Cost of Ownership (TCO) your Service(s).
  8. Data Mapping Management Service Request Patterns (Estimation Creation, Modification, Decommission, Support/Incidents, Complaints, etc.) in order to create intuitive and repeatable user experiences across different Service Types.
  9. Clearly understand what Data Mapping Management Service Resources are required, human or otherwise, to create and deliver your Data Mapping Management Service Deliverables, in a repeatable, cost-efficient, timely, and high quality manner.
  10. For every Data Mapping Management Service Request, understand the chargeback mechanism, in order to recoup your Service Costs.
  11. For every Data Mapping Management Service, it's important to understand the skills that are required, will need to be developed, and will need to be maintained by Service Resources, in order to deliver each Service Deliverable.
  12. It's important to understand who your Data Mapping Management Service Stakeholders are, this includes but is not limited to your Customers, Consumers, Clients, Sponsers, etc. are, as well as the types of problems it is that they're trying to solve or interests that they will have in your Services.

Data Mapping Management Ownership: The most important thing to understand about a Data Mapping Management Service is that, in order for such a Service to be successful, there must be a clear and accountable Owner for it. That is, there needs to be a very clear and accountable named person or organization that owns and is fully responsible for the Service, all of its sub-Services and, most importantly, all of the Service's "Outcomes." Without clear ownership, Services are almost never successful. And, for those few occasions where Services are successful without clear ownership, you can assume that they're successful because the people working in those Service areas are acting as heroes, or... the those Services are just plain lucky (that kind of luck doesn't last for long).

Data Mapping Management Service Inputs: There are typically two types of inputs to any Data Mapping Management Service. The first is what is known as a "Data Mapping Management Service Request" and the second really represents any and all supporting artifacts that are necessary to support such requests, including but not limited to Data and Information in the form of Documents, either electronic or paper in form. Many would argue that the "money" to pay for the Service execution of the Request would be the third but, for now, we will assume that payment is controlled through the Data and Information provided to the Service Operators, in support of the Request.

Data Mapping Management Service Outputs: The outputs of any Service are often referred to as the Service's Deliverables. Therefore, the readers should be aware that the terms "Data Mapping Management Outputs" and "Data Mapping Management Deliverables" are synonymous and interchangeable. All work performed in any enterprise is, by default, a Service that is being performed for someone else and, therefore, all work or Services yield results. These results are the Service's Outputs or Deliverables and a good Service ensures that such Outputs are appropriately documented to the consumers of said Service. This means that for any given Data Mapping Management Service Request Type or Category there will be one or more clearly defined and documented Outputs or Deliverables, making it clear to the consumer what he, she, or they will get in response to their Request. This can be as simple as an answer to a question or as complex as the Merger of two enterprises.

Data Mapping Management Service Levels: Service Levels represent "performance agreements," contractual or otherwise, that dictate how well a Data Mapping Management Service should perform, most often keeping the Customers, Consumers, Clients or End Users of the Service in mind. Data Mapping Management Service Levels can come in many forms and are often worked out by the Customers paying for the Services and the Service Providers who sell or provide the Services. In many cases, Service Levels are also self-imposed by the Service Providers performing the Services as a means to set expectations for Service Customers. In short, Data Mapping Management Service Levels are constraints, limitations, and/or expectations that are tied directly to Data Mapping Management Service Deliverables. They represent measures for things like quality, efficiency, and cost against said Deliverables or Outputs that allow the consumer of such Services to measure what they actually get against what they expected to get.


Service Paradigms: Centralized Data Mapping Management vs. Federated Data Mapping Management

Assuming an enterprise pursues the establishment of Data Mapping Management as a set of controlled Services, there are three common paradigms for doing so. These include:

  1. A "Centralized Data Mapping Management" implementation paradigm
  2. A "Federated Data Mapping Management" implementation paradigm
  3. A "Hybrid Data Mapping Management" implementation paradigm

Centralized Data Mapping Management is defined as:

"1. The term or phrase that implies establishing and/or practicing the Discipline known as Data Mapping Management as a concentric and singular set of organizations and services, usually in order to serve an entire enterprise, regardless of geographic location, further implying full centralization and no federation of any and all Data Mapping Management associated Work, Activities, Actions, Tasks, Capabilities and/or Services."

Federated Data Mapping Management, which is also referred to as Decentralized Data Mapping Management, is defined as:

"1. The term or phrase that implies establishing and/or practicing the Discipline known as Data Mapping Management in multiple pockets, communities, or organizations, further implying no centralization in the implementation and execution of Data Mapping Management associated Work, Activities, Actions, Tasks, Capabilities and/or Services."

There are clear tradeoffs to each of the two models. For example, in a Centralized paradigm, it's normally easier to coordinate work and provide broad coverage, across many areas of the enterprise and relevant stakeholders. However, it becomes far more difficult for a centralized organization to properly fund and staff resources and services in order to perform all required work across all stakeholders, in a much larger enterprise.

It's also important to note that a third paradigm also exists as an option. This is known as a Hybrid Data Mapping Management paradigm or model. In this case, there is a centralized Data Mapping Management organization that is often responsible for things like centralized governance, command, control, and communications, while federated staff and services deal with localized forms of Data Mapping Management. In this type of paradigm, federated staff and services usually report direclty into their local management but may have matrix reporting or responsibilities into the Centralized Data Mapping Management organization.


Principles & Best Practices: Common Principles and Best Practices for Data Mapping Management

A "Principle" is defined as being: "A professed assumption, basis, tenet, doctrine, plan of action or code of conduct for activities, work or behavior." Therefore, we can deduce the definition of "a Data Mapping Management Principle" to be:

Data Mapping Management Principle: "1. A professed assumption, basis, tenet, doctrine, plan of action or code of conduct for any activities, work or behavior associated with the Discipline known as Data Mapping Management."

A "Best Practice" is defined as being: "One or more Activities, Actions, Tasks or Functions that often do not conform with strict Standards and that have evolved, over time, to be considered as conventional wisdom for consistently and repeated achieving Outcomes or Results that can be measured as being equal to or above acceptable norms." Therefore, we can deduce the definition of "a Data Mapping Management Best Practice" to be:

Data Mapping Management Best Practice: "1. One or more Data Mapping Management related Activities, Actions, Tasks or Functions that often do not conform with strict standards and that have evolved, over time, to be considered as conventional wisdom for consistently and repeatedly achieving Outcomes or Results that can be measured as being equal to or above acceptable norms."

The plural form of this term would be "Data Mapping Management Best Practices."

Common Data Mapping Management related principles and best practices exist to help achieve higher than average expectations of quality and to ease in the implementation, support, operations, and future change associated with the solutions industry professionals put in place to address the needs of this Discipline and all its related stakeholders.

While this entire document is meant to represent and serve as a set of common principles and best practices for Data Mapping Management, the following list represents a summary of some very basic examples of what implementers, supporters, and operators of Data Mapping Management should constantly be working toward:

Principle or Best Practice Description
Establish and always have very clear Ownership for Data Mapping Management. Establishing, publishing and socializing clear Ownership for Data Mapping Management allows an enterprise and all its Resources, regardless of their geographic location, to assign accountability for all aspects of the Discipline. It also ensures that there's always at least one person that everyone can go to for transparency into the Discipline as well as for handling work that is associated with the Discipline.
Define, Collect, and Manage Relevant Data Mapping Management Inventories. As an IT professional, there are probably few things that are as important as knowing what is or is not in your portfolio, as well as understanding key traits about your portfolio. You cannot achieve this without the transparency provided by your inventories. Therefore, it is critical that you clearly define, collect, manage, and govern any and all relevant Data Mapping Management inventories. Lack of Data Mapping Management Inventories means no transparency, a chaotic and immature environment, and (even worse) the implication that you don't know how to do your job.
Always use standard terminology for Data Mapping Management, in order to standardize communications between stakeholders. It is often argued that the biggest mistake you can make is to create your own words and/or your own definitions, when communicating with others. There is no place where this is more accurate than in the field of Information Technology. IT Stakeholders make up their own words and definitions far too often, or let their business constituents do so. When you make up words or definitions, or you let others do so, you're creating a grave injustice for your organization. Self invented terminology and grammar often leads to poor communications, which in turn leads to redundancy of solutions, higher complexity of environments, slower delivery times, and much higher costs. Therefore, the IF4IT always recommends that you leverage standard terminology for Data Mapping Management, whenever possible.
Centralization of Data Mapping related data. While often impossible to centralize and collocate all Data Mapping related data and information, especially in a geographically dispersed environment, Data Mapping Management related stakeholders should always strive to centralize all data and information. The goals are to eliminate data fragmentation, improve source of truth for data, reduce the number of systems needed to support stakeholders, reduce the complexity of solutions, improve usability, and to ultimately reduce the costs associated with Data Mapping Management.
Clearly define, implement, track, and analyze Data Mapping Management Metrics. In order to successfully set up the discipline of Data Mapping Management and its related Services, it is critical to clearly define, track, and constantly analyze Data Mapping Management metrics. Such metrics include but are not limited to Supply and Demand Metrics (i.e. Operational Metrics), Performance Metrics, Quality Metrics, and Financial Metrics.
Transparency of Data Mapping related data. Stakeholders should always strive to make any and all Data Mapping Management data transparent to all other appropriate stakeholders, at a minimum, and often to the entire enterprises. The exception when private user data must be protected. Many stakeholders often make the mistake of treating internal operational data as private or protected. This often creates a data silo and will often lead to internally silo-ed organizations that revolve around such data silos.
Do not let "perfection" of Data Mapping Management solutions stand in the way of "good enough solutions". Often, Data Mapping Management stakeholders "overthink" solutions, leading to the impression that best-of-breed or perfect solutions are more effective than "good enough" solutions. Experience tells us that "good enough" is, almost always, the better path to follow. We live in an age where technologies grow old in the blink of an eye. Even the implementation of something that looks perfect, today, will look antiquated, tomorrow. This is especially true if your enterprise doesn't have a long term funding plan and commitment to improvements and upgrades of the solution(s) put in place.
Follow industry Standards, Best Practices, and Guiding Principles for Data Mapping Management, whenever possible". One of the most common errors many enterprises make is to create solutions from scratch or without the guidance, assistance and/or experience of others who have created such solutions, before them. Whenever possible, the IF4IT recommends that you research existing Standards, Best Practices, and Guiding Principles to avoid the mistakes of others, while also gaining from their successes. Remember, we live in a vast world. Chances are very high that someone else has already experienced the pain you're about to create for yourself. Wise people will always look to learn from such people's experiences before they go down the road of implementing their own solutions.
Work toward and maintain a Single Source of Truth (SSoT), whenever possible. While it may be impossible to truly maintain a Single Source of Truth (SSoT) for all data items at all times, especially in the case where the same data entity or instance enters an enterprise through unique data channels, it is an accepted, industry-wide best practice to always work toward such a goal.

Further Reading and Reference Material for Data Mapping Management

The Information Technology (IT) Learning Framework. A tutorial that helps understand Information Technology and how disciplines, such as this one, fits into the bigger picture of IT Operations.

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