Author: Bunny Tharpe

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Enterprise Architecture erwin Expert Blog

Enterprise Architecture vs. Data Architecture

Although there is some crossover, there are stark differences between data architecture and enterprise architecture (EA). That’s because data architecture is actually an offshoot of enterprise architecture.

In this post:

See also: The Difference Between Enterprise Architecture and Solutions Architecture 

The Difference Between Data Architecture and Enterprise Architecture

In simple terms, EA provides a holistic, enterprise wide overview of an organization’s assets and processes, whereas data architecture gets into the nitty gritty.

The difference between data architecture and enterprise architecture can be represented with the Zachman Framework. The Zachman Framework is an enterprise architecture framework that provides a formalized view of an enterprise across two dimensions.

Data architecture and Enterprise Architecture - The Zachman Framework

The first deals with interrogatives (who, when, why, what, and how – columns). The second deals with reification (the transformation of an abstract idea into concrete implementation – rows/levels).

We can abstract the interrogatives from the columns, into data, process, network, people, timing and motivation perspectives.

So, in terms of the Zachman Framework, the role of an enterprise architect spans the full schema.

Whereas a data architect’s scope is mostly limited to the “What”(data) and from a system model/logical (level 3) perspective.

The Value of Data Architecture

We’re working in a fast-paced digital economy in which data is extremely valuable. Those that can mine it and extract value from it will be successful, from local organizations to international governments. Without it, progress will halt.

Good data leads to better understanding and ultimately better decision-making. Those organizations that can find ways to extract data and use it to their advantage will be successful.

However, we really need to understand what data we have, what it means, and where it is located. Without this understanding, data can proliferate and become more of a risk to the business than a benefit.

Data architecture is an important discipline for understanding data and includes data, technology and infrastructure design.

Data Architecture and Data Modeling

Data modeling is a key facet of data architecture and is the process of creating a formal model that represents the information used by the organization and its systems.

It helps you understand data assets visually and provides a formal practice to discover, analyze and communicate the assets within an organization.

There are various techniques and sets of terminology involved in data modeling. These include conceptual, logical, physical, hierarchical, knowledge graphs, ontologies, taxonomies, semantic models and many more.

Data modeling has gone through four basic growth periods:

Early data modeling, 1960s-early 2000s.

With the advent of the first pure commercial database systems, both General Electric and IBM came up with graph forms to represent and communicate the intent of their own databases. The evolution of programming languages had a strong influence on the modeling techniques and semantics.

Relational data modeling, 1970s.
Edgar F. Codd published ideas he’d developed in the late 1960s and offered an innovative way of representing a database using tables, columns and relations. The relations were accessible by a language. Much higher productivity was achieved, and IBM released SQL (structured query language).

Relational model adoption, 1980s. The relational model became very popular, supported by vendors such as IBM, Oracle and Microsoft. Most industries adopted the relational database systems and they became part of the fabric of every industry.

Growth of non-relational models, 2008-present. With increasing data volumes and digitization becoming the norm, organizations needed to store vast quantities of data regardless of format. The birth of NoSQL databases provided the ability to store data that is often non-relational, doesn’t require rigor or schema and is extremely portable. NoSQL databases are well- suited for handling big data.

Data modeling is therefore more necessary than ever before when dealing with non-relational, portable data because we need to know what data we have, where it is, and which systems use it.

The Imperative for Data Architecture and Enterprise Architecture

The location and usage of data are key facets of EA. Without the context of locations, people, applications and technology, data has no true meaning.

For example, an “order” could be viewed one way by the sales department and another way to the accounting department. We have to know if we are dealing with a sales order from an external customer or an order placed by our organization to the supply chain for raw goods and materials.

Enterprise architecture tools can be leveraged to manage such processes.

Organizations using enterprise architecture tools such as erwin Evolve can  synergize EA with wider data governance and management efforts. That means a clear and full picture of the whole data lifecycle in context, so that the intersections between data and the organization’s assets is clear.

You can even try erwin Evolve for yourself and keep any content you produce should you decide to buy.

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Enterprise Architecture erwin Expert Blog

What Is an Enterprise Architecture Roadmap?

Having an enterprise architecture roadmap is essential in modern business. Without it, understanding the current and desired future state can be difficult.

An enterprise architecture roadmap does not have to be in contrast with efforts to promote an agile enterprise architecture. The focus of innovation and agile EA is to increase the agility of the business for digital transformation.

So it’s essential that an organization understands where it will be at any given period of time, so it’s better prepared to deal with disruption.

To keep pace with the speed of innovation and time to market, organizations need the ability to change quickly – and enterprise architecture roadmaps are a critical tool to view how complex or what the impact of the change is or will be.

Roadmaps in Enterprise Architecture

The idea of a roadmap isn’t exclusive to EA, and enterprise architects are far from the first to adopt them.

That said, the nature of roadmaps significantly compliment the way we articulate an organization’s EA. That’s because EA concepts provide a blueprint of the organization, and many aspects of these concepts can be described with a time dimension.

The time dimension can be used to either display a milestone date at which something is expected to happen, or a date range within which something will take place.

Roadmaps as “Views”

In EA, “views” refer to the different ways to represent an enterprise architecture, while keeping a consistent underlying model – similar to how one might represent the data from an excel table using a pie chart, bar chart or line graph.

The representations can offer different perspectives and/or insight that different parties may find of interest.

This enables enterprise architects to represent the information related to the enterprise architecture, according to stakeholder needs.

So just like a diagram is one view of an architecture model, so is a roadmap – offering a time-based perspective.

A roadmap is usually defined as a view for a specific time period (e.g., one year or the next three months).

Roadmaps may be dynamic and reflect the state of the concept at any moment in time in real-time, or they may be static and show how a set of concepts looked at any moment in time.

Many concepts can have multiple time attributes that represent different time properties.

In enterprise architecture, an application component may have a set of lifecycle times that are associated with it such as ’live’ or ‘sunset.’

Time attributes may simply be a single date such as a milestone or be a time period between two dates.

A roadmap view can consist of lanes. The lanes will show any theme or category for a set of concepts. A roadmap may be divided up to show different types of concepts on one roadmap.

For example, it may be useful to show work package duration and the anticipated idea implementation dates so we can see if our plans are on track.

Time usually flows from left to right on a roadmap diagram.

Example of an Enterprise Architecture Roadmap

The image below is an example showing different time properties for application components.

Enterprise Architecture Roadmap Views

As we can see, we have two lanes, live and sunset. These are themes that we may well be interested in.

We are showing on a single roadmap view both application components (CRM, SafeLogistics, SurveyTool) and a business capability (IT Offshoring).

We can show application components with the live date attribute in the live lane.

We can also view the business capability but with a sunset time period. The time period is between two dates.

In this example, we can see how a roadmap can be used to demonstrate date ranges.

The roadmap is an indication that it takes a much longer time to phase out a business capability. The time it takes to phase out a business capability is important to understand for a number of reasons.

For example, it might be important to know which resources and how many (if any) will be tied up during the process. What has to happen to the current enterprise architecture in order for said capability to be phased out efficiently?

“What-If” and Future Scenarios

Roadmaps provide a time-based view of a model. A time-based view of your concepts is essential for ‘what if’ analysis and planning future scenarios.

In different scenarios, the same set of concepts may have a different time visualization based on different time attributes.

Many organizations will have the concept of a lifecycle. It’s important for companies to adopt a set of lifecycle states that have the same meaning across their stakeholders. For example, sunset or end of life but not both.

As roadmaps are always subject to change and are extremely volatile, then roadmap views should be generated automatically from the model. There should be little reason to create roadmaps without a model. They become extremely difficult to maintain and view in different ways later on.

Organizations using erwin Evolve can take advantage of enterprise architecture roadmaps and views in a collaborative enabling, user friendly enterprise architecture tool.

You can even try erwin Evolve for yourself and keep any content you produce should you decide to buy.

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erwin Expert Blog Enterprise Architecture

What Is Agile Enterprise Architecture?

Having an agile enterprise architecture (EA) is the difference between whether an organization flourishes or flounders in an increasingly changing business climate.

Over the years, EA has gotten a bad reputation for not providing business value. However, enterprise architecture frameworks and languages like TOGAF and ArchiMate aren’t responsible for this perception. In fact, these standards provide a mechanism for communication and delivery, but the way enterprise architects historically have used them has caused issues.

Today, organizations need to embrace enterprise architecture – and enterprise architecture tools – because of the value it does provide. How else can they respond to business and IT needs or manage change without first documenting what they have, want and need?

Because that’s exactly what EA addresses. It provides business and IT alignment by mapping applications, technologies and data to the value streams and business functions they support.

Essentially, it’s a holistic, top-down view of an organization and its assets that can be used to better inform strategic planning.

But what is an agile enterprise architecture, and what are its advantages?

The Need for Agile Enterprise Architecture

The old adage that anything of any complexity needs to be modeled before it can be changed definitely holds true.

The issue is that enterprise architects tend to model everything down to an excruciating level of detail, often getting lost in the weeds and rarely surfacing for air to see what the rest of the business is doing and realizing what it needs.

This often makes communicating an organization’s enterprise architecture more difficult, adding to the perception of enterprise architects working in an ivory tower.

Just-in-Time vs Just-Enough Enterprise Architecture

Just in time, just enough and agile development and delivery are phrases we’ve all heard. But how do they pertain to EA?

Just-in-time enterprise architecture

Agile is based on the concept of “just in time.” You can see this in many of the agile practices, especially in DevOps. User stories are created when they are needed and not before, and releases happen when there is appropriate value in releasing, not before and not after. Additionally, each iteration has a commitment that is met on time by the EA team.

Just-enough enterprise architecture

EA is missing the answer to the question of “what exactly is getting delivered?” This is where we introduce the phrase “just enough, just in time” because stakeholders don’t just simply want it in time, they also want just enough of it — regardless of what it is.

This is especially important when communicating with non-EA professionals. In the past, enterprise architects have focused on delivering all of the EA assets to stakeholders and demonstrating the technical wizardry required to build the actual architecture.

Agile Enterprise Architecture Best Practices and Techniques

The following techniques and methods can help you provide just-enough EA:

Campaigns

Create a marketing-style campaign to focus on EA initiatives, gathering and describing only what is required to satisfy the goal of the campaign.

Models

At the start of the project, it doesn’t make sense to build a fancy EA that is going to change anyway. Teams should strive to build just enough architecture to support the campaigns in the pipeline.

Collaboration

Agile teams certainly have high levels of collaboration, and that’s because that level is just enough to help them be successful.

In light of the global pandemic, such collaboration might be more difficult to achieve. But organizations can take advantage of collaborative enterprise architecture tools that support remote working.

Planning

In iteration planning, we don’t look at things outside the iteration. We do just enough planning to make sure we can accomplish our goal for the iteration. Work packages and tasks play a large role in both planning and collaboration.

Agile Enterprise Architecture to Keep Pace with Change

As one of the top job roles in 2020, it’s clear organizations recognize the need for enterprise architects in keeping pace with change.

In modern business, what’s also clear is that maximizing the role’s potential requires an agile approach, or else organizations could fall into the same ivory-tower trappings burdening the discipline in the past.

Organizations can use erwin Evolve to tame complexity, manage change and increase operational efficiency. Its many benefits include:

  • Agility & Efficiency: Achieve faster time to actionable insights and value with integrated views across initiatives to understand and focus on business outcomes.
  • Lower Risks & Costs: Improve performance and profitability with harmonized, optimized and visible processes to enhance training and lower IT costs.
  • Creation & Visualization of Complex Models: Harmonize EA/BP modelling capabilities for greater visibility, control and intelligence in managing any use case.
  • Powerful Analysis: Quickly and easily explore model elements, links and dependencies, plus identify and understand the impact of changes.
  • Documentation & Knowledge Retention: Capture, document and publish information for key business functions to increase employee education and awareness and maintain institutional knowledge, including standard operating procedures.
  • Democratization & Decision-Making: Break down organizational silos and facilitate enterprise collaboration among those both in IT and business roles for more informed decisions that drive successful outcomes.

You can try erwin Evolve for yourself and keep any content you produce should you decide to buy.

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Enterprise Architecture Tools – Getting Started

Many organizations start an enterprise architecture practice without a specialized enterprise architecture tool.

Instead, they rely on a blend of spreadsheets, Visio diagrams, PowerPoint files and the like.

Under normal circumstances, this approach is difficult. In times of rapid change or crisis, it isn’t viable.

Four Compelling Reasons for An Enterprise Architecture Tool

Enterprise architecture (EA) provides comprehensive documentation of systems, applications, people and processes.

Prior research we conducted reveals four key drivers in the decision to adopt a dedicated enterprise architecture tool:

1) Delay Increases Difficulty.

The use of Visio, MS Office files and even with a framework like ArchiMate is a recipe for anarchy. By getting into an enterprise architecture tool early, you minimize the hurdle of moving a lot of unstructured files and disconnected diagrams to a new repository.

Rather than procrastinate in adopting an enterprise architecture tool, choose a reliable, scalable one now to eliminate the administrative hassle of keeping up with disconnected data and diagrams.

2) Are We Too Dependent on Individuals and Keeping Their Files?

Some EA practices collapse when key people change roles or leave the organization. Who last updated our PPT
for capability X? Where is the previous version of this Visio diagram?

Why does this application have three names, depending on where I look? Are we following the same model and framework, or is each team member re-inventing the wheel? Is there an easier way to collaborate?

If any of these questions sound familiar, an enterprise architecture tool is the answer. With it, your EA practice will be able to survive inevitable staffing changes and you won’t be dependendent on an individual who might become a bottleneck or a risk. You also can eliminate the scramble to keep files and tasks lists in sync.

Enterprise architecture tool

3) File-Based EA Is Not Mature, Sustainable or Scalable.

With a tool that can be updated and changed easily, you can effortlessly scale your EA activities by adding new fields, using new diagrams, etc.

For example, you could decide to slowly start using more and more of a standard enterprise architecture framework by activating different aspects of the tool over time – something incredibly difficult to do with mismatched files.

Stop running next to the bike. Get on it instead.

4) Do I Want to Be the EA Librarian or a Well-Regarded Expert?

EA experts are valuable, so their time shouldn’t be spent correcting data errors in spreadsheets, generating PowerPoint files, or manually syncing up your latest Visio file with yet another spreadsheet.

Enterprise architects should be free to focus on revealing hidden relationships, redundancies and impact analyses. In addition, they need to be able to spot opportunities, presenting roadmaps and advising management about ways to manage innovation.

With an actual enterprise architecture tool, all relevant artifacts and supporting data are accessible in a central repository. And you know what was updated and when. Generate reports on the fly in minutes, not hours or days. Combine information from Kanbans, pivot tables, diagrams and roadmaps, adding your comments and circulating to others for their input.

The Increasing Importance of Collaborative Enterprise Architecture

In addition to its traditional role of IT governance, EA has become increasingly relevant to the wider business. In fact, Gartner says EA is becoming a “form of internal management consulting” because it provides relevant, timely insights management needs to make decisions.

While basic visualization tools and spreadsheets can and have been used, they are limited.

Generic solutions require makeshift collaborative efforts, like sharing PDF files and notes via email. When working remotely, this approach causes significant bottlenecks.

Even before the Covid-19 crisis, this sort of collaboration was becoming more difficult, as an increasing number of organizations become decentralized.

So the collaboration required to methodically and continuously measure and maintain models, frameworks and concepts as they evolve, was hindered.

That’s why enterprise architecture management is more strategic and impactful when powered by technology to centrally document and visualize EA artifacts for better decision-making, which is crucial right now.

erwin Evolve is purpose-built for strategic planning, what-if scenarios, and as-is/to-be modeling and its associated impacts.

Collaboration features are built into the tool enabling IT and business stakeholders to create, edit and collaborate on diagrams through a user-friendly interface.

With erwin Evolve, organizations can encourage the wider business to easily participate in EA/BP modeling, planning, design and deployment for a more complete perspective.

It also provides a central repository of key processes, the systems that support them, and the business continuity plans for every working environment so employees have access to the knowledge they need to operate in a clear and defined way under normal circumstances or times of crisis.

You can try erwin Evolve for yourself and keep any content you produce should you decide to buy.

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Types of Enterprise Architecture Frameworks: ArchiMate, TOGAF, DoDAF and more

In enterprise architecture, there are a number of different types of enterprise architecture frameworks, tailored to meet specific business and/or industry needs.

What is an Enterprise Architecture Framework?

An enterprise architecture framework is a standardized methodology that organizations use to create, describe and change their enterprise architectures.

Enterprise architecture (EA) itself describes the blueprint and structure of an organization’s systems and assets. It’s needed to make informed changes that help bridge the gap between the enterprise architecture’s current and desired future state.

Just like any building or infrastructure project, EA has different stakeholders and plan views.

You wouldn’t build a house without understanding the building’s architecture, plumbing, electrical and ground plans all within the context of each other.

So enterprise architecture provides the plans for different views of the enterprise, and EA frameworks describe the standard views an organization can expect to see.

What Makes Up An Enterprise Architecture Framework?

The EA discipline views an organization as having complex and intertwined systems. Effective management of such complexity and scale requires tools and approaches that architects can use.

An enterprise architecture framework provides the tools and approaches to abstract this information to a level of detail that is manageable. It helps bring enterprise design tasks into focus and produces valuable architecture documentation.

The components of an enterprise architecture framework provide structured guidance for four main areas:

1. Architecture description – How to document the enterprise as a system from different viewpoints

Each view describes one domain of the architecture; it includes those meta-types and associations that address particular concerns of interest to particular stakeholders; it may take the form of a list, a table, a chart, a diagram or a higher level composite of such.

2. Architecture notation – How to visualize the enterprise in a standard manner

Each view can be represented by a standard depiction that is understandable and communicable to all stakeholders. One such notation is ArchiMate from The Open Group.

3. Design method – The processes that architects follow

Usually, an overarching enterprise architecture process, composed of phases, breaks into lower-level processes composed of finer grained activities.

A process is defined by its objectives, inputs, phases (steps or activities) and outputs. Approaches, techniques, tools, principles, rules and practices may support it. Agile architecture is one set of supporting techniques.

4. Team organization – The guidance on the team structure, governance, skills, experience and training needed

Kanban boards and agile architecture can help provide team structure, governance and best practices.

Types of Enterprise Architecture Frameworks

There are a number of different types of enterprise architecture frameworks. Here are some of the most popular:

ArchiMate

An Open Group architecture framework this is widely used and includes a notation for visualizing architecture. It may be used in conjunction with TOGAF.

TOGAF

The Open Group Architecture Framework that is widely used and includes an architectural development method and standards for describing various types of architecture.

DODAF

The Department of Defense Architecture Framework that is the standard for defense architectures in the United States.

MODAF

The Ministry of Defense Architecture Framework that is the standard for defense architectures in the United Kingdom.

NAF

The NATO Architecture Framework that is the standard adopted by NATO allies.

FEAF

A Federal Enterprise Architecture Framework issued by the U.S. CIO Council. FEA, the Federal Enterprise Architecture, provides guidance on categorizing and grouping IT investments as issued by the U.S. Office of Management and Budget.

Zachman Framework

A classification scheme for EA artifacts launched in the early 1980s by John Zachman, who is considered the father of EA.

TM FORUM

Telemanagement Forum is the standard reference mode for telecommunication companies.

Enterprise architecutre frameworks: The Zachman Framework

What’s the Best Enterprise Architecture Framework?

Although this might be somewhat of a non-answer, it’s the only one that rings true: the best enterprise architecture framework is the one that’s most relevant to your organization, and what you’re trying to achieve.

Each different type of enterprise architecture framework has its particular benefits and focus. For example, there are types of enterprise architecture frameworks best suited for organizations concerned with defense.

Having a good understanding of what the different types of EA framework are, can help an organization better understand better understand which EA framework to apply.

Ultimately, organizations will benefit most, from an enterprise architecture management system (EAMS) that supports multiple EA frameworks. This way, the most relevant enterprise architecture framework is always available.

How to Implement an Enterprise Architecture Framework

So you’ve established you need an enterprise architecture framework and assessed the different types of enterprise architecture frameworks, but how should you go about implementing and managing your chosen framework?

The answer? Using an enterprise architecture management suite (EAMS).

An EAMS is used to facilitate the management of an organization’s EA. It adds uniformity and structure, whereas many organizations had previously taken an ad-hoc approach.

And enterprise architecture tools are becoming increasingly important.

Thanks to the rate of digital transformation and the increasing abundance of data organizations have to manage, organizations need more mature, formal approaches to enterprise architecture.

Organization’s seeking to introduce an EAMS, should evaluate which frameworks the technology supports.

With erwin Evolve, users can expect a wide range of support for different types of enterprise architecture frameworks among other benefits, such as:

  • Remote collaboration
  • High-performance, scalable and centralized repository
  • Ability to harmonize EA and business process use cases, with a robust, flexible and Web-based modeling and diagramming interface

erwin Evolve was included in Forrester’s “Now Tech: Enterprise Architecture Management Suites for Q1 2020” report.

To understand why erwin excels in the large vendor category, you can see for yourself by starting a free trial of erwin’s Enterprise Architecture & Business Process Modeling Software.

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Change Management: Enterprise Architecture for Managing Change

As organization’s technologies and digital strategies mature, enterprise architecture for change management is becoming increasingly relevant. 

Enterprise architecture’s holistic view of the organization is perfect for understanding how an organization’s assets are interconnected. 

This understanding is crucial when an organization is looking to change from within. But it is perhaps even more crucial still, when external factors and disruption force an organization into change.

Ch-ch-ch-ch-changes …

Organizations in every industry are navigating digital transformation, so change management is an important element to consider as part of those efforts.

And organizations that embrace change often achieve greater success.

Whether in the early stages of implementing a digital strategy or in the midst of a new technology deployment, change management plays a crucial role.

What Is Change Management?

Change management describes the process(es) an organization will undertake to ensure changes to business operations, systems and other assets cause as little disruption as possible.

For example, a change in systems might require employees to be retrained, taking them away from more immediate, value-creating tasks.

A systems change also could disrupt business operations more directly – if it turns out a new system is incompatible with the current technology infrastructure.

Why Is Change Management Important?

Organizations are faced with constant change. Even industries historically resistant to it, such as financial services and healthcare, are now transforming proactively and at a rapid rate.

Successfully implementing and managing any change, but especially those involving technology, requires an understanding of how it will impact the business – particularly when there are impacts outside the intended goal.

While good ideas help a business grow, sometimes their implementations cause stumbles. Most often that’s because there’s a disconnect between an innovative idea and how it becomes reality.

Such disconnects result in redundant technology and processes, inefficient use of resources, and/or missed opportunities.

With effective change management, organizations usually realize faster implementations and lower costs. An organization with a better understanding of a proposed change is less likely to run into the problems that can derail new initiatives.

Smart change management also can help organizations future-proof their operations, anticipating issues such as systems becoming redundant or outdated earlier than expected.

Change Management and Enterprise Architecture

In large organizations, enterprise architecture (EA) has long been recognized as an effective mechanism for change management. It facilitates an organization’s efforts in assessing the impact of change and making recommendations for target states that support business objectives.

New solution architectures also are being used to successfully assess solution alternatives to support these target states.

EA often delivers the business use cases that justify the incorporation of ideas into operations. However, organizations may find its success limited if the EA function continues to operate in an ivory tower.

Historically, the EA group often has been disconnected from business stakeholders as well as the IT project teams assigned to deliver the solution. This disconnect can lead to the EA team suffering from a lack of commitment from the wider organization and thus their recommendations are ignored.

As a result, ideas are adopted without rigorous scrutiny, including the impacts of their execution and potential ripple effects on other projects.

What’s needed is an integrated approach that marries the EA team’s knowledge with a process for managing ideas and innovation.

change management enterprise architecture

Using Enterprise Architecture to Manage Ideation Through Implementation

A strategic planning approach – from assessment and impact and investment analysis through delivery – ensures ideas are captured, analyzed and shared in a structured process.

Feedback is provided to the originator, and the right stakeholders are involved in making the right decisions about IT projects based on sound business cases. Then both communities feel empowered to make changes.

An integrated, strategic planning environment brings a federated view of information from across the organization so that it can be shared. It helps organizations analyze and prioritize ideas, feed them into EA for analysis, and compile a business case.

With all stakeholders reviewing information and providing feedback on proposed projects, everyone can understand how the new ideas fit into the corporate strategy and have a voice in systematically managing the changes.

Plus they can be executed in near real time, allowing the organization to react quickly to seize market advantage.

Organizations looking to adopt such an approach to change management would benefit from an enterprise architecture tool.

erwin Evolve is one such enterprise architecture tool and a solution addressing both enterprise architecture and business process modeling and analysis use cases.

Users employ erwin Evolve to effectively tame complexity, manage change and increase operational efficiency. Its many benefits include:

  • Creation & Visualization of Complex Models: Harmonize EA/BP modeling capabilities for greater visibility, control and intelligence in managing any use case.
  • Powerful Analysis: Quickly and easily explore model elements, links and dependencies, plus identify and understand the impact of changes through intuitive impact analysis.
  • Documentation & Knowledge Retention: Capture, document and publish information for key business functions to increase employee education and awareness and maintain institutional knowledge, including standard operating procedures.
  • Democratization & Decision-Making: Break down organizational silos and facilitate enterprise collaboration among those both in IT and business roles for more informed decisions that drive successful outcomes.
  • Agility & Efficiency: Achieve faster time to actionable insights and value with integrated views across initiatives to understand and focus on business outcomes.
  • Lower Risks & Costs: Improve performance and profitability with harmonized, optimized and visible processes to enhance training and lower IT costs.

Recent enhancements include web-based diagramming for non-IT users, stronger document generation and analytics, TOGAF support, improved modeling and navigation through inferred relationships, new API extensions, and modular packaging so customers can choose the components that best meet their needs.

Try erwin Evolve now with a free, cloud-based trial – your work will be saved and carried over when you buy.

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Types of Data Models: Conceptual, Logical & Physical

There are three different types of data models: conceptual, logical and physical, and each has a specific purpose.

  • Conceptual Data Models: High-level, static business structures and concepts
  • Logical Data Models: Entity types, data attributes and relationships between entities
  • Physical Data Models: The internal schema database design

An organization’s approach to data modeling will be influenced by its particular needs and the goals it is trying to reach, as explained here:

 

But with the different types of data models, an organization benefits from using all three, depending on the information it wishes to convey and the use cases it wants to satisfy.

That’s because all three types of data models have their advantages and ideal instances in which they should be applied.

The conceptual data model should be used to organize and define concepts and rules.
Typically, business stakeholders and data architects will create such a model to convey what a system contains.

In contrast, the logical data models and physical data models are concerned with how such systems should be implemented.

Like the conceptual data model, the logical data model is also used by data architects, but also will be used by business analysts, with the purpose of developing a database management system (DBMS)-agnostic technical map of rules and structures.

The physical data model is used to demonstrate the implementation of a system(s) using a specific DBMS and is typically used by database analysts (DBAs) and developers.

Data Modeling Data Goverance

Choosing Between Different Types of Data Models for Business Stakeholders: Focus on What’s Important

Oftentimes, data professionals want the full picture found in logical and physical data models. But data professionals aren’t the sole audience for data models.

Stakeholders from the wider business – business leaders, decision-makers, etc. – are less likely less concerned with the specifics than with the outcomes.

Therefore, when using a data model to communicate with such stakeholders, the conceptual data model should not be ignored.

As outlined above, different types of data models will be most applicable – or effective – depending on their context.

To determine context, you have to look at who the data model is being created for and what it will be used to communicate.

An important part of communication is making concepts understandable and using terms that are meaningful to the audience.

Another key aspect is making the information readily available. While it may be feasible to have working sessions with stakeholders to review a logical and/or physical data model, it’s not always possible to scale these workshops to everyone within the organization.

In any data governance endeavour, it’s a best practice to prioritize business-critical data elements and relate them to key business drivers. This approach helps gain the buy-in and interest of business users – essential factors in getting projects of the ground.

The same mode of thinking can and should be applied to data models.

Although it may be tempting to always include fully realized and in-depth data models to paint the fullest picture possible, that will not resonate with all parties.

When gathering business requirements, for example, it’s often more effective to use a conceptual data model and be creative with its display, as shown below.

Different Types of Data Models: Conceptual Data Model
Figure 1: Conceptual Data Model (from The Business Value of Data Modeling for Data Governance)

The use of icons and graphics help tell the “story” of the model and ultimately the story of the business. In this approach, data models can be read as a sentence, with the entities as the nouns and the relationships as the verbs.

For example, we can group the “customer” and its relationship to/action concerning the “product.” In this case, the model represents that “a customer may buy one or more products” via a visual “story” that makes sense to the business.

This high-level perspective makes it easier to quickly understand information, omitting the more technical information that would only be useful to those in the weeds (e.g., business analysts, DBAs and developers).

In the example above, business leaders will be able to make better informed decisions regarding important distinctions in business rules and definitions.

For instance, in the example above, is a “customer” the same as a “client?”

The support team uses the term “client,” while sales uses the term “customer.” Are the concepts the same? Both buy products and/or services from the company.

But if a product or service has not actually been purchased, perhaps “prospect” would be a better term to use.

Can relationships between customers (or customers and prospects) be evaluated and grouped together by household for better sales and support?

None of these answers can be determined without the input of business stakeholders. By showing the concepts and their interrelationships in an intuitive way, definitions and business rules more easily come to light.

The Right Data Modeling Tool For You …

Different data model types serve different purposes and audiences. erwin Data Modeler (erwin DM) supports all three types of data model to help business and technical stakeholders collaborate on the design of information systems and the databases that power them.

With erwin DM, data models and database designs can be generated automatically to increase efficiency and reduce errors, making the lives of data modelers – and other stakeholders – much more productive.

New to erwin DM? Try the latest version of erwin DM for yourself for free!

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erwin Expert Blog Data Modeling

Data Modeling Best Practices for Data-Driven Organizations

As data-driven business becomes increasingly prominent, an understanding of data modeling and data modeling best practices is crucial. This posts outlines just that, and other key questions related to data modeling such as “SQL vs. NoSQL.”

What is Data Modeling?

Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface.

Data models provide visualization, create additional metadata and standardize data design across the enterprise.

As the value of data and the way it is used by organizations has changed over the years, so too has data modeling.

In the modern context, data modeling is a function of data governance.

While data modeling has always been the best way to understand complex data sources and automate design standards, modern data modeling goes well beyond these domains to accelerate and ensure the overall success of data governance in any organization.

 

 

As well as keeping the business in compliance with data regulations, data governance – and data modeling – also drive innovation.

Companies that want to advance artificial intelligence (AI) initiatives, for instance, won’t get very far without quality data and well-defined data models.

With the right approach, data modeling promotes greater cohesion and success in organizations’ data strategies.

But what is the right data modeling approach?

Data Modeling Data Goverance

Data Modeling Best Practices

The right approach to data modeling is one in which organizations can make the right data available at the right time to the right people. Otherwise, data-driven initiatives can stall.

Thanks to organizations like Amazon, Netflix and Uber, businesses have changed how they leverage their data and are transforming their business models to innovate – or risk becoming obsolete.

According to a 2018 survey by Tech Pro Research, 70 percent of survey respondents said their companies either have a digital transformation strategy in place or are working on one. And 60% of companies that have undertaken digital transformation have created new business models.

But data-driven business success doesn’t happen by accident. Organizations that adapt that strategy without the necessary processes, platforms and solutions quickly realize that data creates a lot of noise but not necessarily the right insights.

This phenomenon is perhaps best articulated through the lens of the “three Vs” of data: volume, variety and velocity.

Data Modeling Tool

Any2 Data Modeling and Navigating Data Chaos

The three Vs describe the volume (amount), variety (type) and velocity (speed at which it must be processed) of data.

Data’s value grows with context, and such context is found within data. That means there’s an incentive to generate and store higher volumes of data.

Typically, an increase in the volume of data leads to more data sources and types. And higher volumes and varieties of data become increasingly difficult to manage in a way that provides insight.

Without due diligence, the above factors can lead to a chaotic environment for data-driven organizations.

Therefore, the data modeling best practice is one that allows users to view any data from anywhere – a data governance and management best practice we dub “any-squared” (Any2).

Organizations that adopt the Any2 approach can expect greater consistency, clarity and artifact reuse across large-scale data integration, master data management, metadata management, Big Data and business intelligence/analytics initiatives.

SQL or NoSQL? The Advantages of NoSQL Data Modeling

For the most part, databases use “structured query language” (SQL) for maintaining and manipulating data. This structured approach and its proficiency in handling complex queries has led to its widespread use.

But despite the advantages of such structure, its inherent sequential nature (“this, then “this”), means it can be hard to operate holistically and deal with large amounts of data at once.

Additionally, as alluded to earlier, the nature of modern, data-driven business and the three VS means organizations are dealing with increasing amounts of unstructured data.

As such in a modern business context, the three Vs have become somewhat of an Achilles’ heel for SQL databases.

The sheer rate at which businesses collect and store data – as well as the various types of data stored – mean organizations have to adapt and adopt databases that can be maintained with greater agility.

That’s where NoSQL comes in.

Benefits of NoSQL

Despite what many might assume, adopting a NoSQL database doesn’t mean abandoning SQL databases altogether. In fact, NoSQL is actually a contraction of “not only SQL.”

The NoSQL approach builds on the traditional SQL approach, bringing old (but still relevant) ideas in line with modern needs.

NoSQL databases are scalable, promote greater agility, and handle changes to data and the storing of new data more easily.

They’re better at dealing with other non-relational data too. NoSQL supports JavaScript Object Notation (JSON), log messages, XML and unstructured documents.

Data Modeling Is Different for Every Organization

It perhaps goes without saying, but different organizations have different needs.

For some, the legacy approach to databases meets the needs of their current data strategy and maturity level.

For others, the greater flexibility offered by NoSQL databases makes NoSQL databases, and by extension NoSQL data modeling, a necessity.

Some organizations may require an approach to data modeling that promotes collaboration.

Bringing data to the business and making it easy to access and understand increases the value of data assets, providing a return-on-investment and a return-on-opportunity. But neither would be possible without data modeling providing the backbone for metadata management and proper data governance.

Whatever the data modeling need, erwin can help you address it.

erwin DM is available in several versions, including erwin DM NoSQL, with additional options to improve the quality and agility of data capabilities.

And we just announced a new version of erwin DM, with a modern and customizable modeling environment, support for Amazon Redshift; updated support for the latest DB2 releases; time-saving modeling task automation, and more.

New to erwin DM? You can try the new erwin Data Modeler for yourself for free!

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erwin Expert Blog

Top 7 Data Governance and Metadata Management Blog Posts of 2019

Data has been the driving force of the decade. Digital pioneers like Amazon, Netflix and Uber account for some of the most extreme market disruption their respective industries have faced.

But such success cannot be attributed soley to head-starts.

Many organizations have tried and failed to become truly “data-driven,” and many organizations will continue to do so.

The difference between success and failure is often a deeper understanding of the data behind an organization’s operational decisions.

With a deeper understanding of their data assets, organizations can realize more trustworthy and effective analysis.

Such understanding also equips organizations  to meet customer demands as well as deal more effectively with the regulatory landscape – which is evolving at a fast rate.

To help you prepare for 2020, we’ve compiled some of the most popular data governance and metadata management blog posts from the erwin Experts from this year.

Data Governance and Metadata Management Blog Posts

The Best Data Governance and Metadata Management Blog Posts of 2019

Data Governance Framework: Three Steps to Successful and Sustainable Implementation

A strong data governance framework is central to the success of any data-driven organization because it ensures this valuable asset is properly maintained, protected and maximized.

But despite this fact, enterprises often face push back when implementing a new data governance initiative or trying to improve an existing one:

Four Use Cases Proving the Benefits of Metadata-Driven Automation

Data scientists and other data professionals can spend up to 80 percent of their time bogged down trying to understand source data or address errors and inconsistencies.

That’s time needed and better used for data analysis.

In this metadata management blog, the erwin Experts assess four use cases that demonstrate exactly how metadata-driven automation increases productivity:

Data Mapping Tools: What Are the Key Differentiators

Data mapping tools help organizations discover important insights.

They provide context to what otherwise would be isolated units of meaningless data.

Now with the General Data Protection Regulation (GDPR) in effect, data mapping has become even more significant:

The Unified Data Platform – Connecting Everything That Matters

Businesses stand to gain a lot from unifying their data platforms.

Data-driven leaders dominate their respective markets and inspire other organizations across the board to use data to fuel their businesses.

It was even dubbed “the new oil at one point” but data is arguably far more valuable than that analogy suggests:

A Guide to CCPA Compliance and How the California Consumer Privacy Act Compares to GDPR

The California Consumer Privacy Act (CCPA) and GDPR share many of the same data privacy and security requirements.

While the CCPA has been signed into law, organizations have until Jan. 1, 2020, to enact its mandates. Luckily, many organizations have already laid the regulatory groundwork for it because of their efforts to comply with GDPR.

However, there are some key differences, which the erwin Experts explore in a Q&A format:

Business Architecture and Process Modeling for Digital Transformation

Digital transformation involves synthesizing an organization’s people, processes and technologies, so involving business architecture and process modeling is a best practice organizations can’t ignore.

The following post outlines how business architecture and process modeling work in tandem to facilitate efficient and successful digital transformation efforts:

Constructing a Digital Transformation Strategy: Putting the Data in Digital Transformation

Having a clearly defined digital transformation strategy is an essential best practice to ensure success.

But what makes a digital transformation strategy viable? Learn here:

Gartner Magic Quadrant Metadata Management

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erwin Expert Blog

Data Governance 2.0: The CIO’s Guide to Collaborative Data Governance

In the data-driven era, CIO’s need a solid understanding of data governance 2.0 …

Data governance (DG) is no longer about just compliance or relegated to the confines of IT. Today, data governance needs to be a ubiquitous part of your organization’s culture.

As the CIO, your stakeholders include both IT and business users in collaborative relationships, which means data governance is not only your business, it’s everyone’s business.

The ability to quickly collect vast amounts of data, analyze it and then use what you’ve learned to help foster better decision-making is the dream of business executives. But that vision is more difficult to execute than it might first appear.

While many organizations are aware of the need to implement a formal data governance initiative, many have faced obstacles getting started.

A lack of resources, difficulties in proving the business case, and challenges in getting senior management to see the importance of such an effort rank among the biggest obstacles facing DG initiatives, according to a recent survey by UBM.

Common Data Governance Challenges - Data Governance 2.0

Despite such hurdles, organizations are committed to trying to get data governance right. The same UBM study found that 98% of respondents considered data governance either important, or critically important to their organization.

And it’s unsurprising too. Considering that the unprecedented levels of digital transformation, with rapidly changing and evolving technology, mean data governance is not just an option, but rather a necessity.

Recognizing this, the IDC DX Awards recently resurfaced to give proper recognition and distinction to organizations who have successfully digitized their systems and business processes.

Creating a Culture of Data Governance

The right data of the right quality, regardless of where it is stored or what format it is stored in, must be available for use only by the right people for the right purpose. This is the promise of a formal data governance practice.

However, to create a culture of data governance requires buy-in from the top down, and the appropriate systems, tools and frameworks to ensure its continued success.

This take on data governance is often dubbed as Data Governance 2.0.

At erwin, we’ve identified what we believe to be the five pillars of data governance readiness:

  1. Initiative Sponsorship: Without executive sponsorship, you’ll have difficulty obtaining the funding, resources, support and alignment necessary for successful DG.
  2. Organizational Support: DG needs to be integrated into the data stewardship teams and wider culture. It also requires funding.
  3. Team Resources: Most successful organizations have established a formal data management group at the enterprise level. As a foundational component of enterprise data management, DG would reside in such a group.
  4. Enterprise Data Management Methodology: DG is foundational to enterprise data management. Without the other essential components (e.g., metadata management, enterprise data architecture, data quality management), DG will be a struggle.
  5. Delivery Capability: Successful and sustainable DG initiatives are supported by specialized tools, which are scoped as part of the DG initiative’s technical requirements.

Data Security

Data is becoming increasingly difficult to manage, control and secure as evidenced by the uptick in data breaches in almost every industry.

Therefore companies must work to secure intellectual property (IPs), client information and so much more.

So CIOs have to come up with appropriate plans to restrict certain people from accessing this information and allow only a small, relevant circle to view it when necessary.

However, this job isn’t as easy as you think it is. Organizations must walk the line between ease of access/data discoverability and security.

It’s the CIO’s responsibility to keep the balance, and data governance tools with role-based access can help maintain that balance.

Data Storage

The amount of data modern organizations have to manage means CIOs have to rethink data storage, as well as security.

This includes considerations as to what data should be stored and where, as well as understanding what data the organization – and the stakeholders within it – is responsible for.

This knowledge will enable better analysis, and the data used for such analysis more easily accessed when required and by approved parties. This is especially crucial for compliance with government regulations like the General Data Protection Regulation (GDPR), as well as other data regulations.

Defining the Right Audience

It’s a CIO’s responsibility to oversee the organization’s data governance systems. Of course, this means the implementation and upkeep of such systems, but it also includes creating the policies that will inform the data governance program itself.

Nowadays, lots of employees think they need access to all of an organization’s data to help them make better decisions for the company.

However, this can possibly expose company data to numerous threats and cyber attacks as well as intellectual property infringement.

So data governance that ensures only the right audience can access specific company information can come in handy, especially during a company’s brainstorming seasons, new products and services releases, and so much more.

Data governance is to be tailored by CIOs to meet their organizations’ specific needs (and wants). This is to ensure an efficient and effective way of utilizing data while also enabling employees to make better and wiser business decisions.

The Right Tools Help Solve the Enterprise Data Dilemma

What data do we have, where is it and what does it mean? This is the data dilemma that plagues most organizations.

The right tools can make or break your data governance initiatives. They encompass a number of different technologies, including data cataloging, data literacy, business process modeling, enterprise architecture and data modeling.

Each of these tools separately contribute to better data governance, however, increasingly, organizations are realizing the benefits of interconnectivity between them. This interconnectivity can be achieved through centralizing data-driven projects around metadata.

This means data professionals and their work benefits from a single source of truth, making analysis faster, more trustworthy and far easier to collaborate on.

With the erwin EDGE, an “enterprise data governance experience” is created to underpin Data Governance 2.0.

It unifies data and business architectures so all IT and business stakeholders can access relevant data in the context of their roles, supporting a culture committed to using data as a mission-critical asset and orchestrating the key mechanisms required to discover, fully understand, actively govern and effectively socialize and align data to the business.

You can learn more about data governance by reading our whitepaper: Examining the Data Trinity: Governance, Security and Privacy.

Examining the Data Trinity - Governance, Security and Privacy