Categories
erwin Expert Blog Enterprise Architecture

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.

Covid-19 business resources

Categories
erwin Expert Blog

Pillars of Data Governance Readiness: Enterprise Data Management Methodology

Facebook’s data woes continue to dominate the headlines and further highlight the importance of having an enterprise-wide view of data assets. The high-profile case is somewhat different than other prominent data scandals as it wasn’t a “breach,” per se. But questions of negligence persist, and in all cases, data governance is an issue.

This week, the Wall Street Journal ran a story titled “Companies Should Beware Public’s Rising Anxiety Over Data.” It discusses an IBM poll of 10,000 consumers in which 78% of U.S. respondents say a company’s ability to keep their data private is extremely important, yet only 20% completely trust organizations they interact with to maintain data privacy. In fact, 60% indicate they’re more concerned about cybersecurity than a potential war.

The piece concludes with a clear lesson for CIOs: “they must make data governance and compliance with regulations such as the EU’s General Data Protection Regulation [GDPR] an even greater priority, keeping track of data and making sure that the corporation has the ability to monitor its use, and should the need arise, delete it.”

With a more thorough data governance initiative and a better understanding of data assets, their lineage and useful shelf-life, and the privileges behind their access, Facebook likely could have gotten ahead of the problem and quelled it before it became an issue.  Sometimes erasure is the best approach if the reward from keeping data onboard is outweighed by the risk.

But perhaps Facebook is lucky the issue arose when it did. Once the GDPR goes into effect, this type of data snare would make the company non-compliant, as the regulation requires direct consent from the data owner (as well as notification within 72 hours if there is an actual breach).

Five Pillars of DG: Enterprise Data Management Methodology

Considering GDPR, as well as the gargantuan PR fallout and governmental inquiries Facebook faced, companies can’t afford such data governance mistakes.

During the past few weeks, we’ve been exploring each of the five pillars of data governance readiness in detail and how they come together to provide a full view of an organization’s data assets. In this blog, we’ll look at enterprise data management methodology as the fourth key pillar.

Enterprise Data Management in Four Steps

Enterprise data management methodology addresses the need for data governance within the wider data management suite, with all components and solutions working together for maximum benefits.

A successful data governance initiative should both improve a business’ understanding of data lineage/history and install a working system of permissions to prevent access by the wrong people. On the flip side, successful data governance makes data more discoverable, with better context so the right people can make better use of it.

This is the nature of Data Governance 2.0 – helping organizations better understand their data assets and making them easier to manage and capitalize on – and it succeeds where Data Governance 1.0 stumbled.

Enterprise Data Management: So where do you start?

  1. Metadata management provides the organization with the contextual information concerning its data assets. Without it, data governance essentially runs blind.

The value of metadata management is the ability to govern common and reference data used across the organization with cross-departmental standards and definitions, allowing data sharing and reuse, reducing data redundancy and storage, avoiding data errors due to incorrect choices or duplications, and supporting data quality and analytics capabilities.

  1. Your organization also needs to understand enterprise data architecture and enterprise data modeling. Without it, enterprise data governance will be hard to support

Enterprise data architecture supports data governance through concepts such as data movement, data transformation and data integration – since data governance develops policies and standards for these activities.

Data modeling, a vital component of data architecture, is also critical to data governance. By providing insights into the use cases satisfied by the data, organizations can do a better job of proactively analyzing the required shelf-life and better measure the risk/reward of keeping that data around.

Data stewards serve as SMEs in the development and refinement of data models and assist in the creation of data standards that are represented by data models. These artifacts allow your organization to achieve its business goals using enterprise data architecture.

  1. Let’s face it, most organizations implement data governance because they want high quality data. Enterprise data governance is foundational for the success of data quality management.

Data governance supports data quality efforts through the development of standard policies, practices, data standards, common definitions, etc. Data stewards implement these data standards and policies, supporting the data quality professionals.

These standards, policies, and practices lead to effective and sustainable data governance.

  1. Finally, without business intelligence (BI) and analytics, data governance will not add any value. The value of data governance to BI and analytics is the ability to govern data from its sources to destinations in warehouses/marts, define standards for data across those stages, and promote common algorithms and calculations where appropriate. These benefits allow the organization to achieve its business goals with BI and analytics.

Gaining an EDGE on the Competition

Old-school data governance is one-sided, mainly concerned with cataloging data to support search and discovery. The lack of short-term value here often caused executive support to dwindle, so the task of DG was siloed within IT.

These issues are circumvented by using the collaborative Data Governance 2.0 approach, spreading the responsibility of DG among those who use the data. This means that data assets are recorded with more context and are of greater use to an organization.

It also means executive-level employees are more aware of data governance working as they’re involved in it, as well as seeing the extra revenue potential in optimizing data analysis streams and the resulting improvements to times to market.

We refer to this enterprise-wide, collaborative, 2.0 take on data governance as the enterprise data governance experience (EDGE). But organizational collaboration aside, the real EDGE is arguably the collaboration it facilitates between solutions. The EDGE platform recognizes the fundamental reliance data governance has on the enterprise data management methodology suite and unifies them.

By existing on one platform, and sharing one repository, organizations can guarantee their data is uniform across the organization, regardless of department.

Additionally, it drastically improves workflows by allowing for real-time updates across the platform. For example, a change to a term in the data dictionary (data governance) will be automatically reflected in all connected data models (data modeling).

Further, the EDGE integrates enterprise architecture to define application capabilities and interdependencies within the context of their connection to enterprise strategy, enabling technology investments to be prioritized in line with business goals.

Business process also is included so enterprises can clearly define, map and analyze workflows and build models to drive process improvement, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.

Essentially, it’s the approach data governance needs to become a value-adding strategic initiative instead of an isolated effort that peters out.

To learn more about enterprise data management and getting an EDGE on GDPR and the competition, click here.

To assess your data governance readiness ahead of the GDPR, click here.

Take the DG RediChek

Categories
erwin Expert Blog Enterprise Architecture

Agile Enterprise Architecture in Higher Education

Over the last 5-10 years enterprise architecture (EA) has gained momentum in the Higher Education (or Further Education) sector, with many University and College institutions establishing an EA practice to help get on top of constantly changing and complex IT strategy and business strategy requirements.

Universities are in an especially unique situation of being both a research business and education business, with a degree of overlap between the two (researchers are also often the educators). And the added dichotomy of Universities both competing and at the same time collaborating with each other.

There are many complexities to doing EA in Higher Education, with tightening budgets, pressure to rationalize IT and related support and services. At the same time they must provide flexibility to cope with changing requirements, deliver innovative services to students and academics, and prepare for whatever is next on the horizon.

This is where agile enterprise architecture helps. But first, let’s briefly look at the current state of EA in Higher Education.

Agile enterprise architecture for universities

Enterprise Architecture in Universities

There are varying levels of EA maturity in the University/College ecosystem. Less mature organizations will often utilize Visio, Powerpoint or UML modelling tools to complete architecture-related tasks. However, there are major challenges with these tools around consistency of multiple diagrams, the effective communication and collaboration of architecture assets with stakeholders, and the timeliness of assets for use in decision making.

At the opposite end, the more mature institutions have purchased specialist tools and established an EA practice, and are using a common EA language such as ArchiMate® to build, manage and communicate assets in a consistent manner.

So what’s next for Higher Education institutions?

Adopting Agile Enterprise Architecture in Education Institutions

Every year EDUCAUSE, the non-profit organization whose mission is to advance higher education through the use of IT, publish a list of Top 10 IT Issues. One major theme from the 2015 list is the shift in Higher Education IT’s focus from technical problems to business problems, along with the growing interdependence between the IT organization and business units.

How Higher Education institutions respond to this acceleration of changing IT and business requirements is a top issue for Enterprise Architecture. To simply keep pace with the rate of change in 2015 and beyond, organizations must develop the capability to act with agility, to learn, respond and take action in shorter amounts of time.

What’s required is a new approach to enterprise architecture that’s focused on producing just the right amount of architecture assets for senior stakeholders and decision makers – communicating architecture quickly and only when it is valuable to do so for more agile IT and business decision making.

In the past, architects have often been guilty of producing detailed EA documentation but much of it providing little value to senior decision makers. Universities need to move away from this and adopt an agile enterprise architecture approach.

enterprise architecture business process