erwin Expert Blog

Takeaways from Forrester’s Latest Report on Enterprise Architecture Management Suites

Forrester recently released its “Now Tech: Enterprise Architecture Management Suites for Q1 2020” to give organizations an enterprise architecture (EA) playbook.

It also highlights select enterprise architecture management suite (EAMS) vendors based on size and functionality, including erwin.

The report notes six primary EA competencies in which we excel in the large vendor category: modeling, strategy translation, risk management, financial management, insights and change management.

Given our EA expertise, we thought we’d provide our perspective on the report’s key takeaways and how we see technology trends, business innovation and compliance driving companies to use EA in different ways.

Enterprise Architecture Management Systems (EAMS)

Improve Enterprise Architecture with EAMS

To an EA professional, it may seem obvious that tools provide “a holistic view of business demand impact.” Delivery of innovation at speed is critical, but what does that really mean?

Not only should EA be easy to adopt and roll out, artifacts should be easy to visualize quickly and effectively by various stakeholders in the format they need to make decisions rapidly.

For “EA stakeholders to be more productive and effective,” not only is a central repository a necessity but collaboration and a persona-driven approach also are critical to the organization’s adoption of EA.

Just as an ERP system is a fundamental part of business operations, so is an enterprise architecture management suite. It’s a living, breathing tool that feeds into and off of the other physical repositories in the organization, such as ServiceNow for CMDB assets, RSA Archer for risk logs, and Oracle NetSuite and Salesforce for financials.

Being able to connect the enterprise architecture management suites to your business operating model will give you “real-time insights into strategy and operations.”

And you can further prove the value of EA with integrations to your data catalog and business glossary with real-time insights into the organization’s entire data landscape.

enterprise architecture innovation management

Select Enterprise Architecture Vendors Based on Size and Functionality

EA has re-emerged to help solve compliance challenges in banking and finance plus drive innovation with artificial intelligence (AI), machine learning (ML) and robotic automation in pharmaceuticals.

These are large organizations with significant challenges, which require an EA vendor to invest in research and development to innovate across their offerings so EA can become a fundamental part of an organization’s operating model.

We see the need for a “proprietary product platform” in the next generation of EA, so customers can create their own products and services to meet their particular business needs.

They’re looking for product management, dev/ops, security modeling, personas and portfolio management all to be part of an integrated EA platform. In addition, customers want to ensure platforms are secure with sound coding practices and testing.

Determine the Key Enterprise Architecture Capabilities Needed

With more than 20 years of EA experience, erwin has seen a lot of changes in the market, many in the last 24 months. Guess what? This evolution isn’t slowing down.

We’re working with some of the world’s largest companies (and some smaller ones too) as they try to manage change in their respective industries and organizations.

Yesterday’s use case may not serve tomorrow’s use case. An EA solution should be agile enough to meet both short-term and long-term needs.

Use EA Performance Measures to Validate Enterprise Architecture Management Suite Value

EA should provide a strong ROI and help an organization derive value and successful business outcomes.

Additionally, a persona-based approach that involves configuring the user interface and experience to suit stakeholder needs eases the need for training.

Formalized training is important for EA professionals and some stakeholders, and the user interface and experience should reduce the need for a dedicated formal training program for those deriving value out of EA.

Why erwin for Enterprise Architecture?

Whether documenting systems and technology, designing processes and value streams, or managing innovation and change, organizations need flexible but powerful enterprise architecture tools they can rely on for collecting the relevant information for decision-making.

Like constructing a building or even a city – you need a blueprint to understand what goes where, how everything fits together to support the structure, where you have room to grow, and if it will be feasible to knock down any walls if you need to.

Without a picture of what’s what and the interdependencies, your enterprise can’t make changes at speed and scale to serve its needs.

erwin Evolve is a full-featured, configurable set of enterprise architecture tools, in addition to business process modeling and analysis.

The combined solution enables organizations to map IT capabilities to the business functions they support and determine how people, processes, data, technologies and applications interact to ensure alignment in achieving enterprise objectives.

See for yourself why we were included in the latest Forrester EAMS report. We’re pleased to offer you a free trial of erwin Evolve.enterprise architecture business process

erwin Expert Blog

Why Data Governance is the Key to Better Decision-Making

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 many a business executive. But like any number of things that can be summarized in a single sentence, it’s much harder to execute on such a vision than it might first appear.

According to Forrester, 74 percent of firms say they want to be “data-driven,” but only 29 percent say they are good at connecting analytics to action. Consider this: Forrester found that business satisfaction with analytics dropped by 21 percent between 2014 and 2015 – a period of great promise and great investment in Big Data. In other words, the more data businesses were collecting and mining, the less happy they were with their analytics.

A number of factors are potentially at play here, including the analytics software, the culture of the business, and the skill sets of the people using the data. But your analytics applications and the conclusions you draw from your analysis are only as good as the data that is collected and analyzed. Collecting, safeguarding and mining large amounts of data isn’t an inexpensive exercise, and as the saying goes, “garbage in, garbage out.”

“It’s a big investment and if people don’t trust data, they won’t use things like business intelligence tools because they won’t have faith in what they tell them,” says Danny Sandwell, director of product marketing at erwin, Inc.

Using data to inform business decisions is hardly new, of course. The modern idea of market research dates back to the 1920s, and ever since businesses have collected, analyzed and drawn conclusions from information they draw from customers or prospective customers.

The difference today, as you might expect, is the amount of data and how it’s collected. Data is generated by machines large and small, by people, and by old-fashioned market research. It enters today’s businesses from all angles, at lightning speed, and can, in many cases, be available for instant analysis.

As the volume and velocity of data increases, overload becomes a potential problem. Unless the business has a strategic plan for data governance, decisions around where the data is stored, who and what can access it, and how it can be used, becomes increasingly difficult to understand.

Not every business collects massive amounts of data like Facebook and Yahoo, but recent headlines demonstrate how those companies’ inability to govern data is harming their reputations and bottom lines. For Facebook, it was the revelation that the data of 87 million users was improperly obtained to influence the 2016 U. S. presidential election. For Yahoo, the U.S. Securities and Exchange Commission (SEC) levied a $35 million fine for failure to disclose a data breach in a timely manner.

In both the Facebook and Yahoo cases, the misuse or failure to protect data was one problem. Their inability to quickly quantify the scope of the problem and disclose the details made a big issue even worse – and kept it in the headlines even longer.

The issues of data security, data privacy and data governance may not be top of mind for some business users, but these issues manifest themselves in a number of ways that affect what they do on a daily basis. Think of it this way: somewhere in all of the data your organization collects, a piece of information that can support or refute a decision you’re about to make is likely there. Can you find it? Can you trust it?

If the answer to these questions is “no,” then it won’t be easy for your organization to make data-driven decisions.

Better Decision-Making - Data Governance

Powering Better Decision-Making with Data Governance

Nearly half (45 percent) of the respondents to a November 2017 survey by erwin and UBM said better decision-making was one of the factors driving their data governance initiatives.

Data governance helps businesses understand what data they have, how good it is, where it is, and how it’s used. A lot of people are talking about data governance today, and some are putting that talk into action. The erwin/UBM survey found that 52 percent of respondents say data is critically important to their organization and they have a formal data governance strategy in place. But almost as many respondents (46 percent) say they recognize the value of data to their organization but don’t have a formal governance strategy.

Many early attempts at instituting data governance failed to deliver results. They were narrowly focused, and their proponents often had difficulty articulating the value of data governance to the organization, making it difficult to secure budget. Some organizations even understood data governance as a type of data security, locking up data so tightly that the people who wanted to use it to foster better decision-making had trouble getting access.

Issues of ownership also stymied early data governance efforts, as IT and the business couldn’t agree on which side was responsible for a process that affects both on a regular basis. Today, organizations are better equipped to resolve issues of ownership, thanks in large part to a new corporate structure that recognizes how important data is to modern businesses. Roles like chief data officer (CDO), which increasingly sits on the business side, and the data protection officer (DPO), are more common than they were a few years ago.

A modern data governance strategy works a lot like data itself – it permeates the business and its infrastructure. It is part of the enterprise architecture, the business processes, and it help organizations better understand the relationships between data assets using techniques like visualization. Perhaps most important, a modern approach to data governance is ongoing, because organizations and their data are constantly changing and transforming, so their approach to data governance can’t sit still.

As you might expect, better visibility into your data goes a long way toward using that data to make more informed decisions. There is, however, another advantage to the visibility offered by a holistic data governance strategy: it helps you better understand what you don’t know.

By helping businesses understand the areas where they can improve their data collection, data governance helps organizations continually work to create better data, which manifests itself in real business advantages, like better decision-making and top-notch customer experiences, all of which will help grow the business.

Michael Pastore is the Director, Content Services at QuinStreet B2B Tech. This content originally appeared as a sponsored post on

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