April 28, 2025
When it comes to the subject of enterprise architecture (EA), there are a number of areas where there is a lack of consensus. Even within the same organization, people often disagree on what their EA group is there to do.
My time working in the EA space is approaching its sixth year. The first 5.5 years at an EA platform and now with Entrio. The one thing I see most agreement on is that good foundational data is essential for a successful EA practice. It is one of, if not the most important aspects of accomplishing the team's goals.
There are a lot of great tools out there that can help you to achieve your EA goals, whether you need to rationalize the application portfolio, support a digital transformation, minimize tech risk, or build out an organizational capability map. But utilizing those tools without an accurate and complete data set is like buying a car but not having any gas.
Inaccurate and incomplete data are the two major issues that I have seen, time and time again, hinder successful EA journeys. This stands true whether you have invested in an EA tool or are still determining if you need one.
Firstly, most data has a lot of mistakes and duplication. This occurs because data is constantly changing and it is hard for companies to keep up. Vendors are acquired or rebranded. Products right along with them. Vendors announce end of life for their products and versions all the time and if your organization has hundreds or even thousands of solutions, it is a tall order to keep track of it all.
Other times errors are a result of the end user. A simple spelling mistake or not knowing the name of a current product can lead to wrong information being captured. Another typical problem is not having a consistent naming convention. Take Microsoft SQL Server as an example. An organization could end up with their data having this product listed as any of the following:
This is just a small sample of the various permutations that may end up in an organization's data set. On average, we see about 25% of data contains errors or inconsistencies. In addition, we’ve been able to reduce data records by 40% due to duplication. These numbers can be even higher if no previous attempts have been made to clean the data. This level of messiness and inaccuracy can lead you to believe that you have more tools than you actually do or and blurs your overall picture. When you don’t know what you have, you can end up buying more tools unnecessarily or introducing risk into the organization.
The second challenge is that the data is missing key information. When taking on an EA project, there is always going to be data that needs to be provided by your end users, such as what capabilities it is being used for and how well it is meeting those capabilities. However, a lot of data needed on each solution is consistent from one organization to another. This includes key pieces information for almost every EA initiative, like:
This data is also constantly changing and difficult to keep updated. But if you don’t know the true capabilities of a solution, your organization could be set up to waste a massive amount of money. You can’t easily spot opportunities to consolidate solutions because you don’t know that you have multiple tools that are capable of the same thing. You also won’t be able to prevent future sprawl because you can’t support software asset requests or architecture review boards in a timely manner. Employees will request new tools for certain capabilities and the organization will not know that they already have something in place that can accomplish what is needed. Having accurate data about solutions and vendors can increase the reusability of products and cut down significantly on time that employees spend searching for products or researching solutions.
The reason I came to Entrio is that we have developed a solution that solves these two major problems and allows organizations to begin focusing on EA without having to worry about their data.
We have the ability to consolidate, normalize, and cleanse foundational data from any tools whether it's a CMDB, an asset management system, an EA platform, vendor management, procurement, GRC tool, FinOps, Cloud Discovery, Excel, SharePoint, or Confluence. This gives you and the EA team a clear picture of what you truly have. We take the information from these various sources, show the discrepancies and inconsistencies between data sets, and also highlight the errors in vendor or product names. Entrio is able to provide visibility into all asset types, can reconcile in-house or external repositories, and provides one always updating reference catalog that is a source of trust to make sure your solution information is clean.
Once the data is cleansed and normalized, the next step is enriching the data with the fundamental information that is needed to get started. For any product we can automatically provide such key information as what hosting options are available, when each version is going end of life, and most importantly, what the solution actually does. Our detailed and extremely granular taxonomy allows you to know that the true capability of a solution is so that you can maximize use of what you have in place. The enrichment provided gives significant details so that you can have the granularity that is needed to truly know what's in your portfolio.
In my time in EA I have seen many EA teams get disbanded and numerous EA platform initiatives fail. Oftentimes, this is a result of not having the right stakeholders onboard, choosing the wrong tool, or other factors. But the number one reason is by far that the data is not in good enough shape to allow the EA team to succeed. Now with Entrio, you can check off that concern and get back to focusing on EA.
