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Enterprise Architects has added a new name to their titles, Digital Enterprise Architects. This is because their role has expanded over the past few years, especially with the addition of data analytics to the repertoire. Other IT professionals are also seeing their roles changing.
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This is from Thomas Erl, CEO of Arcitura Education, who provides technology skill training to thousands of professionals around the world and is co-author of the field guide for digital transformation. “This is a new era for enterprise architects,” he says. Their role is changing with the business, and Erl, who has written extensively about EA for many years, explains in a recent interview.
“Their lives have been greatly influenced,” he says. “Looking at what makes up an enterprise architect today, they’re responsible for the digital enterprise architecture. It’s a completely different ecosystem they have to maintain.”
EA has traditionally been responsible for application design, application architecture, deployment, management, and messaging. “We build different parts that work together,” says Erl. Elsewhere, “data domains have always been managed by data specialists.”
Today, digital EA needs to take leadership within the data domain as well. “Enterprise architects in the digital enterprise need to understand data science, which is inevitable,” says Erl. “They need to understand themselves to some extent so that executives can no longer rely on technical professionals. Modern enterprise architects specialize in establishing a data enterprise architecture that supports digital. Knowledge cannot be left to the data person. Company. “
Digital companies “need to have data science systems integrated as part of the functionality of their applications,” Erl says. “If we can embrace automated decision-making in terms of how enterprise solutions work and how they are automated, it’s not really about creating reports from managers.”
This introduces a “whole new dimension to application design,” Erl continues. “Enterprise architects need to understand where and shouldn’t be decision points in automated decision making, and the consequences of bad decisions. They need to make AI systems. You need to perform a risk assessment to defer decisions. They are trained to improve their own decisions over time and have implications for how the system works. This is what many companies do. It has become a native part of application design for us. “
Today, data intelligence and application infrastructure are inextricably linked. “Data intelligence not only helps managers make strategic decisions manually, but also makes the solution more effective, responsive, more successful, and more profitable in terms of design targets. It helps to become a thing, “says Erl.
Still, digital EA needs to act as a breakwater for companies that spend a lot of money on technologies that bring uncertain profits. “In a digital transformation environment, we need to not only introduce new technologies, but also blend and balance them in order to do new things,” says Erl.
Erl adds that the changes brought about by the rapid advances in digital transformation are felt throughout the world of IT professionals. “The business side is something that they really need to understand so they don’t get caught up in it because things are changing so rapidly. With digital transformation, things unfold and the world You can promise, but you can keep it to be truly successful, and it depends on how digital transformation is performed within your organization. “
This is because digital transformation “introduces new forms of automation, introduces new forms of decision making, and leverages new forms of data intelligence to take advantage of new innovations needed to take advantage of different cultures and ideas. Is necessary. To tell. “The whole aspect of having very comprehensive data and insightful intelligence available is very powerful, but you need to understand it to take full advantage of it. What is it? If you don’t understand, you’re given insights. If you don’t know how to use it, and most importantly, your IT team doesn’t understand how to properly generate data intelligence related to your organization. If so, that effort can go the wrong way. “
There are skill set gaps that many organizations have, “or are unaware that they have,” says Erl. “People who generate data intelligence need to know what it should produce. Where do the relevant insights come from? It needs to come from leadership.”
Erl anticipates increasing demand for IT professionals with data analytics and business insights. “If we can position the career development path in our careers, programmers can learn about machine learning and AI, and data professionals can learn about robot process automation.”
IT professionals need to broaden their horizons. “In the next few years, employers will want more experience than they used to,” Erl said. “They may not need a pure Java programmer, but they may need a Java programmer that integrates a machine learning system with a cloud-based architecture.”
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