That’s the word from Thomas Erl, CEO of Arcitura Education, which provides technology skills training to thousands of professionals across the globe, and co-author of A Field Guide to Digital Transformation. “It’s a new era for enterprise architects,” he says. Their roles are being transformed along with the business, Erl, who has written extensively about EA over the years, explained in a recent interview. “Their lives have been impacted significantly,” he says. “If you look at what now constitutes an enterprise architect, they are responsible for a digital enterprise architecture. It’s a completely different ecosystem that they have to maintain.” EAs have been traditionally been responsible for application design, application architecture, deployment, administration, and messaging – “building the different parts that work together,” Erl says. Elsewhere, “the data domain has always been managed by data specialists.” Now, digital EAs need to take leadership within data domains as well. “An enterprise architect for a digital enterprise cannot avoid having to gain an understanding of data science – it’s unavoidable,” Erl says. “Just like the executive can’t defer to technical experts anymore, they need a level of understanding themselves, a contemporary enterprise architect can no loner defer to the data people for that expertise in order to establish a data enterprise architecture in support of a digital enterprise.” Digital enterprises “require that data science systems are integrated as part of the functioning of applications,” Erl says. “It’s not actually about creating reports from managers if you can now bring in automated decision-making into how your enterprise solutions will function. And in terms of how they will automate.” This introduces “a whole new dimension to application design,” Erl continues. “Enterprise architects need to understand where decision points should and should not be in automated decision-making, and the consequences of bad decisions being made. They need to conduct a risk assessment of deferring decisions to an AI system, how the AI system can be trained to improve its own decisions over time, and the implications of that how the system operates. It’s now a native part of application designs, for many enterprises.” Data intelligence and application infrastructure are now inextricably tied. “Data intelligence that not only helps managers carry out strategic decision-making less manually, but also helps solutions become more effective, more responsive, and more successful, more profitable in terms of what they’re designed for,” Erl states. Still, digital EAs need to serve as a bulwark against enterprises throwing too much money at technology delivering uncertain returns. “In a digital transformation environment, its not just bringing in new technology, to do new things, that technology needs to be blended and balanced together,” says Erl. The changes brought on by the rapid advance to digital transformation is being felt across the IT professional landscape, Erl adds. “The business side is something they really need to understand so they don’t get caught off guard. Because things are changing so rapidly. With digital transformation, you can roll things out, and you can promise the world, but you have to be able to sustain that for it to be truly successful, and that comes down to how the digital transformation is carried out within the organization.” That’s because digital transformation requires “a different culture, mindset, that is required to go along with leveraging the new technology innovations that introduce new forms of automation, that introduce new forms of decision-making, and new forms of utilizing data intelligence,” Erl says. “The whole aspect of now having very comprehensive data and insightful intelligence available to us is extremely powerful, but needs to be understood in order to be fully leveraged. Because if you don’t understand what it is you’re being given insights for, if you don’t understand how to use it, and most importantly, if your IT teams don’t understand how to properly generate data intelligence that is of relevance to your organization, that effort can take you down the wrong path altogether.” There’s a skillset gap that many organizations have, “or don’t realize they have,” Erl says. “Those that produce data intelligence need to know what it is they should produce. Where does the relevant insight come from? That needs to come from leadership.” Erl predicts increasing demand for IT professionals with data analytics and business acumen. “If you can position your career development path for a career, whereby even if you’re a programmer, you learn about machine learning and AI, and even if you’re a data person, you learn about robotic process automation.” IT professionals need to expand their horizons. “Over the next few years, employers will be looking for more of a breadth of experience than before,” Erl states. “They may not need a pure Java programmer. But they may need the Java programmer who has integrated a machine learning system with a cloud-based architecture.”
