The tech industry has never lacked for data, but siloed systems have cut short the ability to deeply interpret that data. This is all changing as the industry is shedding its past—and tapping the transformative power of a single, unified system.
Tech organizations have experienced whiplash in recent years—and they’re still feeling the effects. Pre-pandemic, many tech organizations focused intently on pursuing growth to become the next unicorn or the next initial public offering (IPO) success story. And then came the pandemic, social upheavals, and economic uncertainties involving supply chain disruptions and rising interest rates. Nearly overnight, the tech industry had no choice but to change.
Companies not only had to embrace virtual work but also had to adjust their understanding of their workforce’s health and wellness, along with placing renewed emphasis on diversity, equity, and inclusion (DE&I) strategies. “The definition of success shifted,” says Justin Joseph, senior director, product strategy, Workday. “It’s no longer about growing at any cost.”
Under these pressures, tech organizations now have to look at the current and future climate and ask themselves where they want to be in the next three to five years—and whether they have the business models, the people, and the technology to help them get there.
Almost 3 in 5 tech industry leaders (59%) said the pandemic accelerated digital transformation—the most of any industry outside of retail, according to “Closing the Acceleration Gap: Toward Sustainable Digital Transformation,” Workday’s global survey of 1,150 senior business executives. Yet fewer than half of surveyed tech leaders (46%) said they were confident in their business’s transformation plan.
“Tech companies now have to do more with less,” says Sarah Glover, industry solutions marketing lead for technology and media, Workday. That’s why, she adds, they need enterprise management cloud systems that leverage artificial intelligence (AI) and machine learning (ML) to handle rote human resources and finance tasks, freeing up talent to take on more value-add work.
“These companies have to lean into automation,” Glover says. “The mundane tasks should be handled by systems, not people.”
Tech leaders know that adopting AI and ML technology platforms is mission critical. The problem is, many of them aren’t there yet. Many tech leaders can’t access the intelligence they need from a string of systems that individually may be top tier but don’t always work well together. In the Workday survey, only 21% of tech industry leaders expressed confidence in their ability to make data-informed decisions in real time.
Almost 3 in 5 tech industry leaders (59%) said the pandemic accelerated digital transformation—the most of any industry outside of retail, according to “Closing the Acceleration Gap: Toward Sustainable Digital Transformation.”
It might seem ironic: An industry that’s all about cutting-edge technology has low confidence in its own operational tools. But it also makes sense, given the industry’s evolution. “Tech organizations start as young, scrappy companies really concerned about costs. So they plug holes in the dam, getting the cheapest best-of-breed solutions,” Glover says. “But you can’t scale that.”
As early adopters, fledgling tech companies have taken a function-by-function, case-by-case approach to enterprise management technology—a people solution here, a finance solution there. “That leads to a lot of different point solutions, and the companies are collecting data from all of them but not tying that data all together,” Joseph says.
A single, unified system can tap data from across the entire enterprise to inform business decisions. And it can interface easily and seamlessly with other solutions. “This is a systems change but also an organizational change,” says Joseph.
Lack of data certainly isn’t the problem. The tech industry continuously collects data on its products, services, and customers. Where they lag is in data interpretation, Joseph says. “This is where AI and ML are so powerful because technology companies are sitting on the greatest amount of data out there. They just haven’t been able to tap into it.”
A central source of truth for data enables a tech organization to identify and deliver the products and services its customers need. By leveraging AI and ML, these companies can, for example, uncover the people skills they have and determine how best to use those skills to drive better business outcomes. AI and ML can tackle important yet time-draining tasks such as managing cash receipts or closing the books, freeing up human talent for higher-level work.
“If it’s not a unified system, there will be cracks that hurt the business,” Glover says. “And that ultimately will lead to lost revenue and unhappy customers.”
Tech layoffs have dominated headlines in recent months, but demand for tech talent still exceeds supply. In a Gartner survey conducted in late 2022, 86% of CIOs said they faced more competition for qualified candidates, and 73% worried about IT talent attrition.
To land top talent, tech companies must “meet workers where they are,” Glover advises. In part, that means adopting cloud-based platforms to give tech talent what they want: workplace flexibility. Tech talent overwhelmingly prefers working remotely over moving for a job, a 2022 McKinsey survey finds. Amid high turnover rates and the steep costs of replacing workers, the tech industry needs enterprise tools that can help attract and retain talent.
“AI- and ML-driven insights are so powerful because technology companies are sitting on the greatest amount of data out there. They just haven’t been able to tap into it.”
Justin Joseph
Senior Director, Product Strategy
Workday
Improved operational systems with better-integrated data can help employers determine the hybrid model that works best for them and their employees. People and business data can shed light, for instance, on the outcomes of product ideation that happens in person versus virtually. In turn, that can help companies decide when and how teams come into the office. “When their people come into the office, they have to make it meaningful for the activities that drive really clear, strong business outcomes,” Joseph says.
Tech organizations are also quickly evolving from roles-based to skills-based workforce planning to boost business outcomes and employee sentiment. Rather than viewing job roles as fixed and rigid, the industry increasingly understands that individuals have skills that can be accessed and developed to meet demands now and in the future. “What we see in tech is that it doesn’t matter what role you have, it matters what you can do,” said Glover.
Nearly one-third (32%) of tech leaders in Workday’s survey say their teams lack certain necessary skills. To have a better handle on what their people can and can’t do, tech companies need enterprise systems that provide a comprehensive view of their projects, resources, and people skills.
A cloud-native platform not only surfaces all the people skills that a tech company has, but it also can validate those skills by considering people’s work and professional development experiences. Using AI- and ML-driven skills technology, employers can track which projects employees have worked on, how much time they spent on a project, and what skills they may have gained in the process.
Nearly one-third (32%) of tech leaders in Workday’s survey say their teams lack certain necessary skills.
These insights allow tech companies to quickly determine whether their workforce has the necessary skills or if they need to start a search. Such agility is critical for the fast-moving tech industry.
Breaking down data silos must be a top priority for tech. A cloud-based, unified enterprise management system can tap the power of AI and ML to realize the full value of internal and external data. “Having data in one place, as opposed to data in fragmented systems, allows AI and ML to learn from itself and to run much more efficiently,” Glover says. “Tech companies have the data, but how are they going to make strategic decisions based on the data to drive growth?”
In the past, most tech companies considered their enterprise technology first, their data second. “That has to shift. They should think about data first and then the systems that support the flow of data in the best way possible,” Joseph says. “Tech leaders know the benefits of unified data. They know they need to transform. But they’re seeing that now is the time for them to do it.”
To learn more about how Workday helps technology companies with digital transformation, visit our website.
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