Spark needed a large-scale IT transformation to fulfill its new strategic direction. “It was hard, but it gave [us] a huge benefit: a byproduct called data,” Kallol says. “That led to the next evolution: developing customer insight.” It also created the foundation for Spark to embrace AI and effectively run on data. As Kallol explains, “Our interconnected data systems allow us not only to understand our customers better, [but to] drive decisioning in every part of the business.”

While many company data strategies focus on how to manage and use data effectively, at Spark the team recognized that there’s more to it. Kallol notes, “The foundations absolutely have to be there, but there [also] has to be some business value realization. Our strategy was to find that slice of data which provides value to the business, and then put it through the right level of governance, the right level of control, and make it available to everybody in the business. And pillar by pillar, we build that data map, so you realize the business value over time.”

New technologies have also played their part in transforming Spark. Kallol adds, “[AI and generative AI] both have huge potential and power in terms of enhancing the journey and experience for customers and employees.” They also offer the opportunity to embed data even further. He continues, “We are using generative AI for different use cases across the business and deploying them in a carefully structured and staged manner. For example, for data management, [such as] detecting anomalies, keeping our data safe, [and managing] the AI and data. They are so interconnected that [it’s also] about how you manage the models and keep them safe.”

Having the right talent in play is key to unlocking the value of the change. But how do you shape a talent pool when you’re moving from legacy, on-premises systems and data to a new, cloud-based way of working? For Spark, a hybrid approach was the answer. “We got the [new] talent we needed, but also established a reskilling program internally to train our own people to move into these roles of the future,” Kallol explains. “Reskilling is really hard, because it needs a lot of thinking, a lot of structure, and not everybody can be reskilled to the level we want in the future,” he adds. “But the combination works for us because we can keep the business IP, have [our people] develop new skills, and get the additional leverage of external skills.”

Asked about the role of metrics on the transformation journey, Kallol is quite clear: “I have found that leading metrics are sometimes more important than the “real” metrics,” he says. “Speed is probably a great leading indicator of both customer experience [and] productivity. Because if you can satisfy a customer request faster… if employees can find the information they’re looking for faster, that speed can essentially enable you to solve for multiple metrics. Finding the right metric to focus for the transformation is very important.”

With so much change happening across the business, how does Spark manage it effectively? “We realized the transformation was not about one area of the business,” Kallol adds. “So, it was the responsibility of the leadership team to be the owner. There are working groups who develop the thinking, but the overall governance sits with the leadership team.” The benefit of this is focus. “It creates leadership commitment,” he concludes, “and it brings the translation through to the organization. For me, it’s not only about ‘Are we on track?’ It’s also about helping the transformation to communicate, bring people along, and then change the workflow to realize the benefits.”