As arguably the world’s most influential and innovative technology company over five decades, it’s natural that business leaders would look to Microsoft for guidance on how to survive and thrive in the digital age. Given that the company will reach a half-century in 2025, it is leading digital and data transformation by example.
Having transitioned from a gigantic multinational company lumbered with legacy infrastructure and data silos to a truly data-first company, Microsoft understands the challenges and pain points as well as any other organisation. Glen Robinson, National Technology Officer at Microsoft UK, is happy to draw on that experience and share the steps to data transformation success.
Step 1: Start with the desired outcome
The concise version of Microsoft’s advice to companies looking to either start or accelerate their data transformation journeys is “think big, start small, and act fast”—but that’s easier said than done. While a memorable mantra, what does this mean in practical terms?
“Microsoft started with clear outcomes front of mind,” says Robinson, who took up his role in November 2019. “Ask yourselves: why would we do this transformation, and how are we going to bring everyone on this journey?”
He stresses this puzzler was especially important for Microsoft to answer at the outset before committing to data transformation. “In terms of the data journey that we love our customers to go on, they need to identify and understand those outcomes they are working towards consistently,” Robinson says. “Ours was to unleash the power of artificial intelligence and equally enable our customers to do so. But for most organisations, it’s probably around a service that they provide to customers or citizens.
“When considering the application of technology, we think about the usefulness, for example, of AI and machine learning. We know first-hand, and appreciate every single day, the benefits of machine learning and how that could be applied to data.”
As an example, Robinson points out how investing in AI and machine learning has supercharged Microsoft’s cybersecurity evolution. “As you would imagine, Microsoft has some enormous datasets,” he says. “Our security business tracks over 24 trillion threat signals a day, and the security graph is enormous. We have gone way beyond the ability for humans to reason at speed over the amount of data. You can only use machine learning to develop insights and then to even take action over that data at the sort of speed that we’d want to.”
Step 2: Drive collaboration across the business and share data
As with any venture outside of a comfort zone, the initial stride is often the hardest. Therefore, having a destination in mind makes progress easier. “Taking that first step of selling the big, bold ambition around the use of AI and the benefit to the teams around better insight, better automation, better productivity, enabled us to go through more of a phased approach,” Robinson says.
“We worked with departments across Microsoft to pool all the data and centralise it into a data lake. From there, you can start to run analytics, measure and report, and drive transparency—all of which helps the different parts of the business to run processes better and add value.”
This sharing of data and breaking down of silos generates a “virtuous circle” within an organisation, suggests Robinson. Further, by being smarter, it reduces duplication of data and work, and improves the quality of the data.
Step 3: Improve accessibility of data insights and measure progress
The last step is creating a mechanism or platform that allows personalised, real-time data insights that empower business departments and individuals to be discoverable. “Data accessibility is critical,” says Robinson. “But for Microsoft, this is always underpinned by strong information governance—data security and privacy is our primary thought.”
He emphasises that data transformation is ongoing, a quest for “continuous improvement”, and measuring process is crucial. But, again, Microsoft’s approach is instructive. “AI is now ubiquitous across our organisation, but to get to this point many years ago, we understood that changes were needed within the business, hence the phased process to support that transformation,” Robinson continues.
“Once we had the strong foundations in place, we could measure against the initial outcomes. Next, we needed to ensure the data being used was solving the right problems, so we created scorecards, and that allowed—and allow—us to learn from the analytics, iterate and determine best practices.”
Offering a final word of advice for business leaders on their data transformation journeys, Robinson says: “I see the post-pandemic opportunity around data in the UK. The value around multi-stakeholder collaboration around data has never been clearer than it is right now in the mind of Microsoft’s customers.”
He adds: “We’re at the start of a fascinating journey where Microsoft has a huge amount of applicability and relevance. The innovation that we’re doing to enable this even further for our customers means there are exciting times ahead.”
This article was written as part of a paid-for content campaign with Microsoft
