From data silos to actionable insights: Innovating within legacy constraints for the digital enterprise
Market View
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Interview with
Christopher Rowntree
DIRECTOR - COMMERCIAL DATA ANALYTICS | PRODUCT MANAGEMENT
about
Christopher Rowntree
Christopher Rowntree held the position of Director - Commercial Data Analytics | Product Management. In this role, he initiated and led a new department called "Data Led Sales," which focused on developing software analytics tools that utilized big data, automation, and AI models to enhance sales effectiveness across the company's business units. These tools transformed the sales approach by automatically recommending optimal product and pricing combinations to customers, streamlining the sales process and improving overall efficiency.
How is your organisation approaching the challenge of extracting more value from its data while working within legacy systems?
Our primary strategy is built around a cloud data solution, particularly Google Cloud Platform (GCP) and Google BigQuery. We’ve integrated data from our legacy systems—SAP, billing, provisioning, and others—into a central cloud repository. This setup allows our data engineers and analysts to piece together a 360-degree view of the business.
There are two parts to this process. First, the data engineering team handles the integration of data into the cloud. Then, my team focuses on extracting value from that data. For this, we need people who not only understand the business but also have the technical and collaborative skills to drive results. One of our biggest successes has been building sales tools that leverage this data.
What do you see as the biggest obstacles to innovation when dealing with established technology stacks?
Surprisingly, the biggest challenges aren't technical. Tools today are much easier to use than 20 years ago, so the technical ‘plumbing’ is no longer the main barrier. Instead, the real difficulty is deciding which problems are worth solving. Organisations often have many opinions on where to invest—be it AI, analytics, or reporting. But too often, the loudest or most senior voice dictates the direction, which doesn’t always lead to the best outcomes.
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The future of data utilisation is certainly leaning towards AI. There's a lot of hype, and I believe it’s justified. Many organisations are just beginning to explore AI, treating it as a curiosity for now. However, I see its potential as truly transformative.
How do you balance the need for data integration across different systems with the risks associated with changing established processes?
By centralising all our data on Google Cloud Platform, we've largely mitigated the need for direct integration between legacy systems. IInstead of costly, time-consuming IT projects to integrate data into systems like Salesforce, we surface data from the cloud where it’s needed. This way, users feel as if all information is integrated, but in reality, it’s pulled from GCP and managed by our data engineering and analytics teams.
This gives us the benefits of having data centralised without the complexities of integrating legacy systems with each other. It creates the illusion of one unified IT system while keeping everything separate under the hood.
The future of data utilisation is certainly leaning towards AI. There's a lot of hype, and I believe it’s justified. Many organisations are just beginning to explore AI, treating it as a curiosity for now. However, I see its potential as truly transformative.
How do you retain these exceptional individuals once you've brought them on board?
Retention is a constant focus. A large part of my role is reminding the team of the purpose behind our work. I make it clear how the challenges they’re tackling are building their skills and contributing to their professional development.
Another key factor is ensuring our technical team feels connected to the business. I actively involve them with the end users of our tools to break down the isolation that often surrounds technical teams. We’ve also adopted a product management approach, treating our internal analytics tools like external products. This has significantly improved our performance.
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I encourage organisations to 'walk before they run.' Many of our capabilities are highly sophisticated without relying on AI.
What's your perspective on the role of AI in data analytics?
AI will undoubtedly be transformative, but I’m cautious about the rush to "AI everything." There’s still a lot of value in traditional analytics and product management that doesn’t require AI. Many of our current capabilities are sophisticated without relying on AI.
I encourage organisations to "walk before they run." Jumping straight into AI without first mastering the fundamentals often results in missed opportunities. AI is powerful, but the most effective solutions often come from a smart combination of traditional analytics and emerging technologies, rather than trying to apply AI to every problem.
If you want leadership to allocate significant resources, you need to clearly articulate the value. That doesn’t mean producing a hundred-page document, but it does mean having a well-defined business case that outlines the costs and benefits.
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