You joined Gain Theory earlier this year – what attracted you to the company and the role?
Gain Theory’s ambition to accelerate client growth by putting data-informed insights at the heart of every investment decision they make was compelling. It felt like the right place for me contribute my skills.
Prior to joining, I led global data, analytics and insight functions across complex agency and client ecosystems. I focused on making data genuinely useful, not just technically impressive, by building strong foundations that enable smarter, faster decisions at scale. This is something I feel will benefit the brands Gain Theory works with.
What are your priorities as Head of Data?
There are three I’d highlight: ensuring data continues to be trusted, flows efficiently and at speed, and that we continuously evolve our data capability. I want to ensure data is a strategic asset: AI-ready, automated where it adds most value, and structured so it can support innovation and differentiation.
I’m particularly excited about ensuring that quality and reliable data underpins the insights our analytics teams model and the outcomes our clients ultimately act on. For me, the real thrill is seeing data move beyond infrastructure to shape growth, positioning, and new business opportunities.
What should brands do to ensure they have AI-ready data while protecting themselves from AI-driven data “pollution”?
Being AI-ready starts with getting the basics right. You need strong upstream data quality: automated checks, deduplication, and validation to stop ‘garbage in’ before it reaches models and produces ‘garbage out’.
Observability tools, such as dashboards that track missing values, sudden spikes, or bot-like activity, help teams to spot issues in real time before they contaminate analytics or AI outcomes.
On the governance side, clear policies around AI data use, such as approved sources, privacy compliance, ethical guidelines, are essential, backed by auditable logs for every transformation. AI-readiness is not just about rules set up by the data function; it is a team sport. Marketing, analytics, and engineering all need to understand the risks of AI-driven noise and bot traffic.
Finally, being AI-ready is a mindset, not a one-off exercise. Brands need to be constantly monitoring and evolving as threats and opportunities shift.
Data fragmentation and integration issues are challenges that marketers continue to face. How can they move to a single source of truth that provides the confidence to make business decisions?
Start by focusing on the data that matters most to business outcomes and accept that a partial single-source-of-truth is often enough to get started. Implement a system of rapid feedback loops between domain experts, analysts and engineers to ensure gaps are spotted and fixed quickly.
On the technical side, a centralized model, with automated quality checks and lineage tracking, will help keep data consistent and traceable. Iteration is key: unify in stages, learn fast, and gradually build a reliable, impactful source of truth across the organization.
What book would you recommend to our readers and why?
An insightful read, initially shared by a peer of mine, is Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage by Douglas Laney. This book really inspires the notion of data as an asset, not just as a by-product of operations.
It provides practical frameworks for measuring data’s value and turning it into strategic advantage. If you are leading data functions or trying to make the case for data as a strategic asset then I suggest giving this a read.
Contact Tasha to discuss any of the topics in this Q&A.
