To make trusted, decision-ready data as normal in pharma as a validated lab method.
I help pharma and biotech commercial teams, from forecasting to market access to competitive intelligence, get answers they can trust from their data, then put automation and AI on top of it with confidence.
Start with a four-week, fixed-fee Data Readiness Review.
Do you recognise these?
- Different teams give different answers to the same question
- Data and models spread across different programs and formats
- Data sets you pay for don’t join up
- Pressure to adopt AI without confidence in the data underneath
What it costs
The cost is hidden: analyst hours spent reconciling, duplicate licences, slow refreshes, and decisions made on numbers nobody fully trusts. None of it shows up as one line, so nobody owns it.
“AI amplifies operational weaknesses. If it finds chaos, it accelerates chaos.”Speaker at the European Healthcare Future Forum, London, June 2026
The most expensive mistake is building an AI or automation project on data that can’t support it. You pay for the build, then pay again to fix the foundation underneath.
What changes when the data holds up
Less time and budget wasted
Analysts stop reconciling and start analysing. Duplicate subscriptions and rebuilt reports get found and cut.
More confidence in the right projects
You invest in what your data can support, and stop the wrong projects early.
Less risk in front of the board
No expensive AI tool or forecast confidently giving the wrong answer.
When to call me, and when not to
Google and ChatGPT are good at explaining how a tool works. They can’t see your data, talk to your teams, or tell you why finance and brand report different numbers for the same month.
Call me when:
- the problem spans several teams, systems or data sources
- nobody can say which number is right, or who owns it
- you’re about to invest in AI or automation and want to know the data can support it
- you need evidence your leadership will act on, not another opinion
You probably don’t need me if the question is how to use a specific tool. A search, or your IT team, will get you there faster, and I’ll tell you so.
Start where most companies are. Go as far as you need to.
Most commercial teams are not short of tools. They are short of data they trust. So the work starts with the foundations, then moves on to automation and AI once there is something solid to build on.
Foundations
The Data Readiness Review shows what is really wrong, what it costs and what to fix first. Four weeks, one fixed fee.
Automation
Fix what the review found: pipelines, models and reporting that run without manual steps, so analysts spend their time on analysis.
AI
Put AI on data you trust, with a clear view of what it can and can’t answer reliably.
If the review shows the root cause isn’t data at all, for example people or process, I’ll say so plainly and point to what would help, rather than sell you a build that won’t fix it.
What fixing the foundations has delivered
A launch-planning assistant that went from right 3 times in 10 to 19 times in 20.
Forecast errors cut tenfold, updated weekly instead of quarterly.
Less manual input categorising ClinicalTrials.gov data for pipeline analysis.
Examples from my work in pharma commercial data, including previous roles.
Who you’ll work with
Dan Roberts, PhD
Founder, Chord Intelligence. Based in Milan, working across Europe and the UK.
Fifteen years in pharma, including leading automated forecasting as an Associate Director at IQVIA, so you’re not paying for someone to learn your industry.
I’ve built the databases, data pipelines and automations behind commercial teams’ reporting, and managed projects from scoping through to adoption. I’ve seen the same pain points across many organisations, and I know which fixes last.
I work best with teams that have a named owner for the problem and want an honest answer, even when it isn’t the one they expected.
How I work
Nobody runs an assay they haven’t validated, yet forecasts are routinely built on data nobody has checked. Three principles shape how I work with commercial teams.
Critical thinking
I ask why before how. I question assumptions, the client’s and my own. I want to understand the context and desired outcome behind the request. I’m not afraid to change when new facts emerge.
Root cause, not symptoms
I fix what’s actually broken, not what’s visibly broken, even when that means a smaller project.
Honesty and ownership
I’m clear about what I know and don’t, I raise problems early, and if the real cause sits outside my scope, I say so.
Ready to start?
Choose the way in that suits you. All three are free, and if a review isn’t the right fit, I’ll say so.
Take the friction check
Twelve quick questions about how your team works with data, in about five minutes. You get one habit to start this week.
Take the free friction checkBook a call
30 minutes to talk through what your team is trying to decide and where the data gets in the way.
Book a free call