Future-Proof Your Life Science Commercial Data Before Launch
I had the opportunity to speak with Mark Henning, Founder and CTO of 2516 Technologies, about the evolving role of AI in life sciences and enterprise operations. Mark brings deep expertise in data analytics, digital transformation, and enterprise technology innovation, with a strong focus on helping organizations transition from legacy systems to AI-ready infrastructures that support data-driven decision-making and operational excellence.
A central theme of our conversation was that AI readiness and compliance readiness begin with the underlying data foundation. Weak data strategies do more than create reporting inefficiencies — they limit what organizations can automate, govern, scale, and ultimately trust. This perspective is especially relevant for pre-commercial and newly launched life sciences teams, where early commercial data decisions can become increasingly difficult and costly to unwind over time.
Readers may also request a complimentary "Commercial Data Health Check" from 2516 Technologies. This structured assessment provides a readiness score across key areas including data ownership, reporting consistency, governance, AI readiness, and launch scalability.
Enjoy Mark's insights below.
Murphy's Law implies "what can go wrong, will". It's cliche but cliche's survive for a reason. With a weak data strategy, a lot can go wrong. That matters for pre-commercial and newly launched life sciences teams. Selecting a CRM, a vendor enters the picture, you create a few KPIs, reporting needs evolve, then AI use cases get added to the conversation.
The risk is when those decisions happen in pieces, without a clear commercial data foundation tying them together.
Beghou's Biopharma Commercialization Research Report points to a familiar pattern: companies are investing in the right areas: data infrastructure, AI, and cross-functional planning, etc... but those efforts are not always moving in sync.
The Lack of Coordination Creates Limits
That lack of coordination can create limits that show up later:
- Expensive vendor dependencies
- Reports that do not align
- Unclear ownership of business rules
- Manual workarounds that become permanent
- AI use cases that sound good but are hard and expensive to support
- Compliance workflows that are hard to govern and audit
- Decision lags when requesting data
Be Intentional Early
The answer is not to overbuild before launch. It is to be intentional and pressure-test the foundation early, while decisions are still flexible.
A few questions can reveal a lot:
- Which systems are the source of truth?
- Who owns the key commercial data definitions?
- How many people are involved to change reporting requirements?
- Can reporting change without constant manual cleanup?
- Is the data structured well enough to support future AI use cases?
- Can the team explain where key information came from and why it is trusted?
The Real Impact
Bad data strategy does not only create messy dashboards. It limits what the business can build, automate, govern, and trust later.
To take a complimentary Commercial Data Health Check, contact 2516 Technologies at www.2516technologies.com.
Mark Henning is the Founder and CTO of 2516 Technologies: mark@2516technologies.com