INDUSTRY BENCHMARK · 2026
The 2026 Life Sciences Technical Delivery Benchmark
How do teams in pharma, biotech, clinical research, and digital health actually get technical work delivered, and what slows it down? We are asking senior product, data, and engineering people across the sector.
What you receive
Early access to the findings
Every participant receives the 2026 Life Sciences Technical Delivery Report before it is published, so you can see where your team sits against the wider sample on delivery speed, resource allocation, and prioritization.
A small thank you
We send a small thank you in the post to everyone who takes part, where your organization's policies allow. We will email you to arrange it, and you are welcome to decline.
Your individual answers are seen only by the DataDrill team. Nothing identifying you or your employer is ever published or shared outside DataDrill. Please leave out proprietary or project-specific confidential information.
The benchmark
01
About you
So we know who to send your findings to.
So we know who to address your findings and thank you to.
Where we send the findings. We will also use it once to arrange your thank you. Nothing else.
Which area best describes your work?
Digital product or platform management
Data, analytics, or AI
IT, engineering, or architecture
Digital strategy or transformation
Operations or program delivery
Clinical or R&D technology
Other
02
Delivery reality
How technical work actually gets done in your team right now.
Which of these is your team working toward right now?Select all that apply.
Launching or scaling a digital product or platform
Consolidating or integrating data across systems
Migrating or modernizing infrastructure
Building analytics or reporting capability for internal users
Putting AI or machine learning into a real workflow
Improving data quality or governance
Automating a manual operational process
Integrating systems or teams after a merger or acquisition
Replacing or retiring a legacy system
Other
Where does delivery most often slow down for you?Select up to three.
0 of 3 selectedEngineering or data capacity is not available when needed
Requirements shift faster than the build can absorb
Data is scattered across systems that do not talk to each other
Data quality is not good enough to build on
Internal dependencies on other teams
Approval, review, or governance cycles
Legacy systems that resist change
Unclear ownership between business and technical teams
Procurement or contracting timelines
Knowledge sits with too few people
Infrastructure or environment constraints
Delivery does not usually slow down significantly
Other
What sits in your backlog because there is no capacity to start it?Select all that apply.
Data integration or pipeline work
Automating something still done manually
A prototype or proof of concept we cannot staff
Analytics or reporting we know users want
An AI or machine learning idea we have not tested
Cloud or infrastructure modernization
Documentation, knowledge transfer, or technical debt
Data quality or governance work
Nothing significant, we are keeping up
Prefer not to say
Over the past 12 months, how has demand for data and engineering work in your area changed relative to the capacity available to you?
Demand has grown much faster than capacity
Demand has grown somewhat faster than capacity
Demand and capacity have grown roughly in line
Capacity has grown faster than demand
Both have been broadly stable
Prefer not to say
When capacity is short, what actually happens?Select all that apply.
The work is deprioritized or deferred
Timelines are extended
Scope is reduced
Internal teams absorb the overflow
We hire
We bring in an external partner or contractor
We escalate for more budget or headcount
It depends entirely on the item
Prefer not to say
How confident are you that your delivery capacity matches your roadmap for the next 12 months?
1
2
3
4
5
Not confident, there is a clear gapFully confident, capacity matches the plan
Please keep it general. No project names or confidential detail.
If one constraint disappeared tomorrow, which would change the most for your team?
Engineering and data capacity
Data quality
Data accessibility across systems
Internal alignment on priorities
Budget
Governance and approval speed
Legacy system constraints
Specialist skills we do not have in house
Other
03
Working with external teams
How your organization works with external technical partners.
When your organization brings in an external technical team, which five factors matter most to you?Select up to five.
0 of 5 selectedTechnical competence
Understanding of life sciences workflows
Access to senior technical people
Clarity and consistency of communication
Clear scope and clear ownership
Reliability against agreed timelines
Commercial transparency and value for cost
Flexibility when requirements change
Proactive identification of risks
Documentation and knowledge transfer
Ability to work with our existing teams
Ability to work with our existing systems
Ability to start small and prove value before scaling
Security and compliance awareness in regulated environments
Overlap in working hours and responsiveness
Geographic proximity
Relevant evidence or references
If you had to choose only one, what creates the most confidence in an external team early on?
Technical competence
Understanding of life sciences workflows
Access to senior technical people
Clarity and consistency of communication
Clear scope and clear ownership
Reliability against agreed timelines
Commercial transparency and value for cost
Flexibility when requirements change
Proactive identification of risks
Documentation and knowledge transfer
Ability to work with our existing teams
Ability to work with our existing systems
Ability to start small and prove value before scaling
Security and compliance awareness in regulated environments
Overlap in working hours and responsiveness
Geographic proximity
Relevant evidence or references
Which three things most often go wrong with external technical teams?Select up to three.
0 of 3 selectedScope was vaguer than it appeared
Capability was oversold
Deadlines slipped
Costs came in above expectation
Communication was slow or inconsistent
The delivery team kept changing
Documentation and knowledge transfer were weak
They did not understand the industry well enough
No clear ownership when something broke
Process was too rigid when requirements changed
Poor fit with our internal teams
Not enough overlap in working hours
Nothing significant has gone wrong
Other
If your team needed outside help on something technical, what shape of engagement would be easiest to get approved?
A short assessment or scoping exercise
A small time-boxed piece of work with a defined output
A fully specified fixed-scope project
Specialists working inside our existing team
An external team owning a defined workstream
Advisory input without delivery
It would depend on the item
I would not be the person to judge
In your own words. This is the question we learn the most from.