7 Feasibility assessment

You now have a data map and a list of use cases scored for business impact. The second scoring dimension is feasibility: how hard is it to actually build this?

Feasibility has two components, and you should score them separately.

Data feasibility — does the data exist, is it accessible, and is it good enough? Use your data map from Step 6 to assess each use case. A use case that requires attributes you know are missing or unreliable will score low here regardless of how valuable it would be.

Capability feasibility — does your team have the skills to build it? A churn prediction model requires different skills than a dashboard. A real-time alerting system requires different infrastructure than a monthly report. Be honest about what your team can deliver in the near term.

Score each dimension on a 1–5 scale:

Score	Label	Description
5	Ready now	Data exists, is accessible, clean, and has sufficient history. You could start building tomorrow.
4	Minor gaps	Mostly there but needs some cleaning, joining, or enrichment. A few weeks of work.
3	Achievable with effort	Data exists but is fragmented, poor quality, or hard to access. A meaningful engineering project.
2	Significant investment	Key data is missing or requires new capture mechanisms. A multi-month effort to get ready.
1	Not currently possible	Data does not exist and cannot be readily created. Requires new processes or systems.

The overall feasibility score for a use case is the lower of the two component scores. A use case that is a 5 on data but a 2 on capability is still a 2 overall — you cannot build what you do not have the skills for.

Keep the component scores visible in your working. A use case that is a 5 on data and a 2 on capability is a hiring or partnering problem. One that is a 2 on data and a 5 on capability is a data engineering problem. These have different solutions and different timelines.

Example — B2B SaaS

Stage	Question	Data	Capability	Overall	Notes
Signup	Where are people dropping off in the signup flow?	5	4	4	Product analytics often captures this well.
Ongoing usage	Which disengaged users are about to churn?	3	2	2	Churn model + clean cohort history — capability often binds before data.
Renewal	Which accounts are at risk of not renewing, and what should we do?	3	2	2	Next-best-action on top of churn risk raises the bar on skills and MLOps.

Use your data map from Step 6 to score every use case — the rows above are examples of how data vs capability can diverge.

Example — Non-bank lender

Stage	Question	Data	Capability	Overall	Notes
Application	What is application volume by channel and how is the mix trending?	5	4	4	Core ops data — often the easy part.
Document collection	Which applications will miss settlement targets unless we intervene?	3	2	2	Prediction on timing usually needs history + ML you may not have in-house.
Servicing	Which borrowers show early signs of stress before they miss a payment?	3	2	2	Early-warning features are powerful when data and modelling capacity line up.

Score the full use-case list the same way — overall feasibility is the lower of data and capability.

If your capability scores are dragging down use cases you know are worth building, that is exactly the problem our platform is designed to solve. TrueState gives teams without large ML engineering headcounts the ability to build the predictive and prescriptive use cases that would otherwise sit in the strategic bet column indefinitely.

For teams that have completed this strategy process, we offer free onboarding — usually priced at $35,000. Book a walkthrough to see how it works.