Software, consultants or BI?
Four ways to analyze spend, side by side.
Every team that wants better spend visibility weighs the same options. Each one is good at something. The differences show up in who does the data work, what happens as new data arrives, and what happens after the answer.
Side by Side
Where each approach shines, and where it stops.
Consulting project
A team cleans and classifies your spend once
- Good at
- A thorough baseline, expert judgment, and a clear report for leadership.
- Who does the data work
- The consultants, working from extracts your team provides.
- As new data arrives
- The analysis is a snapshot. Refreshing it usually means another engagement.
- After the answer
- Recommendations in a deck. Your team carries the work forward.
- Watch for
- Classification that is out of date a year later.
BI dashboards in house
Your analysts build reports on the warehouse
- Good at
- Consistent reporting on what happened, for many people at once.
- Who does the data work
- Your analysts and IT, including normalization and classification.
- As new data arrives
- Reports refresh. Cleansing and classification need ongoing upkeep.
- After the answer
- The work moves to spreadsheets for each renewal, letter or recovery.
- Watch for
- Messy supplier names and unclassified spend under clean looking charts.
General purpose AI tool
A language model pointed at your data
- Good at
- Reading free text and answering questions in plain language.
- Who does the data work
- Usually no one. The model reads the data as it is.
- As new data arrives
- Answers change with the data, and sometimes with the same question asked twice.
- After the answer
- A chat reply. Tools for the decision are up to you.
- Watch for
- Numbers generated by the model rather than calculated from your data.
Spend analysis platform
Data foundation plus tools for the work, like SpendCraft
- Good at
- A governed record of spend that stays current, with tools to work each opportunity.
- Who does the data work
- The platform. Your team reviews a representative set, not every line.
- As new data arrives
- Normalization and classification keep up. Scouts report what changed.
- After the answer
- Caddies work renewals, letters and recoveries to a decision. Spaces keep the work.
- Watch for
- Ask any platform where its numbers come from and test it on your own data.
Choosing between platforms? Use the buyer’s guide for what to evaluate and what to ask.
Questions
Common questions.
Can we combine these?
Yes, and many teams do. Consultants often use a platform during an engagement, and BI dashboards can stay for leadership reporting while the work moves into a platform.
Is a spend analysis platform just a dashboard?
A dashboard shows what happened. A platform also fixes the data underneath, keeps it current, and gives your team tools to act on what it shows.
Where does AI fit?
AI is good at reading messy descriptions and explaining results. In SpendCraft, proprietary AI reads, narrates and suggests, and deterministic code produces every number.
See it on your own data.
A Proof of Value works one category or one moment end to end.