Food & Ingredients · Operating subsidiary, functional ingredients

Data landscape analysis for a subsidiary in the food sector

Starting point

An operating subsidiary of an international food group wanted to implement AI use cases — and ran into a fragmented data landscape: ERP, LIMS, spec systems, layers of inherited Excel. Before a platform decision could make sense, the team needed clarity: what is there, what is missing, what has to be touched first.

What we did

We mapped the data landscape systematically — sources, owners, data quality, interfaces. Four prioritised target use cases tested against this landscape: what is feasible today, what needs prep work, what is not realistic. From that, a platform recommendation with investment frame. Disciplines deployed: data architecture, business-process analysis, platform assessment. One quarter, small team.

Results

1 quarter

from inventory to decision document

12+

data sources mapped (ERP, LIMS, CRM, spec systems, Excel layers)

prioritised target use cases tested against the data landscape

clear platform recommendation with investment frame

What we learned

A platform decision without a data-landscape analysis is a bet. Most tools can technically deliver what they promise — the question is whether the data layer carries them. This sequence — see the terrain first, then pick the platform — saves six- to seven-figure sums later.

This is the summary. How we approached it methodologically — which architectural decisions we made, what we discarded and which patterns can be transferred to other contexts — we discuss in a personal conversation.

Not because we want to sell you something. But because this depth is what our clients engage us for — and it does not belong on the open internet.