MDS → CluedIn (Part 2): Best adoption approach and the mindset change

Audience	Time to read
MDM Lead / Data Steward Lead / Solution Architect	12–20 min

The core shift: hub mindset → mastering + distribution mindset

MDS programs often become:

“the place where master data lives”
“the UI where changes happen”
“the view we publish to everybody”

CluedIn adoption works best when you treat the output as a product:

master across sources
improve continuously
publish explicit contracts through Streams to Export Targets

MindsetShift

Best approach to adopting CluedIn after MDS

1) Start with consumers and contracts (not with “porting the model”)

Instead of asking “how do we model this like MDS?”, ask:

What is the mastered business concept (Customer/Product/Supplier)?
Who consumes it?
What shape does each consumer need?
What cadence and delivery method do they require?

CluedIn makes publishing a first-class concern via:

Streams configuration (what/how/when)
Export Targets
Modes like synchronized vs event log

2) Separate “mastering truth” from “consumer representation”

A frequent MDS anti-pattern is trying to force every consumer into one representation.

In modern MDM:

the mastered truth stays consistent
each consumer gets a contract-appropriate representation (codes, formats, fields)

This reduces downstream one-off transformation chaos.

3) Move publishing earlier than you feel comfortable with

If you wait until mastering is “done”, you’ll discover late that:

consumers need extra fields
keys don’t match expectations
latency assumptions are wrong

Instead:

publish a minimal contract early
validate with one consumer
iterate

4) Treat distribution as an operational system

If you adopt CluedIn seriously, distribution needs:

idempotency rules (safe repeated updates)
error handling and retries
monitoring of export targets/stream health (CluedIn performs health checks before starting streams)

What users need to expect will be different

“Where do I edit data?”

In MDS, editing often centers on the hub UI. In a modern approach, editing is a capability—not the center. Expect a split between:

operational systems where changes originate
stewardship and governance processes
mastering rules and quality logic

“Where do rules live?”

In MDS, rules often act as a gate at entry time. In modern MDM, rules are often split:

contract-level rules (what must be present to publish to a consumer)
quality rules (what must be improved/flagged)
survivorship rules (who wins per attribute)

“How do we release changes without versioning locks?”

You can still operate in release cycles, but modern publishing uses:

contract gating (publish only what meets requirements)
state/lifecycle control
the stream configuration as your controlled release mechanism

Adoption anti-patterns (avoid these)

Port everything 1:1 Every legacy hierarchy and rule is not sacred. Most are accidents of history.
Design only for one consumer You’ll rebuild the view sprawl in a different form.
Ignore operational integration No idempotency, no DLQ pattern, no monitoring → guaranteed pain.
Make stewardship the bottleneck again Stewardship should fix exceptions and guide improvement, not become “the only way data changes.”

A pragmatic 90-day adoption shape

Days 0–30: prove a contract end-to-end

ingest (MDS integration or SQL ingestion)
model 1 entity
publish 1 contract through Streams + Export Target

Days 31–60: improve mastering + add a second consumer

add matching/survivorship refinement
publish a second contract with different requirements

Days 61–90: cut over one consumer

dual-run comparison
cut-over
repeat

Next: Part 3 is an FAQ + a worked example that maps typical MDS use cases into CluedIn patterns.