MDM Engineering
MDM Engineering: the data operating model that your company never designed.
MDM Engineering is our assessment that establishes your company's Master Data Management policy and strategy and designs a new data operating model: processes, roles, indicators and the Data Registration Center. Focused on results, not advice: you leave with a roadmap, RACI and measurable targets.
Trusted by 160 clients
Integrations
Pain
The problem isn't the tool. It's the broken operating model.
Buying software doesn't define who is accountable for the data. Without that, each area creates its own rule and the base reverts to its previous state within a few months.
Data with no owner
No one is accountable for the attribute definition, the approved exception or the quality indicator.
Rework as routine
Manual checking, parallel spreadsheets and tickets to fix what came in wrong take up the entire team.
Risk of fines
Incorrect tax classification, non-compliant suppliers and expired documents only surface when the auditors arrive.
Stalled project
ERP migration, analytics initiatives and now AI projects all get stuck on the same obstacle: master data without governance.
The assessment measures the cost of this model today, in risk, time and money.
The service
What MDM Engineering is.
It is a consulting assessment for medium and large companies that need to establish a Master Data Management policy and strategy and a new data operating model. It solves the model — process, roles, governance — not just the data that is wrong today.
Delivery is measured by results: a clear objective, agreed indicators, a defined roadmap and alignment with the company's strategy. No recommendation reports without an owner and without a deadline.
Where it fits among our services
MDM Engineering designs the operating model and the policy.
Data cleansing cleans and standardizes existing data. See cleansing
Data as a Service runs master data day to day. See DaaS
Data Migration migrates the base to the new ERP. See SAP migration
The six pillars
How MDM Engineering is structured.
Pillar 01
Maturity Map
Measures current maturity and translates each gap into business impact: risk, cost and time. This is where the project begins.
Pillar 02
Value Base
Eliminates waste in the existing process: steps that add nothing, rework, manual checking and queues with no owner.
Pillar 03
Value Forward
Designs value creation: what well-governed data enables in procurement, tax, operations and analytics.
Pillar 04
Assisted Delivery
Results governance: monitoring the roadmap execution, with indicators and course correction.
Pillar 05
Team Cycle
Handles data and processes together, with the teams that operate master data, not in a parallel document.
Pillar 06
Smart Scale
Scalable productivity: automation, a replicable standard across plants and countries, and an SLA that holds up at volume.
The six pillars become a roadmap with an owner, a deadline and an indicator for each front.
How it works
From AS IS to TO BE, in three moves.
The journey goes from an honest picture of your current operation to the business case that supports the board's decision.
01 · AS IS
Maturity Map
Maturity survey with the areas that create and consume master data.
Mapping of current processes and risks at each stage.
Data review: duplicates, completeness and compliance.
02 · TO BE
Value Structuring
Design of the corporate MDM model: policy, roles and approval levels.
Master Data Center model, with flow, SLA and sizing.
Processes redesigned without waste, with OKR and KPI per front.
03 · Decision
Business Proposal
High-level roadmap, in waves, with estimated gain per stage.
Plan to train the teams and automate what is repetitive.
Scalability across plants, companies and countries, with agreed SLA.
The assessment ends with a decision, not a presentation.
Deliverables
Documented, measurable and sustainable transformation.
Executive assessment report
A snapshot of current maturity, with gaps translated into risk, cost and time, in the board's language.
Responsibility matrix (RACI)
Who is responsible, who approves, who is consulted and who is informed, attribute by attribute and step by step.
Performance indicator map
The quality and process KPIs that start being tracked, with target and owner.
Data registration center model
Structure, flow, approval levels, SLA and sizing of the team that will operate the registration.
The artifacts stay with your company, for auditing and continuity.
Results
New model, new numbers.
85–90%
of operational waste eliminated
50%
efficiency gain in master data operations
25–80%
SLA reduction, depending on the domain
+1.500 h
of engineering and training delivered
In MDM Engineering projects, the effect shows up in risk and cash: elimination of fines and liabilities tied to master data, and savings of around R$ 4 to 5 million.
Intelbras
92% of gaps eliminated · 75% SLA · 57% productivity
Revenue of R$ 4.58 billion and SAP S/4HANA, with risk of fines from master data errors, high rework and siloed areas. The new operating model addressed all three.
Aço Cearense
91% of rework eliminated
Revenue of R$ 7.3 billion: MDM came in as a pillar of the S/4HANA roadmap, with a defined golden record, clear governance and automated support processes.
Wella Company
−78% customers · −87% suppliers · −84% materials
The onboarding SLA was excessive with an outsourced operation lacking specialization. With the model and the operation redesigned, it dropped across all three domains.
Percentages from real projects, adjustable to your scope and volume.
Contact
Let's measure maturity before proposing anything.
A specialist assesses how master data is operated today — processes, roles and indicators — and points out where to start the assessment. No commitment.
Av. Queiroz Filho, 1700, Torre E, Conjuntos 715 a 718
Vila Leopoldina, São Paulo, SP, 05319-000
Common questions