How does the MDPC improve master data quality more effectively than traditional MDM tools?

The MDPC connects master data directly to the processes it drives by using object-centric process mining. Instead of focusing only on static data checks, it reveals how incomplete or incorrect master data impacts real operational workflows. This enables Capgemini Invent clients to identify root causes, quantify financial impact, and prioritize the most meaningful improvements.

What benefits can organizations expect when deploying the MDPC?

Organizations gain transparency across all major master data domains and see how data quality influences process performance. This results in fewer process disruptions, more accurate reporting, and faster execution in areas such as purchasing, order management, logistics, and finance. These improvements can translate to reduced rework, enhanced automation readiness, and lower operational costs.

How does Capgemini Invent support companies during an MDPC implementation?

Capgemini Invent provides a full delivery framework that includes assessment, data onboarding, configuration, dashboards, and governance design. Teams support clients in interpreting insights, aligning stakeholders, and embedding continuous data ownership. Capgemini Invent also helps scale the MDPC across business units and connects it with broader transformation initiatives.

How does the MDPC use AI to enhance master data management?

The MDPC incorporates Celonis AI capabilities to detect anomalies, duplicates, and inconsistencies across master data objects. It provides intelligent recommendations and next-best- actions that help data stewards resolve issues faster. With automated checks and workflows, organizations can maintain consistently high data quality and support more reliable end-to-end automation.

Is the MDPC compatible with complex IT landscapes?

Yes. The MDPC integrates with SAP, Salesforce, and more than 50 additional systems, making it suitable for large organizations with diverse and distributed architectures. Capgemini Invent ensures smooth data integration and builds a unified view of master data across all connected applications, enabling consistent governance and harmonized data usage.

Master data management: For process performance and AI readiness

Andreas Nickmann, Erejon Kuci

Jan 22, 2026

Most automation and AI projects don’t fail because of technology; they fail because of bad master data.

For years, master data (e.g., supplier, material, product, and customer information) was an overlooked part of the background of enterprise operations. Rarely celebrated, it was usually addressed only to resolve an issue. Today, master data is starting to become a more prominent feature. As organizations accelerate investments in automation, AI, and end-to-end process optimization, the quality of the master data beneath these initiatives has become impossible to ignore.

Master data underpins nearly every business process. When it is incomplete or inconsistent, processes slow down, manual effort increases, and automation breaks. Even the most advanced tools cannot compensate for an unreliable master data management program.

Together with Celonis, Capgemini Invent has developed a new way to tackle this challenge. The Master Data Performance Center (MDPC) takes a process-first approach and turns master data into a measurable driver of business performance.

Master data management platforms matter more than you think

Master data is often viewed as a governance or IT topic. In reality, it has a direct impact on operational efficiency and financial performance.

Poor master data quality typically leads to:

High manual effort in procurement, finance, and supply chain
Missed cash discounts and overpayments
Capital tied up in inventory due to duplicate or inconsistent material data
Delivery delays caused by incorrect supplier or product information
Inconsistent payment terms that disrupt cash flow
Low automation rates and limited scalability

What makes these problems difficult to solve is visibility. Teams see the symptoms, but the root cause remains unclear. Master data issues are known, but rarely quantified or linked to business impact.

This becomes even more critical as organizations adopt automation and AI. Whether rule-based or AI-driven, systems depend on trusted master data. Without it, automation fails to scale and AI adoption stalls.

A new perspective: From managing data to improving performance

Traditional master data management focuses on maintaining data objects in isolation. Governance models are often rigid and disconnected from how data is actually used in daily operations. The Master Data Performance Center changes this perspective. Instead of starting with data models, the MDPC starts with business processes.

Built on the Celonis Process Intelligence Platform, the MDPC connects master data directly to core processes, such as purchase to pay, order to cash, and finance. This makes the impact of master data quality visible where it matters most.

With the MDPC, organizations can clearly see:

Where master data causes process bottlenecks and errors
How much manual effort and cost is driven by poor data quality
Which data attributes are critical for automation and efficiency
Where issues originate and how they spread across processes

With this increased visibility, master data stops being a static asset and becomes a performance lever that drives real impact.

From insight to action: How the MDPC creates real impact

At the heart of the MDPC is object-centric process mining. It provides an end-to-end view of how master data objects (e.g., vendors, materials, and customers) interact across systems and processes.

This enables organizations to:

Analyze processes across departments
Detect master data issues across multiple workflows
Trace root causes instead of fixing symptoms
Quantify financial impact such as rework cost or tied up capital
Monitor key KPIs like First Time Right, duplicate rates, and rework time

The MDPC does not stop at insights; it enables action. Using orchestration and action flow capabilities, organizations can trigger corrective workflows, notify responsible teams, automate routine fixes, or keep humans in the loop where needed. Improvements are continuously tracked to ensure lasting results.

One platform. Multiple roles. Shared ownership

Master data improvement only works if it is embedded across the organization. The MDPC supports this through role specific views.

Management View: Gives executives a clear overview of master data health and translates data issues into measurable business impact.
Tactical View: Supports analysts and managers with deep dives into domains, regions, and root causes to prioritize improvements.
Operative View: Empowers data stewards with diagnostics and tools to resolve issues directly at the source.

This approach turns master data excellence into a shared responsibility, not a siloed initiative.

Proven results across industries

The Master Data Performance Center is based on real transformation projects.

Challenges faced by our customers that were solved using Capgemini’s MDPC include:

92% cash discount usage through improved supplier master data in consumer goods
EUR 1.9 M in prevented annual sales losses through complete material data in retail
9% higher procurement automation through better catalog and material data alignment in automotive

These examples show how targeted improvements in master data unlock significant operational and financial value.

Looking forward: From cost center to performance engine

Master data has long been underestimated, often treated as a technical necessity rather than a strategic asset. The MDPC challenges that mindset. By linking data quality directly to process performance, financial impact, and AI readiness, it makes master data visible, measurable, and actionable.

The MDPC offers a practical, scalable path forward that turns a hidden liability into a durable source of competitive advantage.

E-book

Master data performance center

The MDPC introduces a new standard for master data excellence. It is measurable, actionable, and directly connected to business value. Explore the approach, architecture, and real…

Download now

Master data management Celonis-horizontal-book-download-report

Authors

Andreas Nickmann is Manager at Capgemini Invent with a strong focus on operational excellence and data-driven process transformation. He supports clients across industries by leading process mining and advanced analytics initiatives that deliver measurable business impact, optimize end-to-end processes, and enable sustainable enterprise transformation through intelligent automation and AI.

First name *

First name is not valid.

Last name *

Last name is not valid.

Company *

Company is not valid.

Email *

Email is not valid.

Country

Country is not valid.

Your message *

Your message is not valid.

Phone (optional)

Expert title

Slide to submit

Thank you for contacting. We will get back to you soon

We are sorry, the form submission failed. Please try again.

Erejon Kuci is an IT Consultant at Capgemini Invent, Germany, specializing in process mining, data-driven automation, and operational excellence. He supports clients in designing and implementing scalable analytics and AI solutions, leveraging object-centric process mining and advanced data analytics to drive measurable performance improvements and enable end-to-end business transformation.