The hidden cost of poor master data
One of the main challenges is that the cost of poor master data is rarely visible. It does not appear as a clearly defined line item. Instead, it accumulates through a multitude of small but persistent inefficiencies embedded across processes and teams – and often accepted as part of “how things work”. In reality, organisations lose significant amounts of time every week correcting, validating and reconciling data. According to Gartner[1], teams spend 15 to 20 percent of their working time on data correction and related activities, amounting to millions in productivity losses each year. The consequences of incomplete or inconsistent data – often across systems and channels – go far beyond operational inefficiency: Product launches are delayed. Customers lose trust. Regulatory reporting becomes more complex and more error-prone. Ultimately, all of this has a direct impact on financial performance.

New developments are amplifying the problem
Taken individually, these issues may appear manageable. In combination, however, they create substantial and ongoing cost. What makes the situation even more critical is that these effects are not static. On the contrary, they are being intensified by several structural developments:

• AI and automation
  AI and automation are placing fundamentally new demands on data quality and structure. Contrary to common assumptions, AI initiatives rarely fail because of algorithms. More often, they stall because the underlying data is inconsistent, incomplete or poorly structured. While humans can interpret incomplete or incorrect information, AI systems cannot. Consider a product that is assigned to the “Outdoor” category in a commerce system but to ‘Sporting Goods’ in an ERP system. A human may still recognise that these categories are not mutually exclusive. For an AI system, however, this inconsistency creates ambiguity. In AI-driven forecasting or pricing models, this can lead to different calculations of demand and revenue – directly affecting planning and pricing decisions. AI systems rely on explicit, well-defined data structures to produce reliable outputs. Without this foundation, even well-designed models struggle to deliver value.
• Agentic commerce
  This dynamic becomes particularly visible in emerging environments such as agentic commerce. In these settings, AI systems actively select, evaluate and recommend products based on available data. Here, missing attributes or inconsistent classifications do not just reduce performance – they can prevent products from being displayed altogether. Data issues that could previously be worked around now have a direct and often immediate impact on business outcomes.
• Regulatory and market pressure
  At the same time, regulatory and market pressures are intensifying. Expectations around product transparency, sustainability reporting, labelling and traceability are becoming more stringent – and more consistently enforced. Meeting these requirements depends on consistent, well-governed data. Without it, compliance becomes reactive, manual and increasingly risky.
• Speed as a competitive factor
  Finally, speed has become a decisive competitive factor. Organisations are expected to bring products to market faster, reduce time to (digital) shelf, onboard partners more quickly and make decisions in near real time. Inconsistent master data slows down each of these processes and increasingly acts as a direct constraint on growth.

Why master data still lacks strategic priority
Despite these developments, many organisations still struggle to treat master data as a strategic priority. This is often due to persistent assumptions: that master data is primarily an IT topic, that its benefits only materialise in the long term, or that governance introduces unnecessary complexity.
In practice, the opposite is true. Without clear ownership and structured governance, data issues cannot be resolved sustainably. Instead, they grow with the organisation. As companies expand into new markets, introduce additional systems and increase their operational complexity, inconsistencies multiply and become harder to manage. Worse still, these challenges do not occur in isolation. They reinforce each other and create a cycle of inefficiency that becomes increasingly difficult to break.
At this point, it becomes clear: doing nothing in terms of Master Data Management is not a viable option. Master data is a strategic factor – and therefore a responsibility at the executive level. How directly this impacts business performance becomes evident when looking at a real-world example. 

Practical example: scaling data-driven operations
As an energy innovator, PAUL Tech AG (PAUL) develops advanced technologies that reduce CO₂ emissions and improve energy efficiency in buildings. These solutions rely on artificial intelligence and measurement data from building systems. When PAUL set out to transform energy efficiency in the real estate industry, it faced a significant data management challenge. Its business model required the ability to collect, process and derive value from vast amounts of data across numerous properties worldwide. This was not merely about storing information, but about creating a dynamic, scalable system that could transform raw data into actionable insights, support rapid global expansion and facilitate a fully digital customer experience. Against this backdrop, a scalable and flexible master data foundation became essential to support these requirements.

PAUL therefore required a solution that could scale from handling data for 100 customers to potentially thousands across multiple countries, while ensuring seamless integration across internal systems. It also needed to support the contextualisation of measurement data with property information, enable the unique identification of individual devices such as sensors and actuators, and provide capabilities for data verification, enrichment and cleaning. At the same time, immediate access to up-to-date data across teams was critical.

With this in mind, the company implemented a Master Data Management solution which enabled the following outcomes: 

• Significant cost savings in data maintenance
• Improved data accuracy through automation
• More efficient processes based on contextualised data
• Scalability to support global data management growth
• A flexible master data model that evolves with changing business needs

 
From hidden cost to measurable value
Organisations that successfully address master data take a fundamentally different approach. Rather than starting with technology, they begin by identifying where poor data creates friction in the business – and what that friction costs. They analyse where time is lost, where processes are delayed and where risks arise. Based on this, they link improvements in master data directly to measurable outcomes, such as reduced operating costs, faster time-to-market, improved compliance and better-informed decision-making. A centrally managed and well-governed master data foundation enables consistent and reliable information across systems and teams. It reduces recurring operational effort, minimises manual corrections, supports automation and creates the structural basis for scalable AI and digital initiatives. In this context, master data management is not about maintaining data. It is about improving how the organisation performs.

Conclusion: A strategic decision, not a technical upgrade
The key question today is no longer whether better master data would be beneficial. It is whether organisations can afford to continue operating on fragmented, non-future-ready data structures. This is all the more critical as structural weaknesses do not remain static – they grow with the organisation. For this reason, master data is no longer just an IT topic. Managing it is a business decision with direct impact on performance, risk and growth. It has become a prerequisite for operational efficiency, regulatory security and sustainable growth. Organisations that address this early turn data from a potential constraint into a sustainable competitive advantage.
Those interested in a more detailed breakdown of how to identify and quantify the cost of poor data can find additional insights in the eBook “Reframing the Business Case for Master Data Management (MDM)” and the accompanying on-demand webinar (Build your 2026 MDM action plan.)

^{[1] Gartner, D&A Leaders Need to Own D&A Risk Management to Drive Business Value, ID G00822989, 19 February 2025.}

By Karim Iskandar, CEO International & Managing Director, Syndigo (www.syndigo.com)

Karim Iskandar heads Syndigo’s international business and is responsible for go-to-market strategy, sales, finance, operations, product strategy and partnerships. Prior to joining Syndigo, he worked at JRNI, a customer engagement platform that drives shoppers to in-store experiences via digital channels, where he launched the company’s business in both Europe and the USA. Karim thrives in hyper-growth environments. He began his career at Forrester Research during its expansion from $30M to $170M in revenue, as part of the European launch team and ultimately served as UK General Manager. Since then, he has held leadership roles across various software, technology and research companies – helping them launch, scale and grow rapidly on an international level.