Most ERP transformations fail to their meet intended goals
Everyone’s heard the sobering statistics again and again. Depending on which research you reference, between 70-80% of ERP transformations fail to meet their intended goals. In response, many transformation programs approach data as a source of risk, complexity, and cost that must be managed and mitigated. Data migration becomes a checkbox exercise, data cleansing a grudging necessity, and master data management an afterthought. This defensive posture misses a fundamental opportunity.
Over 50% of transformation programs explicitly cite improving data and analytics, enabling better decision-making, and driving operational efficiencies as core objectives. These goals are inherently data-driven, yet the data management strategy often receives secondary attention compared to system configuration and process redesign.
The reality is that data works hand-in-glove with your ERP system to generate value. Your ERP system should be configured to generate fit-for-purpose, decision-grade data that enables robust reporting, sophisticated analytics, and informed decision-making. When executed properly, this creates a virtuous cycle where better data leads to better decisions, which in turn generates more valuable data.
From risk management to value creation
The challenge facing transformation leaders is shifting the conversation from “How do we prevent data from derailing the program?” to “How do we maximise data’s value and demonstrate ROI?” This requires a fundamental reframing of how we approach data during ERP implementations.
Traditional data management approaches focus heavily on technical challenges: ensuring data integrity during migration, establishing data governance frameworks, and implementing master data management solutions. While these elements remain crucial, and are widely documented, they represent only half the equation. The other half involves understanding and measuring how data contributes to business value creation.
A strategic framework for data-driven ERP success
Whether you’re planning a new program or midway through an existing program, consider incorporating these three steps into your ERP data management strategy to transform data from a cost center into a strategic value driver:
Step 1: Map data to business value
Begin by identifying which data elements are most critical to ensuring the business activities and processes your new ERP system enables work efficiently. This isn’t simply a technical data inventory, it’s a business value mapping exercise. Which data drives customer satisfaction? Which information enables operational efficiency? What metrics directly correlate with revenue generation or cost reduction? Involving activity and process owners early on will make it easier when it comes to measuring benefits emerging from better data.
Step 2: Measure data’s impact
Once you’ve mapped your critical data elements, assess how effectively your current data supports these business activities. This baseline measurement serves multiple purposes: it helps prioritise migration efforts to maximise value realisation, identifies immediate opportunities for improvement, and establishes metrics for measuring transformation success.
This step often reveals surprising insights. Organisations frequently discover that data they assumed was critical provides limited business value, while information they overlooked significantly impacts operations. These insights enable more strategic migration planning and resource allocation. If done early on in the program lifecycle, this analysis can then inform prioritisation of data improvement and governance initiatives, to ensure the most impactful data is addressed first.
Traditional data management approaches focus heavily on technical challenges: ensuring data integrity during migration, establishing data governance frameworks, and implementing master data management solutions. While these elements remain crucial, and are widely documented, they represent only half the equation. The other half involves understanding and measuring how data contributes to business value creation.
Step 3: Plan for measurable improvement
With your baseline established, develop specific plans for improving your data’s condition to meet defined standards as part of your transformation program. These improvements should be directly tied to the business activities and associated KPIs identified in Step 1, creating clear linkages between data investments and expected business outcomes.
For example, ‘once our supply chain data meets 90% of its business requirements, we expect our order fulfilment cycle time to reduce by 10%’. Making the condition of data tangible in the context of expected outcomes will help demonstrate return on investment and realisation of expected process efficiency benefits.
Maximising the return on data investment
One of the most powerful aspects of this approach is its emphasis on measurement. By establishing baseline metrics for how data currently supports business activities, you create the foundation for demonstrating tangible return on investment from your data management strategy.
This measurement capability addresses one of the most common challenges in justifying data investments: proving business value. When you can demonstrate that improved customer data led to a 15% increase in cross-selling success, or that enhanced inventory data reduced stockouts by 25%, data transitions from a cost center to a recognised value driver.
By understanding the value of your data before you change the system that houses it, you can make informed decisions about where to invest time and resources during your transformation. You can prioritise high-value data for careful migration and improvement, while potentially archiving or eliminating data that provides limited business value.
Anmut specialises in measuring data’s impact on organisational goals. Whether you’re planning an ERP system migration, or looking to increase the return on an existing transformation program, get in touch to find out how you can maximise your data’s value.