With the ever-growing importance of scalable data and analytics, two key aspects of successful data system implementations are data governance and change management. These two elements are consistently seen together in strong, strategic transitions built for long-term effectiveness. However, when one element is ignored, either in part or entirely, organizations can be left scrambling to address issues quickly or even miss out on improvement opportunities altogether.  

Each element provides its own benefits – change management methods add momentum while solid data governance offers order – but both are required for meaningful and lasting change. These unexpected teammates rely on each other, and overlooking one will diminish the value of the other.  

Foundational Concepts 

Change management focuses on the human aspects of change to help drive adoption. By acknowledging that resistance is a natural part of change, friction can be reduced by intentionally addressing its sources. Effective expectation setting about what, when, and why the change is happening provides teams with clarity and can give a sense of ownership to the transition.  

Open communication with opportunities for two-way discussions can reduce ambiguity before changes are made. Defining what success after transition will look like with input from affected team members can ensure teams are aligned on both purpose and outcome. Ongoing training can help close knowledge gaps and ensure everyone feels competent under the new system. 

Data governance outlines standards around data acquisition, management, and quality. This provides a critical foundation for transparent and consistent data use across an organization while increasing its accuracy and value. Ensuring all team members are aligned and adhere to the same standards can protect data landscapes from losing reliability over time. 

Key outcomes of strong data governance programs include consistent processes, clear lineage, accountable ownership, and data definitions that are accessible at an enterprise level. This shared knowledge acts as a bridge between business and data teams, encouraging collaboration during periods of transition and building trust in data as a reliable source of truth.   

Change Management without Data Governance: Momentum without Foundation 

When change management principles are applied in the absence of strong data governance, it can lead to actions taken on unreliable or misunderstood data, unclear ownership and accountability, conflicting data definitions, and generally chaotic data use. Ultimately, these can all contribute to distrust in data. 

These effects can play out in a wide range of ways, including but not limited to: 

• Absence of metadata management: an R&D team makes a change to centralize their experimental data in a cloud platform, but legacy folder structure and a lack of tagging make it difficult to find and reuse data 
• Lack of data integrity and reliability: a supply chain team involves users in the development of a new inventory system with streamlined categorization logic, but historical data isn’t migrated or reconciled, so trend reports are incompatible and break down 
• Undefined sources and lack of data traceability: Teams are trained to use an AI-based chatbot assistant that summarizes literature for Medical Science Liaisons (MSLs), but it pulls from unreviewed preprints, creating reliability concerns and distrust in the new tool
• Inconsistent and unstandardized data: a data science team builds and socializes a demand forecasting model from product hierarchies that aren’t consistently maintained across departments, leading to a confusing rollout and eventual abandonment of the model 
• Distrust in data caused by unclear data definitions: a regional affiliate shares patient journey dashboards globally, but a lack of inclusion logic documentation leads to confusion on which patient cohorts are being tracked 

Data Governance without Change Management: Order without Guidance 

Systems with strong data governance but no cohesive push towards change can lead to governance being delivered “to” teams instead of “with” them. Without a shared vision and desire to adopt change, well-governed systems can end up being misunderstood, underutilized, or completely ignored. 

In practice, the outcomes of this lack of motivation to follow governance can look like: 

• Falling back on familiar datasets: a commercial team publishes a centralized catalog of real-world data sources, but team members continue purchasing third-party datasets independently due to familiarity and speed 
• Unused training resources: A company-wide AI ethics policy is released, but product teams developing customer-facing tools aren’t trained on how to apply the new framework 
• Ignored data discrepancies: an MDM system is updated to flag discrepancies in hospital affiliations for prescribers, but field teams aren’t given information on how to reconcile differences, so the data remains unused 
• New roles are ignored: data stewardship roles are formally assigned and documented, but teams continue escalating to former contacts and bypass the new structure entirely 
• Inconsistent adoption of improved support tools: a commercial team has access to centrally governed retail scorecards with performance benchmarks, but with no change strategy, adoption is inconsistent and key insights remain unknown   

Where Governance Meets Change: Successful Adoption and Maintenance 

It’s clear that leaving out either change management or data governance not only misses out on its own advantages but also diminishes the benefits of the other. Active and aligned behavioral change can’t drive value without strong underlying data policies and guidance. Similarly, even the best tools in the most ordered system don’t drive value if they aren’t tied with larger business goals and used consistently across an organization. 

These negative outcomes can be resolved or avoided altogether by using change management and data governance as a package deal, even boosting their impact when used together. Effective implementation of strong business strategy requires both people and organizational considerations.  

Organizations that have solid underlying data governance practices benefit from reliable and credible data with defined ownership, and stacking change management on top amplifies the effect by enabling teams to feel confident when using that data. This sense of responsibility and trust unlocks more value from data-driven decisions for the organization as a whole.  

When teams are actively involved in change with clear purpose and guidance, having a proper data governance system boosts the stability of underlying data and reduces confusion or technical issues with the initial transition. This in turn allows for smoother changeover with less friction and enables lasting change. 

For example, upgrading to SAP S/4HANA from SAP ECC requires careful attention to proper data preparation, including master data governance as well as a solid training and change management plan. A strong data governance program and change management implementation will help ensure that your data remains clean and relevant well after go live, even years down the road.   

An additional consideration here is the importance of organization-wide data governance, i.e. focusing beyond SAP.  An SAP data governance program is important for the relevance of that system, but an organization-wide program is important for efficiencies, accuracy, and compliance across your entire data (and systems) landscape. 

Looking Ahead: Data Governance and Change Management as Accelerators of AI 

Anticipating the growing benefits (and risks) of new AI technologies, companies that pair robust data governance with strong change management will gain more from these systems. Advancing these technologies with poor data governance in place brings the same risks of data distrust and misuse, but these will be repeated and amplified at a much faster pace than they were in pre-AI systems. Without change management, even custom AI tools purpose-built to expedite discovery or enable deeper insights will be poorly understood and underutilized.  

Organizations looking to move beyond the experimental phase of AI adoption into more mature and complex phases of stabilized and strategic AI use will not be able to support its increasing value potential without a solid underlying data and analytics foundation in partnership with championing change around AI tools.