In his classic study, The Innovator’s Dilemma, Christensen highlights how successful companies can struggle to successfully transition when disruption hits their industry.

These companies, often optimized for serving their existing customers with incremental improvements (sustaining innovations), find themselves ill-equipped to handle disruptive innovations that initially target niche markets or offer different value propositions.

1. Applying Disruption to Data Governance
2. The Need for a “Heavyweight” Approach
3. When a “Heavyweight” Data Governance Team is Needed for Process Creation:
4. Key takeaway:

Applying Disruption to Data Governance

This phenomenon isn’t limited to product development; it applies equally to organizational changes, and implementing robust data governance can be viewed as a disruptive force within an organization.

Data governance often requires significant shifts in how data is handled, accessed, and utilized. It can challenge established data silos, introduce new roles and responsibilities, and necessitate changes to existing workflows.

For many organizations, this represents a fundamental change in how they operate, creating resistance and challenges similar to those faced by companies confronted with disruptive technologies.

The Need for a “Heavyweight” Approach

Applying the lessons of The Innovator’s Dilemma to data governance implementation may be crucial for success. Just as established companies need to create separate, more agile structures to nurture disruptive innovations, organizations implementing data governance might consider a phased approach, starting with a dedicated, cross-functional team—a “heavyweight” team in Christensen’s terms—to establish core processes and capabilities.

“On the other hand, if the ways of getting work done and of decision-making in the mainstream business would impede rather than facilitate the work of the new team—because different people need to interact with different people about different subjects and with different timing than has habitually been necessary— then a heavyweight team structure is necessary. Heavyweight teams are tools to create new processes—new ways of working together that constitute new capabilities. In these teams, members do not simply represent the interests and skills of their function. They are charged to act like general managers, and reach decisions and make trade-offs for the good of the project. They typically are dedicated and colocated.”

This passage from Christensen highlights a crucial point: when implementing something that requires new ways of working and new capabilities, a “heavyweight” team structure might be necessary, not for strict control, but for process creation. Let’s apply this to data governance:

When a “Heavyweight” Data Governance Team is Needed for Process Creation:

If implementing data governance requires significant changes to how data is handled, accessed, and used across the organization—essentially creating new processes and new capabilities—a “heavyweight” team structure, as described by Christensen, can become valuable. This is especially true in the early stages of a data governance program.

Here’s how this applies:

• New Interactions and Timing: Implementing data governance often requires different departments and individuals to interact in new ways, sharing information and making decisions in a coordinated manner that wasn’t previously necessary. A “heavyweight” team can facilitate these new interactions by:
  • Establishing communication channels: Defining how different stakeholders should communicate and collaborate on data-related issues.
  • Setting clear roles and responsibilities: Clarifying who is responsible for what aspect of data governance.
  • Creating forums for discussion and decision-making: Establishing regular meetings or working groups to address data governance issues.
• New Capabilities: Data governance often requires the development of new capabilities, such as:
  • Data literacy: Educating employees about data concepts, best practices, and policies.
  • Data quality management: Implementing processes for identifying, correcting, and preventing data quality issues.
  • Data security and privacy: Establishing controls to protect sensitive data.
  • Metadata management: Creating and maintaining metadata to improve data discoverability and understanding.

A dedicated, “heavyweight” team can drive the development of these new capabilities by:

• Developing training programs: Creating and delivering training to improve data literacy across the organization.
• Defining data quality rules and metrics: Establishing standards for data quality and developing metrics to track progress.
• Implementing data security and privacy controls: Working with IT and security teams to implement appropriate safeguards.
• Establishing metadata standards and processes: Defining how metadata should be created, managed, and used.
• Acting as “General Managers”: In this context, the “heavyweight” data governance team members act as “general managers” for data, making trade-offs and decisions for the good of the overall data governance program. They don’t just represent their function; they represent the organization’s data interests. This includes:
  • Prioritizing initiatives: Determining which data governance initiatives are most important and allocating resources accordingly.
  • Resolving conflicts: Addressing disagreements between different departments or stakeholders regarding data-related issues.
  • Ensuring alignment with business goals: Making sure that data governance efforts support the organization’s overall strategic objectives.
• Dedicated and Colocated (or Highly Connected): While physical colocation might not always be feasible, the team needs to be highly connected, with clear communication channels, regular meetings, and a strong sense of shared purpose. This ensures effective collaboration and decision-making.

Key takeaway:

The “heavyweight” team is not about imposing rigid control from the outset. It’s about building the foundation for effective data governance by establishing new processes, developing new capabilities, and fostering a shared understanding of data as a valuable organizational asset.

Once these foundations are in place, the organization can move towards a more distributed, “light-weight” approach, empowering business units to manage their own data within the established framework