This is part 2 of a multi-part series on the Analytics Operating Model.

In our recent blog, “7 Key Considerations to Establish & Evolve Analytics Capabilities”, we explored the essential elements of a capability positioned for growth. As we continue this series, we will dive deeper to explore each element of a sound analytics capability. We live in an era where data is plentiful and computing is powerful, but results have not yet lived up to the hype.

Taken together, analytics governance and organization structure should provide a framework for how decisions are made and by whom. This is easier said than done – Pressure on managers and executives to deliver on the promise of analytics is mounting, yet according to an April 2016 McKinsey Analytics Survey, 86% of executives say their organizations have been, at best, ‘somewhat effective’ at meeting the primary objectives of their data and analytics programs. It is tempting, under such pressure, to overlook the foundational elements of governance model and organization structure, but doing so will ultimately handicap your team.

To equip an analytics team for success, an organization must develop a transparent approach to governance and a streamlined organization structure. Together, these elements are the foundation for the other essential management processes, as demonstrated in Figure 1.


Figure 1: Effective planning and delivery processes such as value management and project lifecycle are built on the foundations of governance & organization structure


Every analytics organization, whether it be a small group of pioneers or a large and dedicated team, needs to address the decision-making process. A team that leaps into delivery without considering governance may face conflicting direction from stakeholders, or worse yet, be perceived as out of sync with enterprise strategy. Consider the example of a multi-national corporation that selected a supplier to pilot predictive models on market segmentation. Key decisions on what to pilot and with what supplier took place early – before an internal analytics team or governance was in place. Without the bare bones of governance in place, the supplier delivered the pilot in a vacuum with no stakeholders to affirm its value and no analytics team to validate its methods.

Analytics governance is, in many ways, like governance for any technology portfolio. It includes processes for managing demand, value, project lifecycle, and suppliers. However, the analytics portfolio differs in two key ways:

  • Lack of common understanding around delivery challenges: the value of analytics is well-recognized, but challenges of delivery are not.
  • The variable, non-linear progress of analytics projects: Analytics projects may progress at a rollercoaster pace, requiring agile delivery and frequent iteration to sustain stakeholder support.

These two differences require that decisions on the analytics portfolio – what projects move ahead, which suppliers deliver the work, who is ready to apply and use the results – be readily understood by business and technology stakeholders alike.

What is the solution? Consider a blended approach to governance, where both business and technology have a seat at the decision-making table. This contributes to transparency for analytics governance. For an organization that’s new to analytics, a blended approach also allows critical stakeholders to build their collective understanding, ultimately lessening impact of delivery challenges and the variable progress of analytics projects, as described above.


Organization Structure 

Likewise, a blended organizational structure, where both business and technology leaders are involved, can also prove effective. As shown in Figure 2, this may take different forms: distributed, embedded, or standalone. The decision on which model to use is driven largely by the analytics maturity of the organization and how widely demand is distributed across business domains. Including parallel efforts to add data science capabilities within individual domains can add additional complications.

Figure 2: Each of these organization structures support a blended business-technology approach, which multiplies the team’s ability to deliver


Regardless of the structure selected, the ability to deliver is multiplied when cross-functional business and technology teams work together – whether that is virtually, as in the embedded model or literally, as in the standalone alone. The key idea is that technology and business roles work together to build the portfolio and deliver on projects. Equally important, the blended governance and organizational models can be scaled over time. Scaling can also mean increasing committee membership as more business owners have a stake in the success of analytics.

Starting streamlined and small is a benefit for a capability that sits at the intersection of business and technology and holds tremendous promise for transforming how organizations work.

Click here to learn more about our Analytics & Information Management practice. Want to continue the conversation? Contact us at, and continue to follow this series as we dig deeper into each element of the Analytics Operating Model.