Analytics operating model

Making the best and highest use of your analytics talent

In their quest to become an Insight Driven Organization (IDO)—those that turn analytics into a core capability by promoting a culture of data-driven decision-making—Canadian businesses have made significant technology and data investments. Yet until organizations are ready to engage the power of their people, IDO success will remain elusive.

In this four-part blog series, we explore the four ‘people’ levers organizations must activate to accelerate the development of sustainable analytics capabilities including: leadership; operating model; talent and capability development; and culture and change management.


Fierce competition for internal analytics resources. Ineffective deployment of analytics talent. Lack of collaboration or the sharing of best practices. Pursuit of isolated analytics projects that have limited effect on corporate strategy.

These are just some of the challenges organizations face when they fail to design an effective analytics operating model. Yet, despite the pitfalls, only one in five Canadian organizations has implemented an organizational structure that makes the best and highest use of scarce analytics resources, and ensures common tools and processes are developed and used across the enterprise.

To avoid these common missteps, it helps to learn from successful insight-driven organizations (IDOs)—those that have turned analytics into a core capability across the enterprise by promoting a culture of data-driven decision-making. At leading IDOs, analytics is treated as a living ecosystem where decisions around people, process, and technology are strongly integrated. In Canada, only 9% of Canadian organizations view analytics as a living ecosystem where decisions around people, process and technology are strongly integrated through an insight-driven mindset in only and the leadership of an enterprise shared services or Centre of excellence for analytics.

If Canadian organizations are to keep pace in a market characterized by disruption and constant change, they must adopt analytics operating models that enhance analytics maturity.

Operating model options
Every organization is different, so it should come as no surprise that there is no optimal analytics operating model. The goal is to make the best use of scare analytics talent and drive alignment to common standards and priorities in line with your organization’s strategy, goals, level of analytics maturity, and the supply and demand of organizational capabilities.

That said, at early stages of analytics maturity, organizations typically adopt either a functional or Centre of Excellence (CoE) model:

  • Functional models consolidate scarce resources in business units that have already developed the most advanced analytics capabilities. These functions then act as pioneers to develop common standards and instill an enterprise-wide analytics culture.
  • CoE models centralize their scare analytics talent by co-locating people in a cross-functional “hub”. They also generally centralize enterprise-wide activities ranging from strategy and governance to awareness and education, process standardization, project management, and execution.

Shared services and CoE models deliver additional benefits as well by giving analytics talent the opportunity to work on a wider range of analytics use cases. Notably, experience shows that analytics success rises when organizations select tangible use cases intended to solve a business issue and deliver measurable financial statement impacts. At the same time, the ability to work on diverse use cases enhances the attraction of analytics talent, encourages shared learning, and keeps always-curious data scientists motivated and engaged.

Adaptability is key
Although it can be tempting to assume that the operating model they select will see organizations through each level of analytics maturity, the truth is more complex. As organizations evolve, so too must their analytics operating model. The key, then, is to design for adaptability. One way to do that is by creating cross-functional teams and co-locating them in a bid to break down organizational silos and foster a hands-on mindset. By bringing together data scientists (people with ‘red’ technical and quantitative skills) and business experts (people with ‘blue’ business acumen and storytelling skills) to compose purple teams, organizations gain the agility to adapt their operating models in the face of rapid change.

This post on operating models is the second of a four-part series on the four ‘people’ levers organizations can pull to gain an analytics advantage: leadership; operating model; talent and capability development; and culture and change management. Tune in next week for part 3.

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