This is part 7 of a seven-part series on the Analytics Operating Model.
Throughout our blog series on the analytics operating model, we’ve explored the challenges of building an analytics capability, and now we turn to the question of how to incrementally increase maturity – all while continuing to deliver value. The Enaxis Operating Model, shown in Figure 1, is structured as a complete, interdependent framework, but often, large enterprises have existing pockets or silos of analytics capability and then discover the need to synchronize these capabilities.
Figure 1: Our Analytics Operating Model identifies all of the elements needed for a mature, fully capable organization.
Analytics maturity is a composite of all elements in our operating model, although it can be tempting to focus on only a few. This type of limited focus can be driven by the need for a quick win or a visible change, but ultimately, it may not move the figurative needle towards improved capability.
Consider the retail organization that matures its marketing data platform with a multi-year and costly roll-out of the latest technology. The focus on a platform upgrade may be done with the best intent: believing that this elevates analytics capabilities. Unfortunately, without a focus on the related elements of governance or talent, the new platform doesn’t deliver as depicted. This is how the true value of analytics may go unrealized. The progression towards a mature operating model requires clear vision for building across these elements, as well as sustained executive support. It takes the right talent, likely with an associated investment to hire and train resources or source supplemental talent. All of this is built on a robust technology platform with standardized data management processes.
The progression towards a mature operating model requires clear vision
for building across these elements, as well as sustained executive support.
Right Capability, Right Time
The shift to enterprise analytics capabilities often uncovers pockets of analytics capabilities, data silos, and a variety of tools spanning across business units. You will need to make difficult decisions to successfully move toward a unified and integrated capability. The decisions should be driven by organizational need and use cases, rather than the possibility of new tools, algorithms, or even data. Reaching a balance between what’s needed and what’s possible takes deliberate action, an honest assessment of current state, and a pragmatic roadmap. In this way, your analytics organization can successfully engage the entire enterprise on this journey to a mature analytics capability.
Appetite for Change
Publications and mass media are awash with examples of our tentative human appetite for the change that is possible with analytics. McKinsey Global Institute examined more than 800 jobs across a multitude of sectors to identify those most at risk of being overtaken by a machine – whether that be a robot, or a data-driven model. The potential for resistance and tension is significant, as business and technology teams understand new or changed roles. Thus, the progression towards more mature capability depends in part on your organization’s appetite for the change that will be introduced. Enaxis views the levels of analytics maturity across each element of the operating model to support the progression from descriptive to diagnostic, predictive, and prescriptive capabilities. At the midpoint of this continuum, we delineate the Integrated State: the transition where analytics capabilities move from silos within each function to a shared vision across the enterprise. This is also the point where appetite for change may drive how quickly those advanced states of predictive and prescriptive analytics are reached. End users of the analytics solution must be ready to adopt and champion the changes to their roles and responsibilities.
Figure 2: The Enaxis Analytics Maturity Model shows the progression of capabilities to support the various types of analytics.
The roadmap to analytics maturity, described in Figure 2, is a journey starting with a clear understanding of the organizational need, followed by a logical progression that brings both the analytics team and the business users on that journey together. With a deliberate approach to introduce the right capabilities for the organization and a focus on managing the change, the momentum for analytics will propel the organization toward a data-driven culture.
Click here to learn more about our Data & Analytics practice. Want to continue the conversation? Contact us at firstname.lastname@example.org, and refer to the other posts in this series where we explore each element of the Analytics Operating Model.
Part 1: 7 Key Considerations to Establish and Evolve Analytics Capabilities
Part 2: Equipped for Success: Analytics Governance and Organization
Part 3: Bring the Horsepower: Developing Analytics Talent and Capability
Part 4: The Art and Science Behind Analytics Processes
Part 5: Data Oriented Architecture: Laying the Right Foundation
Part 6: Governing and Managing Data in the Big Data Era