Insights | Analytics & Insights

New Year, New Evolution

Resolutions for evolving your Data and Analytics Capabilities

Author: Ian Foley

The calendar has flipped to 2021 and with it comes a new year of opportunities for growth and innovation. Specifically, the new year is a great time to take inventory of your organization’s Data and Analytics (D&A) capabilities and plan for the next evolution.  Modern businesses require constant information to thrive. Thus challenging D&A teams to be responsive and support the ever-growing demands for information.  Taking a thoughtful approach to evolution can help provide the right technology, processes, and people to provide powerful organizational capabilities. I’ll detail some of the hallmarks of each stage and share some actions you can take to continue evolving.

Through my experience, I have seen organizations mature their Data and Analytics capabilities following a fairly similar cadence. With each evolution, organizations realize an increase in value from their investments in Data and Analytics. In most cases, I have observed three stages of evolution:

  1. Initial Success
  2. Distributed and Collaborative
  3. Ubiquitous and Advanced

Initial Success

Initial forays into Data and Analytics prove business value opportunities

The Initial Successes stage is usually a start-up effort for an entire organization or within a specific department.  These initial efforts tend to be focused on a few key use cases.  For example, a finance department creates a data store to consolidate critical financial and accounting data from multiple, disparate sources.  This increases visibility to more accurately forecast and realize actuals with minimal variance, which drives more efficient use of resources.

This stage typically involves adopting new technology, but that technology is typically not procured with the intent of supporting a broader Data and Analytics capability.  This stage can be really powerful in that stakeholders gain an understanding of the business value of better insights and they always want more.

To move beyond this stage and capitalize on the initial “win”, consider the following next steps:

  • Evangelize successes with business leaders by sharing quantitative benefits
  • Partner with IT and services partners to create a cross-enterprise data and analytics strategy
  • Evaluate technology and determine need for new functionality
  • Inventory potential use cases from across the organization and create a plan for execution of those that drive the highest ROI
  • Allocate budget specifically for investments in Data and Analytics

Distributed and Collaborative

Cross-organizational alignment drives increased capabilities and collaboration among departments.

The Distributed and Collaborative stage typically involves building on initial successes to create centralized capabilities to empower the broader organization.  Centralization inherently involves more stakeholders with differing needs and also identifies crossover data.  A central data store is created by taking into account shared data elements while enabling flexibility for unique uses of data across departments.  At this point, it is critical to establish sources of truth and agreed-upon data definitions. In addition, data champions exist in IT and as citizen data users in the business.

Key steps for evolving beyond the Distributive and Collaborative stage include:

  • Establish a data governance program and implement supporting technology
  • Allocate budgets to departmental data initiatives in addition to central efforts
  • Create a Data and Analytics leadership position that bridges the gap between business and IT
  • Tackle cross-functional use cases like Customer 360 to move successes out of departmental silos
  • Experiment with more advanced uses of data for machine learning and artificial intelligence
  • Establish an organizational data governance committee and build a citizen data worker community

Ubiquitous and Advanced

Advanced data capabilities integrated into every facet of your organization drive significant value and competitive advantage.

Just as in nature, evolution of your organization’s Data and Analytics capability is never complete. Over time, evolution becomes innate to your organization enabling your capabilities to constantly incorporate and react to dynamic changes in data, new technologies, and competitive business forces.  At this state, all members of the business rely on insights and visibility (the desired “data-driven culture”) and advocate for the continued investment and strategic importance of Data and Analytics capabilities.  Most importantly, this stage requires constant innovation, speed, and flexibility.

Keys to continued success in this stage are:

  • Adoption of technologies and processes such as DataOps, augmented analytics, and advanced data acquisition capabilities
  • External sources of data are relied on to create visibility beyond the walls of the organization
  • Investments in Data and Analytics capabilities are significant and grow each year in parallel with the measurable ROI that is delivered
  • Machine learning and artificial intelligence are embedded in business processes
  • Citizen data workers and engineers are fully empowered with data, tools, and processes
  • Stakeholders have fully adopted the language of data and rely on insights


Powerful Data and Analytics capabilities are table stakes to compete in the modern business world.  With thoughtful and committed leadership, data will become a key engine of growth for your organization.  A change of the calendar can be just another season of business, or it can be a launching point for evolution.  Why not make a business resolution to double-down on Data and Analytics?


Ian Foley co-leads RevGen’s Analytics and Insights practice. He is passionate about enabling his clients to establish and evolve a robust data-driven culture.


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