Challenge

A media company long known as an innovator was slowing down due to data and platform constraints, while competitors were catching up. Fragmented data on unstable platforms, low trust, and limited access hampered data-driven decision-making and sidelined AI initiatives. Leadership wanted to move into AI to keep pace with competitors, but the immaturity of the company’s data was a blocker. With data not previously treated as a business capability, the organization was leaving significant cost savings and incremental revenue on the table.

Diagnosis

As part of a practicum exercise, I led a simulated assessment of the company’s data maturity using the Data Management Capability Assessment Model (DCAM) v3. We scored data readiness across key domains and identified priority improvements and potential AI initiatives aligned to business goals:

• Data Organization and Strategy: No centralized data organization existed, so data was treated as an afterthought rather than a strategic asset. The absence of formal roadmaps and ownership models led to reactive, underfunded initiatives.

• Foundational Data: Outdated, failing technology made data management insufficient and unscalable. Data assets were not integrated into a centralized platform, limiting reuse and advanced analytics.

• Governance & Data Quality: Some governance existed, but roles were poorly defined and accountability was weak. Low trust in data across teams led to misaligned and often incorrect reporting.

• Siloed Analytics: There was no centralized analytics function or vision, resulting in duplicate but inconsistent findings and hindering cross–business unit insights.

• AI Initiatives: Without a solid enterprise data management program, AI initiatives would remain inaccurate pilots rather than scalable enterprise capabilities.

Approach

As the lead on the practicum team, I used the assessment findings to design a prioritized three-year data transformation roadmap with a coordinated program of strategic data and AI initiatives. For each initiative, we built a business case that included modeled costs, timelines, and projected benefits. Key initiatives included:

• Enterprise Data Foundations: Increase maturity and usability of the company’s enterprise data by:

  • Establishing a data program aligned to the business with a clear vision and strategy.

  • Expanding data governance to include AI, solidifying ownership and accountabilities, and providing data fluency training for all employees.

  • Modernizing the data platform to be cloud-based with real-time data pipelines, and decommissioning the unstable on-premises platform to reduce costs and prevent continued reliance.

  • Creating an analytics hub with consistent data and enhanced reporting and dashboards.

• Performance AI: Leverage the improved enterprise data, create AI-powered products to provide better insights, identify incremental revenue opportunities, and streamline operational practices.

• Innovative AI: Develop a new AI-powered product that builds on the company’s core competencies in mind while increasing new revenue opportunities and reinforcing the company’s innovation leadership.

Modeled Results

Based on the assessment and modeled scenarios, the projected three-year impact of the roadmap was:

• Data and AI maturity advanced significantly over three years with a total investment of $50.2M and an ongoing operating budget of $15.5M. Enterprise data shifted from a blocker to an enabler.

• Projected savings and incremental revenue fully offset the investment costs in year 2 of the 3 year roadmap. Approximately 10% of the annual benefits would be sufficient to cover ongoing operating costs.

• Maturing enterprise data is projected to deliver up to $20M per year in cost savings and newly identified revenue opportunities.

• AI initiatives designed to optimize performance are projected to generate $8M–$10M in annual revenue and $17.5M–$21M in annual savings.

• The innovative AI initiative focused on creating the “business of tomorrow” is projected to deliver $80M+ in new revenue annually.

Core Takeaway

Without solid data, a company’s growth is stunted and AI initiatives are likely to fail. Without an honest assessment of an organization’s data maturity, data will never reach its full potential.Assessments identify gaps in data maturity; closing those gaps unlocks significant benefits and enables meaningful AI advancement.

This is a scenario-based case study developed from a practicum simulation. It illustrates how I would approach a data and AI maturity assessment and transformation roadmap for a media company. Numbers represent modeled impacts based on the simulation data and industry benchmarks, not realized client results.