Turning Data Into Decisions—Less Guesswork, More Game Plans.

Scaling AI, data and analytics is hard, John Hogue has over a decade of in-depth experience leading organizations to become more data-driven. From multi-billion dollar enterprises to pre-seed startups, John has the expertise to advise C-Suite and Senior Leaders on data and analytics priorities, organization, operating model and change management.

“Data strategy is as much about people and process as it is about tools and technology.”

Common Challenges Leaders Face

AI, Data & Analytics Strategy:

  • Unclear path to AI readiness and difficulty identifying high-impact AI use cases.
  • Challenge articulating current and aspirational skillsets and capabilities needed to drive innovation.
  • HiPPO or Peanut Butter prioritization of data-driven initiatives resulting in stalled progress.
Turn strategic uncertainty into clear direction and high-impact execution.
  • Inventory business pain points with PNL leaders, mapping them to use cases.
  • Identify skill and capability gaps to meeting both short and long term enterprise goals.
  • Coach and adapt CRUISE prioritization framework to organization.

Cost Management & Optimization:

  • Difficulty evaluating the impact of cloud investments, costs and their drivers.
  • Cost allocation methodologies incentivizing poor design decisions.
  • Challenges assessing data compatibility and integration during mergers.
Align cloud investments with business value and operational clarity.
  • Establish key metrics to monitor cloud spending and performance
  • Implement cost transparency and chargeback models aligning investments with strategic priorities
  • Assess potential M&A opportunities’ tech stack for synergies and challenges.

Data Governance & Literacy & Maturity:

  • Undefined data roles and processes causing accountability and governance failures.
  • Inability to find, trust and connect data leading to low utilization.
  • Struggling to implement data-driven decision-making processes across enterprise.
  • Poor data understanding of available data and its impact on KPIs.
Build trust, accountability, and confidence in enterprise data.
  • Establish data governance frameworks defining roles, responsibilities and process.
  • Evaluate enterprise data utilization and identify obstacles.
  • Develop change management strategy, including pilots to roll out data-centric decisioning.
  • Assess and improve data literacy across the organization.

Change Management & Organizational Transformation:

  • Limited understanding and expertise around AI, data and analytics, leading to uncertainty about how to get started.
  • Difficulty identifying and integrating emerging technologies, resulting in patchwork architecture and operating model.
Bridge the gap between ambition and execution in data-driven transformation.
  • Provide leadership coaching on AI, data and analytics.
  • Conduct organizational assessments to identify barriers to change.
  • Benchmark and assess current and future needs of tech and how it fits into current architecture landscape.

Contact

john@roguehogue.com

linkedin.com/johnhogue/