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Establish an Analytics Operating Model

Accelerate data-driven decision making.

  • Organizations are struggling to understand what's involved in the analytics developer lifecycle to generate reusable insights faster.
  • Discover what it takes to become a citizen analytics developer. Identify the proper way to enable self-serve analytics.
  • Self-serve business intelligence/analytics is misunderstood and confusing to the business, especially with regards to the roles and responsibilities of IT and the business.
  • End users are dissatisfied due to a lack of access to the data and the absence of a single source of truth.

Our Advice

Critical Insight

Organizations that take data seriously should:

  • Decouple processes in which data is separated from business processes and elevate the visibility of the organization's data assets.
  • Leverage a secure platform where data can be easily exchanged for insights generation.
  • Create density for analytics where resources are mobilized around analytics ideas to generate value.

Analytics is a journey, not a destination. This journey can eventually result in some level of sophisticated AI/machine learning in your organization. Every organization needs to mobilize its resources and enhance its analytics capabilities to quickly and incrementally add value to data products and services. However, most organizations fail to mobilize their resources in this way.

Impact and Result

  • Firms become more agile when they realize efficiencies in their analytics operating models and can quickly implement reusable analytics.
  • IT becomes more flexible and efficient in understanding the business' data needs and eliminates redundant processes.
  • Trust in data-driven decision making goes up with collaboration, engagement, and transparency.
  • There is a clear path to continuous improvement in analytics.

Establish an Analytics Operating Model Research & Tools

Start here – read the Executive Brief

Read our concise Executive Brief that outlines Info-Tech’s methodology for assessing the business' analytics needs and aligning your data governance, capabilities, and organizational structure to deliver analytics faster.

Establish an Analytics Operating Model – Executive Brief
Establish an Analytics Operating Model – Phases 1-3

1. Define your analytics needs

This phase helps you understand your organization's data landscape and current analytics environment so you gain a deeper understanding of your future analytics needs.

Establish an Analytics Operating Model – Phase 1: Define Your Analytics Needs

2. Establish an analytics operating model

This phase introduces you to data operating model frameworks and provides a step-by-step guide on how to capture the right analytics operating model for your organization.

Establish an Analytics Operating Model – Phase 2: Establish an Analytics Operating Model
Analytics Operating Model Building Tool

3. Implement your operating model

This phase helps you implement your chosen analytics operating model, as well as establish an engagement model and communications plan.

Establish an Analytics Operating Model – Phase 3: Implement Your Analytics Operating Model

About Info-Tech

Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.

We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.

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A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.

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Author

Rajesh Parab

Contributors

  • Gail Ulan, Program Manager & Architect, Central Arizona Project
  • Jordan Thomas, Solution Architect, Databricks
  • Maik Schutze, Chief Analytics Officer, Kentucky Cabinet for Health and Family
  • Toby Balsley, Lead Enterprise Architect, Tufts Health Plan
  • Viannie Kaye, Data Governance Manager, Hamilton Health Sciences

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