Build a Business-Aligned Data Architecture Optimization Strategy

Optimizing your data architecture requires a prioritized and tactical plan for the people, processes, and technology, not just data models.

Onsite Workshop

Not having a business-aligned approach to data architecture results in:

  • Inefficient, inaccurate, and delayed decision making.
  • Wasted resources due to inefficient operations and processes.
  • Inaccurate insights, inconsistent, or erroneous operational and management reports, and ineffective cross-departmental use of analytics.

Having a business-aligned approach to data architecture results in:

  • Good data quality, allowing you to generate insights into customer intimacy, operational excellence, and innovation leadership.
  • Effective data architecture tactics driven by business need for data.
  • A corporate culture that holds data as a priority and an asset.

Module 1: Identify the Drivers of the Business for Optimizing Data Architecture

The Purpose

  • Explain approach and value proposition.
  • Review the common business drivers and how the organization is driving a need to optimize data architecture.
  • Understand Info-Tech’s five-tier data architecture model.
  • Determine the pattern of tactics that apply to the organization for optimization.

Key Benefits Achieved

  • Understanding of the current data architecture landscape.
  • Priorities for tactical initiatives in the data architecture practice are identified.
  • Target state for the data quality practice is defined.

Activities: Outputs:
1.1 Explain approach and value proposition.
  • Five-tier logical data architecture model
1.2 Review the common business drivers and how the organization is driving a need to optimize data architecture.
  • Data architecture tactic plan
1.3 Understand Info-Tech’s five-tier data architecture model.
1.4 Determine the pattern of tactics that apply to the organization for optimization.

Module 2: Determine Your Tactics For Optimizing Data Architecture

The Purpose

  • Define improvement initiatives.
  • Define a data architecture improvement strategy and roadmap.

Key Benefits Achieved

  • Gaps, inefficiencies, and opportunities in the data architecture practice are identified.

Activities: Outputs:
2.1 Create business unit prioritization roadmap.
  • Business unit prioritization roadmap
2.2 Develop subject area project scope.
  • Subject area scope
2.3 Subject area 1: data lineage analysis, root cause analysis, impact assessment, business analysis
  • Data lineage diagram

Module 3: Create a Strategy for Data Quality Project 2

The Purpose

  • Define improvement initiatives.
  • Define a data quality improvement strategy and roadmap.

Key Benefits Achieved

  • Improvement initiatives are defined.
  • Improvement initiatives are evaluated and prioritized to develop an improvement strategy.
  • A roadmap is defined to depict when and how to tackle the improvement initiatives.

Activities: Outputs:
3.1 Create business unit prioritization roadmap.
  • Business unit prioritization roadmap
3.2 Develop subject area project scope.
  • Subject area scope
3.3 Subject area 1: data lineage analysis, root cause analysis, impact assessment, business analysis.
  • Data lineage diagram

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