Data Architecture

Manage valuable data assets

Onsite Workshop

Non-existent or initial approaches to data can cause:

  • Low data quality, which can lead to inefficient operations.
  • Increased risk of data loss or data breaches.
  • Low data trust throughout the organization, which can lead to business decisions being made based on feeling rather than empirical data.
  • Increased instances of rogue data being used.

Treating data as an asset results in:

  • Transparency about where data sits, how it is used, and who is using it.
  • Increased data trust, which improves decision making.
  • Improved availability – data is available to the right people at the right time.
  • Tight data lifecycles.
  • A thorough understanding of any gaps or issues that exist, and the steps needed to mitigate them.

Module 1: Perform a Current State Assessment

The Purpose

  • Establish the organization’s approach to data and its perceived data maturity level.
  • Identify the business process that will be used in the data audit.

Key Benefits Achieved

  • Issue identification.
  • Prepared for the data audit, validating the scope, stakeholders, and participants prior to implementation.

Activities: Outputs:
1.1 Determine the organization’s data value and risk.
  • Data Value/Risk Assessment
1.2 Establish maturity level.
  • Maturity Assessment
1.3 Determine what data sources contribute to a particular business process.
1.4 Review and assess the IT reports.
1.5 Identify data audit participants.

Module 2: Perform the Data Audit

The Purpose

  • Establish a data audit framework.
  • Understand how to effectively communicate with audit participants to obtain valuable information.
  • Ensure internal policies are implemented to help minimize compliance issues.
  • Identify where there are gaps in your data.

Key Benefits Achieved

  • Focus your efforts on fixing major gaps in your organization’s data.
  • Properly identify key data stakeholders and establish data ownership and responsibility.

Activities: Outputs:
2.1 Create a data audit interview guide.
  • Customized interview guide
2.2 Create a data source inventory.
  • Complete Data Source Inventory
2.3 Conduct data audit interviews.
  • Completed interviews
2.4 Identify gaps in the data and perform a root cause analysis.
  • Identification of problem areas

Module 3: Develop Data Architecture

The Purpose

  • Set architectural principles for the target architecture.
  • Select new building blocks for your target architecture.
  • Prioritize the building blocks.
  • Reconcile the new building blocks and their ability to solve data issues.

Key Benefits Achieved

  • Selected building blocks to solve the issues uncovered in the audit.
  • Reconcile the solution against the problems uncovered in the audit.
  • Refined the target architecture.

Activities: Outputs:
3.1 Develop a target architecture.
  • Determined architectural building blocks
3.2 Reconcile architecture.
  • Updated architecture

Module 4: Create a Data Architecture Implementation Plan

The Purpose

  • Define dependencies among building blocks.
  • Group building blocks into initiatives.
  • Draft a high level plan to implement the initiatives, taking cost/value and dependencies into consideration.

Key Benefits Achieved

  • Establish initiatives, taking cost and value into consideration.
  • Tackle the initiatives that have been identified with a realistic and manageable approach.
  • Determine if the target state is going to meet the data asset management goals and objectives.

Activities: Outputs:
4.1 Plan initiatives.
  • Initiative plan
4.2 Develop a strategic roadmap.

Module 5: Establish Metrics for Success and Communicate the Plan

The Purpose

  • Set initiative benchmarks to track success.
  • Determine what initiatives need to be communicated and to whom.
  • Have a plan to communicate initiatives to the organization.
  • Communicate the results of the data audit, along with the short and long term plans, to the organization.

Key Benefits Achieved

  • Avoid creating a document that is never used.
  • Workshop summary.
  • Defined metric and communication goals.

Activities: Outputs:
5.1 Establish metrics.
  • Metric tracking
5.2 Create a communication plan.
  • Completed communication plan

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Case Studies and Deliverables

Data Architecture Case Study of a Large Hospital

A large hospital with a rich and time-honored history was well-known for its innovation and academic research activities. IT needed to ensure that its data architecture could support both the hospital’s day-to-day patient care and ongoing research mandates. The hospital recruited Info-Tech to guide them through a one-week data architecture workshop.

Data Architecture Case Study of Olmsted Medical Center

Olmsted Medical Center provides healthcare to communities in southern Minnesota via a centralized hospital and 13 clinical locations. Olmsted has recently implemented an Electronic Health Record, and wants to combine clinical and ambulatory data to give a single view of patient information. Representatives from the technology and finance teams participated in an Info-Tech workshop to develop a process for reviewing and refining data architecture principles and processes.

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