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Optimize the Organization’s Data Integration Practices

Ensure the organization’s data accurately flows to the critical downstream IT and business processes through effective data integration.

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Contributors

  • Abram Chang, Systems Analyst, Strathcona County Alberta
  • Imdad Ali, Architect, Gamma Dynacare
  • Shah Rao, Information Lifecycle Governance Consultant
  • Shaun Mitchell, Manager of Information Management, Western Financial Group Inc.

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  • Analyst Interviews: Share your best practices, opinions, tools or templates with your peers.
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Your Challenge

  • As organizations process more information at faster rates, there is increased pressure for faster and more efficient data integration programs.
  • Data integration is an intermediary function that is critical for downstream functions of data management and business operations to be successful.
  • Evolving business models and uses of data are growing rapidly at rates that often exceed the investments in data management and integration tools, and as a result there is often a gap between data availability and the business’s latency demands.

Our Advice

Critical Insight

  • Identifying the right pattern for your data use cases is only part of the battle. More times than not, success in data integration is hinged on the performance of activities in development, architecture, governance, and quality.
  • Successful data integration solutions require more than just technology – they require design, planning, governance, and maintenance.

Impact and Result

  • Create a data integration program that supports the flow of data through the organization and meets the organization’s requirements around data latency.
  • Ensure that the necessary architecture, governance, MDM, and quality building blocks support your data integrations.
  • Build your data integration practice with a firm foundation in governance and reference architecture. Use best-fit reference architecture patterns and the related technology and resources to ensure that your process is scalable and sustainable.
  • Cloud is disrupting how traditional data integrations are performed; with new deployment methods and locations of data, new decisions around integration points and types of services must also be evaluated.
  • The business’s uses of data are constantly changing and evolving, and as a result the integration processes that ensure data availability must be frequently reviewed and repositioned in order to continue to grow with the business.

Research & Tools

1. Identify the need and readiness for improved data integration practices

Determine the value and opportunity associated with an investment in time, energy, and technology in data integration.

2. Structure the data integration project

Complete the necessary project planning and stakeholder management.

3. Assess how related data management functions support data integration

Identify how additional data management functions enhance or constrain the organization’s data integration practices.

4. Analyze the broader data integration requirements

Determine the business and IT requirements for data integration.

5. Select integration patterns

Evaluate and select the appropriate patterns for the integration scenarios.

6. Optimize data integrations with governance

Enforce integration governance through the creation of planning and control activities.

7. Create an implementation plan

Create an action plan for implementing the project’s findings and tackling the necessary action items.

Guided Implementations

This guided implementation is a six call advisory process.

Guided Implementation #1 - Launch data integration project

Call #1 - Discuss the position and value of data for your organization.
Call #2 - Create a plan for your data integration project.

Guided Implementation #2 - Assess and gather requirements

Call #1 - Review how the performance of additional data management functions are impacting data integration.
Call #2 - Discuss your requirements interview and assessment findings.

Guided Implementation #3 - Plan your data integration solutions

Call #1 - Review your reference pattern selection.
Call #2 - Identify governance initiatives for data integration and develop a plan for optimization.

Onsite Workshop

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Module 1: Launch Your Data Integration Project

The Purpose

  • Identify if your business needs data or application integration.
  • Identify the business's opportunities when investing in data integration.
  • Plan your data integration project.
  • Plan how to gain buy-in for your project.

Key Benefits Achieved

  • Confirmation of project fit.
  • A planned project.
  • A plan for gaining stakeholder support.

Outputs

Activities:

1.1

Assess value and opportunities.

1.2

Determine scope and objectives.

1.3

Create a project plan.

  • A project plan.
1.4

Create a staffing plan.

  • Defined roles and responsibilities.
1.5

Plan and make the case for data integration.

  • Completed business case.

Module 2: Assess and Gather Requirements

The Purpose

  • Identify how data integration impacts and is impacted by additional data management functions.
  • Identify the business’s data access and integration requirements.
  • Identify integration scenarios.

Key Benefits Achieved

  • Understanding of the current capabilities supporting the organization’s data integration practices.
  • Determine the business’s data integration requirements.
  • Document integration scenarios.

Outputs

Activities:

2.1

Identify the current state of data management building blocks for successful data integration.

  • Action plan for addressing missing building blocks.
2.2

Interview the business.

  • Business and IT requirements for data integration.
2.3

Interview IT staff.

2.4

Identify integration scenarios.

  • Integration scenarios.

Module 3: Plan Your Data Integration Solutions

The Purpose

  • Analyze and select reference architecture patterns.
  • Evaluate the impact of cloud in performing your organization’s data integrations.
  • Create governance around performing data integration.

Key Benefits Achieved

  • Selection of integration reference patterns.
  • Understanding of the considerations around storing and integrating data in cloud environments.
  • The creation of governance principles and management practices that control how integrations are performed.

Outputs

Activities:

3.1

Evaluate reference patterns.

  • Data integration reference architecture.
3.2

Select the reference patterns that fit your integration scenarios.

  • Integration governance.
3.3

Create an implementation plan.

  • Implementation plan.
3.4

Identify and document investment requirements.

  • Action plan for tackling next steps.