Optimize the DRP for Business-Critical Analytics
As analytics become more critical to business processes, whether it's big data or “small” data, your DRP needs to keep up.
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Analytics will often morph into a key organizational dependency, however, ad hoc DR processes will miss this transformation and cause:
- Gaps in your DR preparedness, decreasing the likelihood of a successful recovery during a disaster.
- An inability to prioritize resources and provide adequate support for the most critical analytics data sets.
- A lack of engagement from executives, resulting in under-provisioned DR investment.
Proactive evaluation of the criticality of analytics will result in:
- Increased awareness of the importance of analytics data, resulting in increased likelihood of a successful recovery.
- Appropriate resource allocation for one of the organization’s most critical dependencies.
- Ability to create a scalable DR environment that will accommodate the future analytics needs of the business.
Module 1: Map Out Key Business Processes
The Purpose
- Identify critical business processes that rely on analytics data.
- Determine where within the business process is analytics data critical and what the business impact is if the analytics data was not available during downtime.
Key Benefits Achieved
- Define analytics data dependencies within critical business processes.
- Uncover knowledge gaps where the business is leveraging analytics data in a way that IT is not aware of.
| Activities: | Outputs: | |
|---|---|---|
| 1.1 | Document critical business processes. |
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| 1.2 | Map out data-driven business processes. |
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| 1.3 | Determine where analytics data becomes critical in the business process. |
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Module 2: Initiate the Data Criticality Assessment
The Purpose
- Establish a program to evaluate which data sets warrant higher availability.
- Identify and document which repositories currently hold critical analytics data.
Key Benefits Achieved
- Differentiate levels of criticality within data sets and identify the most critical analytics data.
- Determine which repositories should be given the highest priority.
| Activities: | Outputs: | |
|---|---|---|
| 2.1 | Create the data criticality inventory scheme. |
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| 2.2 | Identify and describe data repositories. |
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| 2.3 | Begin documenting data sets in the data criticality inventory. |
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Module 3: Complete the Data Criticality Assessment
The Purpose
- Document all critical analytics data sets into the data criticality inventory tool.
- Determine DR requirements for each specific data set.
- Evaluate potential DR solutions based on DR requirements.
Key Benefits Achieved
- Catalog of critical analytics data sets that are matched to each repository.
- Differentiate between each data set and prioritize available resources.
- Overview available options and select a best-fit solution.
| Activities: | Outputs: | |
|---|---|---|
| 3.1 | Complete the data criticality inventory. |
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| 3.2 | Assign RTO and RPO requirements based on business impact. |
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| 3.3 | Assess the recommended recovery solutions based on DR requirements. |
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Module 4: Optimize the Disaster Recovery Plan
The Purpose
- Define an appropriate deployment model for the desired DR solution.
- Garner executive support for current and future DR projects.
- Document the necessary project steps to implement the desired DR solution.
Key Benefits Achieved
- Select a DR solution that fits into your current DR environment and management appetite for DR initiatives.
- Level set people, process, and technology requirements for the DR solution.
- Organize tasks and determine FTE gaps or budget gaps within the implementation plan.
| Activities: | Outputs: | |
|---|---|---|
| 4.1 | Review the DR solution selection methodology. |
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| 4.2 | Craft the Executive Presentation Deck. |
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| 4.3 | Complete the Project Planning and Prioritization Tool. |
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