Build a Data Pipeline for Reporting and Analytics
Data architecture best practices to prepare data for reporting and analytics.
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Building centralized monstrous enterprise data warehouses takes forever to deliver positive ROI.
- Sub-par performance while loading, retrieving, and querying data.
- Continuous database design updates while trying to have one design pattern to fit all use cases.
- Unnecessarily complicated database design limits usability of the data and requires knowledge of specific data structures for their effective use.
Use-case optimized data delivery repositories facilitate data self-service.
- Facilitated use of data repositories built on predictable patterns.
- Simplified (pattern-based) creation of data processing/consuming applications.
- Increased stability of data processing/consuming applications.
- Adequate performance while loading, retrieving, and querying data.
- Improved data refresh cycle.
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Onsite Workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn’t enough, we offer low-cost onsite delivery of our Project Workshops. We take you through every phase of your project and ensure that you have a road map in place to complete your project successfully.
Book NowModule 1: Understand Data Progression
The Purpose
Identify major business capabilities, business processes running inside and across them, and datasets produced or used by these business processes and activities performed thereupon.
Key Benefits Achieved
Indicates the ownership of datasets and the high-level data flows across the organization.
| Activities: | Outputs: | |
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| 1.1 | Review & discuss typical pitfalls (and their causes) of major data management initiatives. |
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| 1.2 | Discuss the main business capabilities of the organization and how they interact. |
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| 1.3 | Discuss the business processes running inside and across business capabilities and the datasets involved. |
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| 1.4 | Create the Enterprise Business Process Model (EBPM). |
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Module 2: Identify Data Pipeline Components
The Purpose
Identify data pipeline vertical zones: data creation, accumulation, augmentation, and consumption, as well as horizontal lanes: fast, medium, and slow speed.
Key Benefits Achieved
Design the high-level data progression pipeline.
| Activities: | Outputs: | |
|---|---|---|
| 2.1 | Review and discuss the concept of a data pipeline in general, as well as the vertical zones: data creation, accumulation, augmentation, and consumption. |
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| 2.2 | Identify these zones in the enterprise business model. |
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| 2.3 | Review and discuss multi-lane data progression. |
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| 2.4 | Identify different speed lanes in the enterprise business model. |
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Module 3: Develop the Roadmap
The Purpose
Select the right data design patterns for the data pipeline components, as well as an applicable data model industry standard (if available).
Key Benefits Achieved
Use of appropriate data design pattern for each zone with calibration on the data progression speed.
| Activities: | Outputs: | |
|---|---|---|
| 3.1 | Review and discuss various data design patterns. |
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| 3.2 | Discuss and select the data design pattern selection for data pipeline components. |
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| 3.3 | Discuss applicability of data model industry standards (if available). |
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