Build a Data Pipeline for Reporting and Analytics

Data architecture best practices to prepare data for reporting and analytics.

Onsite Workshop

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.

Module 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:
1.1 Review & discuss typical pitfalls (and their causes) of major data management initiatives.
  • Understanding typical pitfalls (and their causes) of major data management initiatives.
1.2 Discuss the main business capabilities of the organization and how they interact.
  • Business capabilities map
1.3 Discuss the business processes running inside and across business capabilities and the datasets involved.
  • Business processes map
1.4 Create the Enterprise Business Process Model (EBPM).
  • Enterprise Business Process Model (EBPM)

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.
  • Understanding of a data pipeline design, including its zones.
2.2 Identify these zones in the enterprise business model.
  • EBPM mapping to Data Pipeline Zones
2.3 Review and discuss multi-lane data progression.
  • Understanding of multi-lane data progression
2.4 Identify different speed lanes in the enterprise business model.
  • EBPM mapping to Multi-Speed Data Progression Lanes

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.
  • Understanding of various data design patterns.
3.2 Discuss and select the data design pattern selection for data pipeline components.
  • Data Design Patterns mapping to the data pipeline.
3.3 Discuss applicability of data model industry standards (if available).
  • Selection of an applicable data model from available industry standards.

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