- Continuous and disruptive database design updates while trying to have one design pattern to fit all use cases.
- Sub-par performance while loading, retrieving, and querying data.
- You want to shorten time-to-market of the projects aimed at data delivery and consumption.
- Unnecessarily complicated database design limits usability of the data and requires knowledge of specific data structures for their effective use.
Our Advice
Critical Insight
- Evolve your data architecture. Data pipeline is an evolutionary break away from the enterprise data warehouse methodology.
- Avoid endless data projects. Building centralized all-in-one enterprise data warehouses takes forever to deliver a positive ROI.
- Facilitate data self-service. Use-case optimized data delivery repositories facilitate data self-service.
Impact and Result
- Understand your high-level business capabilities and interactions across them – your data repositories and flows should be just a digital reflection thereof.
- Divide your data world in logical verticals overlaid with various speed data progression lanes, i.e. build your data pipeline – and conquer it one segment at a time.
- Use the most appropriate database design pattern for a given phase/component in your data pipeline progression.
Build a Data Pipeline for Reporting and Analytics Research & Tools
Start here – read the Executive Brief
Build your data pipeline using the most appropriate data design patterns.Besides the small introduction, subscribers and consulting clients within this management domain have access to:
- Build a Data Pipeline for Reporting and Analytics – Phases 1-3
1. Understand data progression
Identify major business capabilities, business processes running inside and across them, and datasets produced or used by these business processes and activities performed thereupon.
- Build a Data Pipeline for Reporting and Analytics – Phase 1: Understand Data Progression
2. Identify data pipeline components
Identify data pipeline vertical zones: data creation, accumulation, augmentation, and consumption, as well as horizontal lanes: fast, medium, and slow speed.
- Build a Data Pipeline for Reporting and Analytics – Phase 2: Identify Data Pipeline Components
3. Select data design patterns
Select the right data design patterns for the data pipeline components, as well as an applicable data model industry standard (if available).
- Build a Data Pipeline for Reporting and Analytics – Phase 3: Select Data Design Patterns
Workshop: Build a Data Pipeline for Reporting and Analytics
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 delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.
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
1.1 Review & discuss typical pitfalls (and their causes) of major data management initiatives.
1.2 Discuss the main business capabilities of the organization and how they interact.
1.3 Discuss the business processes running inside and across business capabilities and the datasets involved.
1.4 Create the Enterprise Business Process Model (EBPM).
Outputs
Understanding typical pitfalls (and their causes) of major data management initiatives.
Business capabilities map
Business processes map
Enterprise Business Process Model (EBPM)
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
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.
2.2 Identify these zones in the enterprise business model.
2.3 Review and discuss multi-lane data progression.
2.4 Identify different speed lanes in the enterprise business model.
Outputs
Understanding of a data pipeline design, including its zones.
EBPM mapping to Data Pipeline Zones
Understanding of multi-lane data progression
EBPM mapping to Multi-Speed Data Progression Lanes
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
3.1 Review and discuss various data design patterns.
3.2 Discuss and select the data design pattern selection for data pipeline components.
3.3 Discuss applicability of data model industry standards (if available).
Outputs
Understanding of various data design patterns.
Data Design Patterns mapping to the data pipeline.
Selection of an applicable data model from available industry standards.