Build a Data Pipeline for Reporting and Analytics | Info-Tech Research Group

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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.

Build a Data Pipeline for Reporting and Analytics – Executive Brief

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.

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.

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).

Member Testimonials

After each Info-Tech experience, we ask our members to quantify the real-time savings, monetary impact, and project improvements our research helped them achieve. See our top member experiences for this blueprint and what our clients have to say.

9.0/10

Overall Impact

$13,015

Average $ Saved

20

Average Days Saved

Client

Experience

Impact

$ Saved

Days Saved

Testimonial

Northeast Credit Union

Guided Implementation

10/10

$13,700

20

Igor is extremely knowledgeable and helpful and immediately added value. No negative or worse parts of this engagement.

Analytics IQ

Guided Implementation

8/10

$12,330

20

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Guided Implementation 1: Understand data progression

Call 1: Review and discuss typical pitfalls (and their causes) of major Data Management initiatives. Discuss the main business capabilities of the organization and how they interact.
Call 2: Discuss the business processes running inside and across business capabilities and the datasets involved.

Guided Implementation 2: Identify data pipeline components

Call 1: Review and discuss the concept of a Data Pipeline in general, as well as the vertical zones: data creation, accumulation, augmentation, and consumption. Identify these zones in the enterprise business model.
Call 2: Review and discuss multi-lane data progression and identify different speed lanes in the enterprise business model.

Guided Implementation 3: Select data design patterns

Call 1: Review and discuss various data design patterns.
Call 2: Discuss the data design pattern selection for Data Pipeline components. Discuss applicability of Industry Standard data model (if available).

Author

Igor Ikonnikov

Contributors

Iryna Roy, Consultant, Architecture and Standards, Gevity Consulting Inc.
Nicholas Yee, Chief Strategy Officer, Hubio Technology
Shantanu Raje, Director, IT Software Solutions
Biplab Sarker, Data Architect, Sun Life
Mike Lapenna, Enterprise Information Architect, Manulife
Guy Kayembe, Big Data Architect, Pilgrim Data Services

Search Code: 94863
Last Revised: November 2, 2020

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