Create a Customized Big Data Architecture and Implementation Plan

Our systems detected an issue with your IP. If you think this is an error please submit your concerns via our contact form.

Big data architecture is different from traditional data for several key reasons, including:
- Big data architecture starts with the data itself, taking a bottom-up approach. Decisions about data influence decisions about components that use data.
- Big data introduces new data sources such as social media content and streaming data.
- The enterprise data warehouse (EDW) becomes a source for big data.
- Master data management (MDM) is used as an index to content in big data about the people, places, and things the organization cares about.
- The variety of big data and unstructured data requires a new type of persistence.
Many data architects have no experience with big data and feel overwhelmed by the number of options available to them (including vendor options, storage options, etc.). They often have little to no comfort with new big data management technologies.
If organizations do not architect for big data, there are a couple of main risks:
- The existing data architecture is unable to handle big data, which will eventually result in a failure that could compromise the entire data environment.
- Solutions will be selected in an ad hoc manner, which can cause incompatibility issues down the road.

Our Advice

Critical Insight

Before beginning to make technology decisions regarding the big data architecture, make sure a strategy is in place to document architecture principles and guidelines, the organization’s big data business pattern, and high-level functional and quality of service requirements.
The big data business pattern can be used to determine what data sources should be used in your architecture, which will then dictate the data integration capabilities required. By documenting current technologies, and determining what technologies are required, you can uncover gaps to be addressed in an implementation plan.
Once you have identified and filled technology gaps, perform an architectural walkthrough to pull decisions and gaps together and provide a fuller picture. After the architectural walkthrough, fill in any uncovered gaps. A proof-of-technology project can be started as soon as you have evaluation copies (or OSS) products and at least one person who understands the technology.

Impact and Result

Save time and energy trying to fix incompatibilities between technology and data.
Allow the Data Architect to respond to big data requests from the business more quickly.
Provide the organization with valuable insights through the analytics and visualization technologies that are integrated with the other building blocks.

Create a Customized Big Data Architecture and Implementation Plan Research & Tools

1. Recognize the importance of big data architecture

Big data is centered on the volume, variety, velocity, veracity, and value of data. Achieve a data architecture that can support big data.

2. Define architectural principles and guidelines while taking into consideration maturity

Understand the importance of a big data architecture strategy. Assess big data maturity to assist with creation of your architectural principles.

3. Build the big data architecture

Come to accurate big data architecture decisions.

4. Determine common services needs

What are common services?

5. Plan a big data architecture implementation

Gain business satisfaction with big data requests. Determine what steps need to be taken to achieve your big data architecture.

Create a Customized Big Data Architecture and Implementation Plan visualization

Big data architecture is not your father’s data architecture.

About Info-Tech

Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.

We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.

What Is a Blueprint?

A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.

Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.

Table of Contents

Need Extra Help?
Speak With An Analyst

Get the help you need in this 1-phase advisory process. You'll receive 4 touchpoints with our researchers, all included in your membership.

Call 1: Design a big data architecture strategy

Discuss with an Analyst the implications of your maturity on your principles and guidelines. Validate your pattern selection tool results and ensure there are no requirement gaps.
Call 2: Build the big data architecture

Validate your Big Data Architecture Decision Making Tool results and receive guidance on next steps to understand the implications on your common services.
Call 3: Determine common service needs

Receive guidance on your common services needs and advice on beginning your implementation plan.
Call 4: Plan the big data architecture implementation

Grasp how initiatives can be grouped together and the dependencies between initiatives. Recognize metrics for determining the success of your architecture.

Authors

Stewart Bond

Kylie Grace

Amanda Robinson

Contributors

Bill & Melinda Gates Foundation
Bosch Automotive Aftermarket Diagnostics
Cardinal Health
Centre for Computational Research, University of Buffalo
Milne Library, SUNY College
Protegrity
US Bank
1 additional organization contributed information to assist with the development of this solution set. Due to the sensitivity of the information, this contributor requested confidentiality.