Create a Customized Big Data Architecture and Implementation Plan
Big data architecture is not your father’s data architecture.
- 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.
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.
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.
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.
Build a Robust and Comprehensive Data Strategy
Create a Data Management Roadmap
Establish Data Governance
Build a Data Architecture Roadmap
Sprint Toward Data-Driven Culture Using DataOps
Develop a Master Data Management Practice and Platform
Build a Data Integration Strategy
Build a Data Pipeline for Reporting and Analytics
Build an Extensible Data Warehouse Foundation
Build a Reporting and Analytics Strategy
Modernize Data Architecture for Measurable Business Results
Create a Customized Big Data Architecture and Implementation Plan
Break Open Your DAM With Intuitive Metadata
Perform a Data Audit
Create an Architecture for AI
Launch Your AI Proof of Value
