Enterprise Architecture Patterns For Big Data

Thursday 20 October 2016, 6.00pm

ODI Leeds, 3rd Floor Munro House, Duke Street, Leeds, LS9 8AG - Maps
Just 2 mins from the bus station and 5 mins from the train station.


  • 6.00pm - 6.20pm -  Registration
  • 6.20pm - 6.30pm -  AGM
  • 6.30pm - 7.15pm -  Presentation
  • 7.15pm - 8.00pm - Networking opportunity


This session looks at the best practice BT has developed after 5 years research and 2.5 years in production with Big Data Platforms. It looks at the Technical & Commercial Benefits adopting big data technologies and the stakeholders that need to be engaged. 

BT has developed a set of Architectural Patterns (and anti-patterns) that it uses to accelerate application adoption and exploitation of this data to meet business needs. 

 The talk will look at the most commonly used patterns :- 

  •  Multi-Tenant Big Data Enterprise Service Platform, as an enabler to recombining data silos 
  • How Applications extend their footprint to get experience of Big Data 
  • Common Data Transfer Patterns 

Speaker - Phil Radley 

Phil Radley is a Physics Graduate with an MBA who has worked in IT and communications industry for 30 years, mostly with British

Telecommunications plc. He is Chief Data Architect for BT at their Adastral Park campus in the UK. 

Phill works in BT’s core Enterprise Architecture team with responsibility for Data Architecture across BT Group plc. He currently leads BT’s MDM and Big Data initiatives driving associated strategic architecture and investment roadmaps for the business. 

His previous roles in BT include; 9 years as Chief Architect for Infrastructure Performance Management solutions, working on diverse range of projects from UK consumer broadband through to outsourced Fortune 500 networks and hi-performance trading networks. 

He has broad global experience including BT’s Concert global venture in USA and 5 years as Asia Pacific BSS/OSS Architect based in Sydney. 


PDF Icon AGM 2016 minutes


PDF Icon Enterprise Architecture Patterns For Big Data - Phil Radley