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Data Science (Analytics to discover and prove valuable insights)
Data Engineering (Implementing robust operational data flows and applications)
Governance for Data & Architecture (Alignment of multiple projects and the business)
Asking Data Scientists to be responsible for both Analytics and Engineering is to combine roles as diverse as those of a benign hacker and a DBA. At best, one person will be an amateur in one of those fields but to charge one person with the conflicting responsibilities of discovery and operations is to underrate one of them to the disadvantage of the organisation.
Join Kenneth and get an overview of Big Data Issues & solutions in respect of
- Resourcing and Managing concurrent Analytics Projects
o Sharing research on the separation of responsibilities for Analytics and Engineering
o Data Acquisition and Managing the “Data Lake”
- Planning for the future
o Operational Algorithms become MISSION CRITICAL
o Translation of Data Models from Conceptual to schemas for Object Relational,
Document, RDF, Graph, etc.
o Python v Scala v R, etc.
o Movement of data
o Architecture for timely, robust and sustainable operations
- Governance work and forums.
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Big Data needs more than just Data Scientists
Ken has nearly twenty years of experience in Big Data - contributing or leading projects integrating Big Data for reporting and analytics – working for large enterprises in Banking, Retail, Insurance, Telco, Shipping, Telco and Airlines. His latest project was creation of a Conceptual Data Model and Business Glossary to align development on Teradata and Hadoop and he has spent many months on learning and researching the newer technologies and their use by a variety of organisations. His work in Big Data built upon foundations of Business Management (Financial control and regulatory reporting), Operations and Marketing, in International and Retail Banking in the UK and Italy.