How do you define the size and growth of data sources and really make the most of the Big Data Opportunity? A company excelling in this field is eBay Research Labs. By using Big Data to understand their customers better they have boosted advertising revenue and minimised customer churn. Using Big Data, advertisers can really focus down to their core audience, whilst customers no long receive irrelevant advert or email.
What tools should you use? How do you communicate this opportunity to C-Level executives? Utilising Big Data correctly will bring the prospect of additional ROI and position your company ahead of your competitors. On Tuesday the 18th of September, Dr Neel Sundersan, Senior Director and Head, eBay Research Labs will present the opening keynote to Big Data World, focusing on the opportunities and successes utilising Big Data can bring to a company. To find out more about the conference, register for a brochure and once our programme is finalised we will email a PDF version to you: Register for a brochure
Seize the data: building success with Big Data
- Defining the size and growth of data sources and the opportunities these present
- Drawing on the successes of Big Data pioneers to understand the potential for achievement
- Utilising the newest tools to tackle our data obesity
- Why will moving Big Data to the core of your business ensure advantage over your competitors?
- Realising the massive ROI from data analytic strategies
âº Dr Neel Sundersan, Senior Director and Head, eBay Research Labs
His current areas of research interest include Social and Incentive Networks, Trust and Reputation Systems, Machine Learning as applied to Recommender systems, Classification, Ontology, and Search. He has been with eBay since 2005. Prior to joining eBay, he was a founder and CTO of a startup focused on multi-attribute fuzzy search and network CRM. Prior to this, he was the head of the eMerging Internet Technologies group at the IBM Research Center. There he built the first XML-based Search Engine. He was one of the early leaders in building XML technologies including schema-aware compression algorithms, application component generators, and pattern-match systems and compilers. He built the first RDF reference implementation as a W3C standard recommendation. He led research work in other areas like domain specific search engines, multi-modal interfaces and assistive technologies, semantic transcoding, web mining, query systems, and classification for semi-structured data. Prior to this he worked on C++ compiler and runtime systems for massively parallel machines and for shared memory systems and also on retargetable compilers, program translators and generators.