Speaking

Sample Keynote Topics

Applied Big Data Analytics:  Less research, more business value

How can we maximize the value drawn from our investments in Big Data Analytics?   In this talk, we discuss the three critical elements that must be in place:  Business Intuition, Experimentation and Analysis.  We show how agile approaches bring quick value while minimizing chance of failure.  In addition, we’ll show how ‘crowd-sourcing’ and ‘user-sourcing’ can be so valuable for analytic programs.

Case Study:  Starting a new Data Science program

What happens when a 30-year old company makes a strategic decision to start using data and analytics to empower growth?   In this case study, we’ll explore the challenges and opportunities faced by Dr. Stephenson when he was brought onboard by @Leisure, one of Europe’s leading online booking platforms, to launch a greenfield data science program during a period of triple digit growth.  We’ll discuss how we formed the analytics strategy, constructed the short- and long-term analytics roadmaps, and selected tooling, technology and vendors.  We’ll also talk about how we achieved perhaps the most difficult objective, the recruitment of a top-notch data science team.

Case Study:  Implementing a real-time streaming recommendation engine within two weeks

Recommendation engines are critical in today’s e-commerce sector.  They dramatically increase sales while simultaneously improving the customer shopping experience by helping customers quickly identify and locate the most relevant items.  Implementing such a recommendation engine in production, however, can be a very expensive and time consuming process.   In this case study, Dr. Stephenson will describe a recent project within the Axel Springer digital group where he and a team of three people creatively leveraged existing technologies in a way that enabled them to build a real-time streaming implementation on an existing ecommerce platform in only two weeks.  Neo4j was used as a key part of the solution architecture

Conversion Rate Optimization using Big Data and Data Science

 Conversion Rate Optimization (CRO) requires a multi-disciplinary approach fed by a broad data ecosystem. In this talk, we discuss ways in which Big Data and Data Science can provide valuable enhancements to CRO, particularly by enabling customer journey analysis and by expanding the insights related to user-centric hypothesis testing. We also talk about how data science is both complemented and supplemented by Big Data solutions and end by discussing an architecture used recently to quickly move a mid-sized company into Big Data capabilities and further enable a real-time recommendation engine with only a few weeks of development time.

The fastest way to bring Big Data to your online platform

Big Data technologies have advanced to the point where there is now a relatively easy way to become Big Data enabled.  Many companies already have 90% of the necessary infrastructure already in place and can become Big Data enabled with a minimal investment in technology, skills and time.  In this talk, we’ll discuss potentials and pitfalls on the road to becoming Big Data enabled and talk about some quick business value that can be harnessed from Big Data.

Recruiting Data Scientists

Managers and recruiters often express their frustration at the difficulty of recruiting data scientists.  Companies are more and more realizing that data scientists can add tremendous business value, but the candidate pool cannot support the surge in demand.  Leadership roles are particularly difficult to fill, yet play a critical role in bridging the gap between science and business value.  In this talk, Dr. Stephenson will draw on 20 years of industry experience in scoping and recruiting for a broad spectrum of analytic roles, focusing on the current state of the data science market.   He will discuss the challenges of scoping positions, finding talent, screening candidates, and convincing the best people to join your organization 

Data Science and Privacy:  Walking a fine line

Data Scientists can produce powerful insights from data, but many of these insights potentially cross the boundary between personalized and intrusive, even risking privacy laws violations.  Several high-profile cases have demonstrated the PR and legal problems than can arise.   In this talk, Dr. Stephenson will discuss some high-profile case studies and some underlying principles to keep in mind for analytics efforts.  We will also touch on the EU’s new General Data Protection Regulation (GDPR) and why it will have such a huge impact on companies in general and data scientists in particular.

How advanced analytics is transforming the modern customer journey 

Big Data analytics can have a tremendous impact on revenue, products and customer experience.  In this talk, we’ll see how your customer’s online behaviour is a leading source of big data and how big data innovators are personalising the online customer experience.  We’ll illustrate using some fascinating case studies of well-known companies that have used data and analytics to significantly grow revenue.

Why your data science program isn’t working

In a recent article published in the Harvard Business Review, 150 data scientists were asked who had deployed and evaluated a tool that added business value.  None raised their hands.  In this talk, we discuss three typical reasons why data science programs fail to deliver value and we suggest four changes to make in order to get the most value from your data science program.