On Data Analysis. Interview with Rob Winters
“I’ve managed several employees who have successfully transitioned from an operations role to an analytics role. In fact, some of them have become my best analysts because they have brought a deeper domain knowledge to their analyses than someone approaching from the outside may have done. “–Rob Winters
I have interviewed Rob Winters,Head of Business Intelligence at TravelBird. The interview covers Rob`s projects experience with data analytics and HPE Vertica.
RVZ
Q1. What is the business of TravelBird?
Rob Winters: TravelBird builds and provides a daily selection of inspirational holiday offerings in twelve markets across Europe. Our goal is to create packages which excite the imagination and bring simplicity and joy to the act of travelling. These packages are then shared with our travellers via email, our website, and our iOS and Android applications.
Q2. What are the current data projects at TravelBird?
Rob Winters: TravelBird’s journey with being data driven is relatively short, beginning our initial Business Intelligence buildout in mid-2015. Currently our BI team is engaged in a number of projects, both more traditional BI and advanced analytics, including:
– Building data sources and training an organization in self-service BI
– Replacing our generic daily selections with personalized content selection models
– Optimizing pricing of packages based on product price volatility and customer demand
– Adjusting email frequency and timing to improve customer engagement and lifetime value
Q3. What is your experience in using predictive analytics?
Rob Winters: I have been working in the predictive analytics field for six years now across a variety of problem areas – customer service, retail, gaming, and now travel. From a technology standpoint I originally worked heavily with commercial solutions (Teradata, SAS) but for the last four years have used almost exclusively open source software including Hadoop, Spark, R, and Python.
Q4. How do you evaluate if your discovering insights are “good”?
Rob Winters: During the initial development of our algorithms we will typically follow a basic version of CRISP-DM to build an initial working model for our problem. To test models, we always use an A/B test and typically follow a two phase process: first the model is split-test against the current operational process/human selection, then when the model consistently outperforms the status quo, we will test future model iterations against the control.
Q5. Can you tell us a bit about the work you did in designing and implementing a fully automated, machine learning based content selection platform?
Rob Winters: To provide context, every day our planning team creates six unique product offerings for their target market of 50-500k customers to be shared via web, iOS/Android app, and email. Our goal was to replace that model with one that selects six unique products for each recipient based on past browsing and travel behavior. To do so, we designed an ensemble model consisting of several components:
– A customer preference model (user-item recommendation model)
– A product similarity model (item-item similarity)
– A “hotness” model to promote destinations which are trending/outperforming/expected to do well
– A portfolio model to select the right diversity for each recipient based on recommendation confidence, lifecycle state, and yield optimization of cannibalization vs product fit for a recipient
The data to feed these models is based on observing dozens of events per recipient per day, positive and negative feedback events of the recipient, all observable product features, and human expert input. The models are also able to improve themselves by continuously tuning the input parameters of each model based on recommendation split testing.
Q6. What are the primary technologies you are using?
Rob Winters: Our technology stack consists of the following:
-BI: Tableau
-Data warehousing: HPE Vertica
-Operations DBs: MySQL (web services) + Postgres (internal services)
-Recommendations serving: Redis
-Modeling/Analysis: Python, Spark via PySpark
Q7. What is your experience in using HPE Vertica?
Rob Winters: I have been using Vertica for five years in a number of organizations and facilitated the first rollout in the Netherlands. During that time I have been primarily an end user/data analyst but have also been the DBA for my deployments for the last two years.
Q8: Can you give us some more technical details of what was this first rollout in the Netherlands? What challenges did you solve in using HPE Vertica? What business benefits did you obtain?
Rob Winters: The objective of our rollout was to implement a centralized company datawarehouse to unify several production databases plus external API data.
The existing platform was Postgres (row-based solution) and relatively limited in performance. Primary gains were significantly faster analytics, the ability to add in several terabytes of event data (which was not possible on the prior platform), and new insights into the email database regarding churn, conversion, and customer value.
Q9: What were the main criteria for you to choose HPE Vertica? Did you do any performance test for HPE Vertica?
Rob Winters: We considered a number of alternatives including Microsoft PDW, Greenplum, and Infobright.
The primary considerations were price/performance, scalability, and analytical functionality. We found Vertica to be the best options across those aspects. Regarding performance testing, we did compare Infobright and Vertica and found the latter to be both more performant and easier to work with.
Q10. What specific functionalities of HPE Vertica do you find particularly useful in your job?
Rob Winters: There are a number of aspects which I find extremely beneficial, including:
-Ease of administration
-Performance tunability is very good, much higher than (for example) Redshift
-Analytical function extensions enable extremely powerful analyses directly via SQL
-The ability to load JSON data allows very rapid data integration from new sources
Q11. Do you think is it possible to turn an employee into a data analyst?
Rob Winters: Absolutely, I’ve managed several employees who have successfully transitioned from an operations role to an analytics role. In fact, some of them have become my best analysts because they have brought a deeper domain knowledge to their analyses than someone approaching from the outside may have done. The biggest drivers for success in the transitition have been:
– Attitude/eagerness to learn
– Close collaboration with a more experienced analyst, either their supervisor or a more senior peer
– Making their initial projects in areas where they are unable to fall back on domain knowledge
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Rob Winters, Head of Business Intelligence at TravelBird.
Rob has been working with and leading analytics teams since 2006 across a number of industries including telco, gaming, retail, and travel. His primary focus since 2011 has been green-field implementations of technology and team creation for both traditional business intelligence and predictive analytics; full details are listed on my linkedin profile. He holds a bachelor’s in economics and an MBA with a IT concentration.
Resources
– Data-X: Video lectures on very practical and applied Data Analytics. Data-X is a project to produce a collection of video lectures on very practical and applied data analytics.
– HPE Vertica 8 “Frontloader” BY Jeff Healey. ODBMS.org SEPTEMBER 12, 2016
–Benchmarking HPE Vertica and Amazon Redshift. (Webinar)
– HPE Vertica Analytics Platform on Microsoft Azure. By Chris_Daly. ODBMS.org SEPTEMBER 12, 2016
– Hewlett Packard Enterprise Introduces HPE Vertica 8. ODBMS.org SEPTEMBER 7, 2016
Related Posts
– On data analytics for finance. Interview with Jason S.Cornez. ODBMS Industry Watch, May 17, 2016
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