HER+Data Kickoff Event: Meet Amazing Data Women

On May 17th I organized a kickoff event for the HER+Data community hosted by Intercom, Dublin. And what a kickoff it was! The theme of the kickoff was to give attendees the opportunity to meet some amazing women who work with data and hear their inspiring career stories. The goal was to showcase that  there are multiple paths and diverse backgrounds that lead to a career in data. We had talks from:

  • Niamh Kirk, a Media researcher working on a PhD in the field of journalism, culture and migration; a media analyst and multimedia journalist;  founder of The Media Editor blog and contributor with The Institute for Future Media and Journalism (@FujoMedia)
  • Flora Devlin, Product Analyst @Intercom, who helps product teams use their data effectively and understand it thoughtfully. A background in Maths & Economics, Flora is passionate about strategy and loves all things related to travel.
  • Martina Naughton, Data Scientist @ZalandoTech with over 10 years across industrial and academic research. Martina has a Computer Science background, is passionate about machine learning and data mining, and is a big travel and sports enthusiast!

I also spoke at the kick-off event and shared my experiences working in Yahoo, Telefonica and now Intercom. When I founded HER+Data I didn’t really know if this community would be interesting, welcomed or useful for other women working in data. I suspected it would be, but you never really know! To my surprise > 30 women turned up for our kickoff event. The talks was super, the conversation flowed and feedback from the ladies that turned up was super. I’m so excited to keep the momentum going and seeing how far this community can go over the next year!

Founding of HER+Data

Her+Data LogoI recently founded HER+Data, a group for women who work with and love data. My goal is to bring together women who work with data or who would like work in data – to support one another, share experiences and talk data. I’d like to connect and inspire amazing data women with diverse expertise and experience. So artists, analysts, scientists, students, developers, researchers, designer,s journalists, leaders and entrepreneurs.  HER+Data is a space where everyone can share their stories and develop. Our events are simple and informal. There will be great content, laughter and a room full of data women from various backgrounds. So come along, ask questions, and get inspired! And if there are specific topics that you would like to see discussed then please let us know. We are always happy to explore new ideas 🙂 You can join the community via our Meetup page.

1st Interversary in Intercom

IMG_4298Wow! Today marks my 1 year anniversary in Intercom 🙂 And what a year it’s been. I’ve moved jobs and countries, been challenged frequently and I have learned a huge amount. Bring on the next year!

Intercom has this wonderful tradition of creating an amazing personalized illustration of you for each Interversary. They collect interesting facts about you from your team and colleagues, and then a wonderful designer, Sebastian creates something like this!! Beautiful!!

Intercom’s 1st Data Report! Emoji Trends in Business Messages

Hero_Emoji-Report

Today I published Intercom’s first ever data report with my colleague Sara Yin where we explored emoji trends in business messaging. We analyzed more than two million anonymized conversations that took place between our customers (i.e. businesses) and their end users (i.e. consumers) during a 3-month period, from June to August in 2015 and 2016. Below is a just a couple of the interesting things we found. You can read the full report here and a fun blog post here.

#1 Emojis improve message engagement

We discovered that messages started by a business that contained an emoji were four times more likely to elicit a response from a consumer than those that didn’t!!

#2 The Top 20 Emoji

The top 20 emoji found in messages in 2016 were quite similar to ones you might use in your personal life. Category-wise, over a half (51%) of the top 20 emoji fall under a facial category, followed by object-based emoji (18%). As you might expect in a business setting, money-related emoji also featured (11%).

top20_2016-v21

When we compare the top 20 emoji used by businesses vs. consumers we find some real differences! Consumers used facial emoji 30% more than businesses did (83% vs. 51%); businesses stuck to objects (18%) and money symbols (11%), neither of which show up in the consumer list.

Pie_Chart_Blog

Predict Conference – Great Analytics Starts with Great Foundations

Predict is an analytics conference that took place in Dublin’s RDS in October 2016. Its mission was to:

mobilise an international community to solve important human challenges through the power of data and predictive analytics.

The theme of this year’s event was the Journey from Data to Predictive Analytics.

It was my first time to attend and present at the conference and I wasn’t disappointed! I gave a talk on what it means to deliver great analytics and shared the 4 key areas that my team and I in Intercom have been focusing on to build a strong analytics team. Specifically:

  1. Foster an open feedback culture
  2. Develop close partnerships
  3. Use a common language
  4. Educate

RecSys 2016 – Highlights

This year’s RecSys was jam packed and fantastic. The dual track 3-day conference took place in beautiful MIT in Boston and showcased lots of great talks around advances in recommendation system algorithms and approaches.  Below are some of my highlights from the conference.  Full proceedings are available here

#1 Awesome Industry Track

I co-chaired the industry track along with Paul Lamere, Director of Developer Platform at Echonest / Spotify and Hrishi Aradhye, Engineering Director at Google. This involved selecting and curating a set of industry track talks / speakers from a diverse range of companies who actively work in the recommender systems space.  The industry track resulted in a set of 15 talks across 3 sessions featuring speakers from Mendeley, Meetup, Bloomberg, Foursquare, Spotify, Netflix, Pandora, Stitch Fix, Expedia, Nara Logics, GraphSQL, Retail Rocket, Quora, Google and Pinterest. Here’s just some of the awesome industry track talks that were presented.

#2 Record Attendance at Women’s Lunch!

I co-organized a women’s lunch with Tao Ye, Principal Scientist at Pandora. We had a record number of women attend (almost 50). The lunch resulted in a range of action items for next year’s conference including (hopefully) the organization of day care and a listing of female speakers so that future organizers can choose from a set of talented female researchers and practitioners who want to speak at conferences.

RecSys2016_WomensLunch

#3 Combining Machine Learning with Human Curation

A prominent theme at this year’s RecSys is the combination of machine learning techniques with large-scale human curation to improve recommendations. Stitch Fix, a clothing delivery service where customers get their own personalized selected of five clothing items called a “Fix”, gave a great talk on this topic. Stitch fix employ over 3000 stylists across the globe! They have a large algorithms team who build recommendation algorithms to help the stylists choose what to send to customers. Customers keep only what they like and send back the rest so it’s important that the recommendations are good! Katherine Levins, a data scientist from Stitch Fix talked about how they approach understanding, measuring and optimizing the role of human selection in a recommendation system. It turns out that they employ a combination of cognitive research, eyetracking, and machine learning models to tune the behavior of stylists. As data related initiatives like merlin take shape in Intercom, it feels like this combination of algorithm and human curation is something we could learn from and try going forward. Katherine’s talk is available here. Earlier this year Katherine published a great related blog post.

#4 Deep Learning

There were lots of talks around deep learning at this year’s RecSys, with several papers accepted accepted to the main conference track and an entire workshop dedicated to deep learning for recommender systems. Paul Convington presented a really interesting talk on deep neural networks for YouTube recommendations. The problem he’s working on is how to predict what movie a user will want to watch next. He discussed how they’ve implemented an age feature designed to remove the bias towards recommending movies / videos from the past. What was most intriguing is that despite the promise of deep learning to advance the field of recommendation, he said that they still have to do lots of feature engineering in YouTube. And overall they have found that user’s interactions with similar items are the best features for improving recommendation.

#5 The Explainability Spectrum & Signal Decay

Shashi Thakur, a Distinguished Engineer and head of the Google Now team gave an interesting keynote on personalization, recommendation and exploration in Google Now. Shashi talked about the importance of setting the right expectations for users and explaining why recommendations are being made at a given point in time. He talked about the explainability spectrum where on one end a recommendation is so clear that it doesn’t require any explanation, and on the other end the recommendation is higher risk / less clear and so needs a clear, concrete explanation. Depending on the circumstances surrounding the recommendation and the person for whom the recommendation is being made, the spectrum of explainability will shift in one direction or the other. I thought this was interesting to consider in the context of merlin and how we surface merlin’s recommendations.

Another interesting point raised across a few talks (including Google and Netflix) related to signal decay. The general advice is to revisit recommendation features often because signals that were once useful may not longer be as useful / as impactful as when the recommender system was first built.