Data Leaders Summit 2018
October 18, 2018

Data Leaders Summit 2018 – debrief on the grand finale

By Paul Laughlin

Let’s continue my debrief from chairing the Data Leaders Summit 2018 in Barcelona.

It has been impressive that, even after a late night of consuming beverages, attendance & participation has still been high. Data Leaders are clearly a hardy bunch!

All joking aside, the quality of both the speakers & audience questions have shown why this event is worth attending. Plus, the mix of keynotes, interviews, panels & workshops have kept the content fresh. 20 minute sessions (including Q&A) also help focus the mind.

So, without further ado, here again are my three points I took from the sessions I attended:

Hard lessons from the movie “Deep Impact” (Dyson)

The irrepressible Ryan den Rooijen, Global Director of Data Services at Dyson, shared these tips. They are based on the analogy of realising you are facing an asteroid of “inflated expectations of AI” in your C-Suite, heading for your current capability (c.f. Deep Impact movie):

  • Lesson 1: Ensure you avoid ‘good news reporting‘ and know what is really working & not working
  • Lesson 2: Be intentional about your comms strategy, to manage expectations & share progress
  • Lesson 3: Practice aggressive prioritisation, through being clear on real business priorities

Question: How well do your directors understand the alignment of your work with achieving top business priorities? Could you educate them better through tailored communication?

How to get out of the Machine Learning Lab (VW Financial Services)

Dr Alex Borek, Global Head of Data Analytics at Volkswagen Financial Services, shared these tips:

  • Recommend moving from a lab just delivering pilots, to a role that delivers scale/value in a business area
  • If you want more self-learning models, provide data on context & feedback loops (not just rebuilds)
  • To avoid failing to get out of lab, you need: (1) Data & AI platforms; (2) Data & AI processes; (3) Data & AI engineers; (4) Data & AI Ops; (5) Data & AI Product Manager.

Question: Do you have the roles needed in your team to succeed in scaling/deploying your analytics/AI? (A common theme at this conference was the need for ‘Product Managers’ – an evolution from Business Partner roles)

It’s all about People, so focus on them (Spence Partners)

Doug Spence, Executive Coach at Spence Partners, shared these tips:

  • As with the physiological, so with the psychological (you need flexibility & it takes time to break habits)
  • 80% of who you are won’t change, so learn how to play to your strengths most of the time
  • The new golden rule should be: “Do unto others as they would like done unto them” (segmented approach)

Question: Are you adapting your style to work for different stakeholders, do you know the style they value? (as an aside it was also encouraging to see an executive coach speak at this event – more validation of need for coaching data leaders)

Preparing for the future of using Big Data for revenue (Vodafone)

David Gonzalez Martinez, Head of Big Data Analytics & AI at Vodafone, shared these tips:

  • Research from Blackline has estimated value of data in your company is 15-35% of total revenue
  • Don’t just focus on revenue generation now (from analytics & AI), also invest in innovation for future (c.f. Nokia)
  • Be pragmatic in what you prioritise against value & do-ability (neither too easy or too hard & sufficient value)

Question: Are you taking time out to prioritise across the different potential business applications of your capability? 

Making money from use of AI & considerable data (Sky)

Rob McLaughlin, Head of Digital Analytics & Decisioning at Sky, shared these tips:

  • Vanilla is always wrong! (a priority is moving beyond ‘one size fits nobody‘ comms/services)
  • Tech vendors have done a good job of inflating expectations in C-Suite, so calm them down first
  • Design improved journeys for users based on behaviour & refine through usage (not testing)

Question: Could you be more ambitious in your use of existing data to personalise comms & services? Do you need to test first? (Rob said some controversial things about not needing insight or testing, but his challenge to take action was good).

Monetising your data to create new revenue streams (Trip Advisor)

Charlie Ballard, Global Director of Strategic Insights at Trip Advisor, shared these tips:

  • Learn to identify when you are sitting on a data asset that might be of value to others
  • Understand how your end customers change their behaviour as a result of using you (e.g. longer stays)
  • Think through what you might be able to uniquely bring to market as data giving insights (e.g. competitor set)

Question: Could you be offering a new service to existing or new clients, by using the data you already have? How will that be perceived by those who provided the data? (Trip Advisor & Google have mitigated risk through some public data sharing)

Monetising your data to help more clients (Carlson Wagonlit)

Eric Tyree, Chief Data Scientist at Carlson Wagonlit Travel, shared these tips:

  • Identify all the opportunities to monetise your data (sometimes in both directions on your value chain)
  • Keep close to GDPR guidance, even legal advice, to ensure you stay legal and fair (challenge is good)
  • Start by understanding the challenges of your key stakeholders, what are barriers for their business?

Question: How could your existing data help your key stakeholders? Could you charge for that data sharing or analytics on top (to break through their current limitations?)

Getting buy-in for new Data Science products (Uber)

Totte Herinen, Senior Data Scientist at Uber, shared these tips:

  • Identify Data Science methods that need to be scaled (it’s about ideas not just infrastructure)
  • You may need to develop a better platform for experimentation to achieve this
  • But, you can scale in different ways: platform; package; workflow; documentation or community

Question: How can you identify the Data Science methods you need to scale to achieve business priorities?

So, another great day that got us all thinking. Including a final panel on the need for us to take seriously both the threat of AI & the need for AI Ethics. All agreed this would be a key discussion point for future events.

Data Leaders Summit 2018 – were you there?

I hope you found that debrief useful. Were you there? If so, please also feel free to share your views or the actions that you noted. You can do so in comment box below, or via social media. I look forward to hearing the wisdom of this great community of data leaders.

If you weren’t there, I recommend you seriously consider attending in 2019. You can also see my debriefs from 2017 & 2016, to give you an idea of how this event has matured. Data Leaders need to keep talking to shape the future we all need.

I look forward to talking further with you in future.