More specifically, I was running a Masterclass entitled “Everyday Data Science“. The brief was to demystify this topic for a diverse audience. It was clearly popular as it drew a good crowd.
The delegates who attended my Masterclass varied from data analysts to directors. However, most worked in some branch of public sector services.
What do you cover when you get such a brief?
My first challenge was what to include. It is always tempting when dealing with a topic like this to rush into evidencing ones technical credentials. But all speakers should start by focussing on – what does your audience need?
In this case, I decided they needed an overview and specifically the ability to assess whether or not Data Science was relevant for them. To achieve the second half of that, I sought to focus on enabling them to assess their readiness for Data Science.
It was a good session with great involvement from those who participated. So, I would encourage those who work in South Wales to consider attending one of USW’s masterclasses. Further down this post, I have also shared the slides that I used via SlideShare.
The point of this blog post is to share the content choices that I made to answer this challenge. I hope it helps you if you need to communicate about Data Science to a non-technical audience within your business.
Make it personal, even for Data Science and AI
The first challenge for all speakers is the WIIFM of your audience. That’s “What’s In It For Me?” So, I chose to focus on them all as UK citizens.
I engaged them personally by sharing just an excerpt of some Data Science stories from the news. As well as one that was timely, this was deliberately a mixed diet of potentially positive and negative implications.
For instance, on the positive side, I shared about how Data Science has helped with reducing bias in marking at university, improving the energy efficiency of power stations and training the UK Womens’ World Cup football team.
To get delegates thinking about the potential downsides, I shared on the Brexit referendum, Crime Prediction & potentially racist search algorithms. It’s surprising how people relate to the risks to them even better than they engage with positive opportunities.
Understanding Data Scientists are different
One of the goals expressed by delegates at the start of my Masterclass was to understand how Data Scientists differ from Analysts. As we have shared before on this blog, many commercial businesses are also still confused about which they need.
To bring this to life, I shared both the inevitable Venn Diagram definitions of Data Science and why they thus need a wide range of skills.
This section gave me the opportunity to emphasise both the range of technical skills and softer skills needed. I have shared before on the benefits of having a competency framework. So, I shared the EDISON framework to understand the breadth of technical skills needed. This was complemented by my own Softer Skills 9 Step Model.
Finally, I wanted to alert delegates as to the ways that Data Scientists need to work differently. So, in this section, I shared both some of the popular Data Science methodologies and the relevance of Agile Working.
What are others doing with their Data Science applications?
These days everyone seems to want use-cases. To learn from other applications & avoid being “bleeding edge” innovators. So, I shared some applications that have impressed me.
This was a useful opportunity to highlight several of the case studies that I have heard eloquently outlined at other events. So, I drew on the following conferences:
- Customer Experience Conference (CXC) for Financial Services
- Day One of the latest Data Leaders Summit, Barcelona
- Day Two of the latest Data Leaders Summit, Barcelona
Delegates appeared particularly engaged with the implications of needing to think mobile-first in designing your applications. So, I shared some of the advice from “The Mobile Mindshift“.
Don’t overlook the fundamentals (Data)
To help delegates assess their readiness for Data Science, I covered a lot of data fundamentals.
Understanding the breadth of Big Data types and their potential relevance (and challenge for legacy systems).
It was also important to emphasise the critical need for Data Quality and the impact of GDPR. Both these topics helped clarify challenges that have still not been addressed by most delegates and could severely limit what a Data Scientist could do.
This section also included considering the importance of life events. How these provide a potential opportunity (as relevant “trigger” data) and make it harder to keep your data up-to-date.
This certainly identified some work still to be done by most delegates.
Recognise the growing popularity and opportunity of coding
I introduced the delegates to 3 potential Data Science programming languages (R, Python & Julia). To bring to life their growing popularity I shared my experience of finding that a guide to learning Python had pride of place in the magazine stand of my local Sainsburys.
We reviewed the ecosystems and resources available to help people learn and master these languages. I also pitched the importance of Data Visualisation skills for Data Science teams and we touched on some of the resources available to support Data Visualisers.
Test your own readiness
Finally, I revisited a post I’ve shared before from Mango software. In that post, they suggest the following steps to ensure you are ready for Data Science work:
- Ensure adequate access to the data needed (perhaps a Data Lake?)
- Ensure you have the software/tools that Data Scientists need
- Manage your domain knowledge, for access by Data Scientists
- Carefully manage your relationship with the IT department
- Ensure you have clarity on goals to achieve
Well worth checking out some of the other posts that I have linked to above, as those should help guide you through what to consider.
Helping you communicate Data Science to your audience.
As a final resource to help you, here are the slides that I presented to delegates at this USW Masterclass:
Have a great summer & hope to see you at a future event.