presenting online
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Virtual events will be a constant fixture on our calendars for the remainder of 2021, and are set to be just as popular once Covid calms down. The upside to this is the ability for speakers to present from anywhere in the world, as well as making events more accessible to attendees.

Presenting to nothing but your screen can be a daunting experience – you have no audience to gauge how your talk is being received, and you don’t have a room full of people to help energise your talk – but this doesn’t need to mean a dull and unengaging presentation!

  1. Testing, testing, 123!

Events are held over a variety of platforms, so ensuring you are comfortable with the software before the live event is essential. If you’re the event host, set up a short tech test call with your speakers in the days leading up to your event, so your speakers can test the software and make sure their presentation looks right. As a speaker, it is worth practising your presentation before you go live – you want to know what’s coming up but not follow a rigid script or read word for word what your slides say. If appropriate, your tone should be friendly and conversational – imagine you are presenting to friends.

2. Perfect your setup

As mentioned in point number one, as a speaker you need to ensure people can hear and see you clearly. You don’t need a fully professional set up to achieve this, but using headphones is considered best practice when speaking as it eliminates the risk of a pesky echo when you talk. Not everyone can present with a perfectly set background (or posed bookcases!), but do take a moment to consider what is going on behind you so it’s not distracting. It may be worth considering standing as you present as well, as this will help you present with energy. If you are coding or typing, make sure your microphone isn’t too close so that your audience isn’t startled by loud typing.

3. Is anyone there?

It’s hard to imagine a crowd watching you when you talk at your computer, but it’s important to remember your audience and try and connect with them as much as you can. You could start your talk by asking your audience a question, and have them reply if you have a chat function – you could run a poll mid-way through, or even stick a photo of an audience up behind your camera so you feel the sense of occasion that presenting gives. If you have a camera, you should try to make eye contact with it as much as possible so that the audience feels addressed by you. When we host LondonR, a nice way to encourage some chat is to simply ask where everyone is joining from (surprisingly not just London) which leads to a relaxed start to the event.

4.  Take a break

There’s lots of research that suggests that we will only pay attention to something for 10 minutes or less, so with this in mind, if you’re presenting a long session, make sure you let the audience have breaks. If possible, for a long session, plan segments for interaction. By asking the audience to interact you will keep them engaged, and also feel part of the event.

5. Accessibility

One of the best parts of presenting online is that more people can usually attend your event – it’s cheaper for them, they don’t need to travel and they can fit into people’s busy days easier. Accessibility for talks is something that should be looked into for presenting online and in-person, Joselyn Chavez has put together a fantastic guide on how to make sure your presentation is accessible.

6. Record and review

If presenting online is a regular fixture in your diary, take some time to watch back one of your presentations and note what you have done well and what you could improve on. Presenting and public speaking is a skill that can serve you well in your career and it’s also a skill that can continue to be fine-tuned. When stuck for inspiration, it’s always worth seeing what other people are doing, attending someones else talk can leave you with new ideas and an energised approach to presenting.

We hope these tips help you with presenting online. If you have never presented before, it’s worthwhile checking out where your local R meetup group is and offering a talk there – it will be a friendly place to start your presenting practice.

If you’re aiming to present more this year,  then consider submitting an abstract to present at this year’s EARL Conference – which is focused on the commercial use of R. The Enterprise Applications of the R Language Conference will be held online again in 2021, last year we had over 300 attendees join us for a day full of brilliant R based presentations – and 2021 could feature you! The abstract deadline is 31st March 2021 – if you have any questions please tweet the EARL Team.

managed service
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As a language, R can come with barriers when it comes to the implementation and necessary technical know-how of installing, configuring, and supporting a centralised data science platform. As a full service Certified RStudio partner in Europe, we have introduced a new service to help overcome such technical hurdles associated with scaling your R environment.

Designed to be up and running effectively almost immediately, this expert Managed Service, removes the need for specialist in-house IT expertise and guarantees a service level agreement to meet your requirements in terms of configuration, maintenance and system updates.

Is Managed Service for you?

A Managed Service RStudio environment gives you the benefit of a quick and effective cloud environment, run and maintained by Mango Solutions to minimise client responsibility. It presents a solution for reducing concerns of supporting an appropriate infrastructure for data science teams, allowing them to focus their valuable time on vital project collaboration and their core area of responsibility, rather than needing to have any concerns over their system configuration and maintenance.

The benefits an RStudio Managed Service provides:

  • Quick and effective installation – the environment is setup in the Cloud, negating the need for Linux experts within your business
  • Outsourced management – guaranteeing an excellent service level agreement (SLA) with automatic updates, managed maintenance, and reporting
  • Option for pre-installed validated packages – using ValidR® the solution maximises assurance of a compliant environment and provides the reassurance of knowing the code is robust, effective, and reproducible
  • Predictable low cost – outsourcing complex solutions ensure simplified budgets and costs
  • Proven expertise – provides levels of support to run and maintain to meet your business, reducing the time and support from your over-burdened IT teams
  • Option to engage with additional Data Science services to grow your knowledge and productivity

Keen to know more? We can demonstrate how this is already providing an effective solution within Government departments – talk to our Managed Service team.

adding data thinking to software solutions
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Whilst the world of Data and AI offers significant opportunity to drive value, knowledge of their potential and mechanics are mostly confined to data practitioners.

As a result, when business users look for solutions to their challenges, they are typically unaware of this potential.  Instead, they may ask technical teams to deliver a software solution to their problem, outlining a capability via a set of features.

However, when making this leap we risk missing out on the opportunity to build more effective systems using data and analytics, creating “part solutions” to our challenge.

Let’s use a real-life example to illustrate this …

Case Study: Customer Engagement

One of our customers is a major financial services firm, which has a number of touch points with their B2B customers.  This can include a variety of interactions including customer support calls, service contract renewals and even customer complaints.  These interactions are driven by their large, globally dispersed customer team.

The goal of the customer team is to increase retention of their high-value customers and, where possible, to upsell them to more expensive service offerings.  As such, they see that every interaction is an opportunity to build better relationships with customers, and to suggest compelling offers for new products.

Let’s imagine the head of this customer team looks for support to better achieve their aims … 

Software-first Projects

A classic approach would be for the customer team to turn to the world of software for support.

Knowing the possibilities that a modern software system can bring which puts all the information about a customer in front of the customer team member during interactions (akin to a “Single Customer View”).  This information could include:

  • General customer details (e.g. sector, size)
  • Purchasing history (e.g. services they current subscribe to, volumes)
  • Usage (e.g. how often they use a particular product or service)
  • Recent interactions (e.g. what happened during the last interaction)
  • Offers (e.g. what did we last offer them and how did they react)

This could create an invaluable asset for the customer team – by having all of the relevant information at hand they can have more informed discussions.

However, the customer team still needs to fill the “gap” between being presented information and achieving their goal of customer retention and product upsell.  They do this using standard scripts, or by interpreting the information presented to consider appropriate discussion points.

So while the software system supports their aims, the human brain is left to do most of the work.  

Data-first Projects

In the above example, the head of the customer team didn’t request a software system – instead, she turned to an internal data professional for advice.  After some conversations, the data professional identified the potential for analytics to support the customer team.

They engaged us with the concept of building a “next best action” engine that could support more intelligent customer conversations.  Working with the customer team and the internal data professional, we developed a system that presented the relevant information (as above), but crucially added:

  • Enriched data outputs (e.g. expected customer lifetime value)
  • Predicted outcomes (e.g. likelihood that the customer will churn in next 3 months)
  • Suggested “next best actions” (e.g. best offer to present to the customer which maximised the chance of conversion, best action to reduce churn risk)

These capabilities spoke more directly to the customer team aims, and demonstrated a significant uplift in retention and upsell.  The system has since been rolled out to the global teams, and is considered to be one of a few “core applications” for the organisation – a real success story.

Software vs Data Projects

It is important to note here the similarities in the delivery of the system between these 2 approaches: fundamentally, the majority of the work involved in both approaches would be considered software development.  After all, developing clever algorithms only gets you so far – to realise value we need to implement software systems to deliver wisdom to end users, and to support resulting actions by integration with internal systems.

However, the key difference in mindset that leads to the approaches described are driven by 2 characteristics:

  • Knowledge of the Data Opportunity – a key factor in the above example was the presence of a data professional who could empathise with the head of the customer team, and identify the potential for analytics. Having this viewpoint available ensured that the broader capabilities of software AND data were available when considering a possible solution to the challenge presented.  Without access to this knowledge, this would likely have turned into a “single customer view” software project.
  • An Openness to Design Thinking – in the world of software design best practices, there are 2 (often conflated) concepts: “design thinking” (empathise and ideate to develop effective solutions) and “user-centred design” (put the user first when designing user experiences). In software-first projects, the focus is often on the delivery of a solution that has been pre-determined, leading to a user-centred design process.  When we consider the world of data, the lack of understanding of the potential solutions in this space can lead more naturally to a “design thinking” process, where we focus more on “how can we solve this challenge” as opposed to “how do I build this software system really well”. 

Adding Data Thinking to “Software-First” Projects

So how do we ensure we consider the broader opportunity, and potential that data and analytics provides, when presented with a software development project?   We can accomplish this with 3 steps:

  1. Enable a Design Thinking Approach

Design thinking allows us to empathise with a challenge and ideate to find solutions, as opposed to focusing on the delivery of a pre-determined solution.  Within this context, we can focus on the broader aspirations, constraints and consequences so that a solution can be considered which connects more closely to the business outcomes.

  1. Include Data Knowledge

During this design thinking activity, it is essential that we have representatives who understand the potential that data and analytics represents.  In this way, the team is able to consider the broader set of capabilities when designing possible solutions.

  1. Design the Data flow

Data is always a consideration in software design.   However, the potential of analytics requires us to think differently around the flow of data through a system with a view to delivering value-add capabilities.  This takes us beyond thinking about how we store and manage data, and towards a situation where we consider new data sources, data access, and the lifecycle of model-driven data outputs (such as predictions or actions).  This is particularly important where the “data” opportunity may be added to a system at a later date, once core “nuts and bolts” functionality has been delivered.

Data + Software + Design Thinking

The approach described here enables us to leverage the opportunity that resides on the bounds of data and software, and fundamentally deliver more value to users by delivering richer capabilities more aligned to business outcomes.

Moreover, we’ve seen that effective application of design thinking, combined with deep knowledge of data, analytic and software, has enabled us to deliver significant value for customers that goes way beyond solutions that may have been originally imagined.

Author: Rich Pugh, Chief Data Scientist

 

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The Enterprise Applications of the R Language Conference is back for 2021!

EARL will be held online again in 2021. We are planning workshops from 6-9th September, and a whole conference day on Friday 10th September.

For those of you who haven’t attended EARL before, this conference focuses on the real-world usage of R. A huge variety of sectors are represented, and talks can strike up inspiration no matter what industry you work in. At our in-person events, over 300 hundred R fans join us in London for three days of talks, workshops and networking. In 2020, we took EARL online and saw over 400 R users join us for a day of presentations.

We are pleased to announce we are now accepting talk abstracts until 31st March. If you need any ideas or encouragement, take a look at our Youtube page to see recordings of past talks, or click here to watch last year’s EARL presentations.

This year, we are interested in showcasing talks that showcase the commercial use of R in:

  • Business use cases of R
  • Python and R
  • Green R – using R to better the environment
  • R in Production
  • Using R to understand COVID
  • R for Automation (automation of data pipeline, automation through package building etc)
  • R Packages developed for business
  • Shiny

We are also looking for 10-minute lightning talks on:

  • Data for good – the use of R in addressing, measuring or solving issues to better the world.

We look forward to receiving your abstracts – please submit them here.