Our Top Five Tips For Telling Stories With Data

Sandra Namatovu our consultant managing the role
Posting date: 9/6/2018 8:54 AM
As the Data & Analytics marketplace continues to grow, what is it that makes a candidate stand out? More and more, employers are on the lookout for people with both hard and soft skills; those who cannot only interpret data, but possess the ability to translate and relay that data to key stakeholders. 

To convey data in a cohesive, informative, and memorable way, we need to think beyond making something aesthetically pleasing. People connect with stories, be they fictional, personal, historical or otherwise. By utilising universal storytelling techniques, we can share data in a way that people intuitively connect with. 

Here are our Top Five Tips for telling stories with data:

Start With The Structure 


Structures are the essential foundations that sit under any good story. Without a solid structure, the story we are telling can become confusing, distracting and unfocused. When presenting data, it is essential that we work to a clear structure to ensure that we can be understood. 

All stories feature three things; a beginning, a middle, and an end. A story told through data is no different:

  • The Beginning: What is the question that has been asked? What are we trying to learn from this information?
  • The Middle: The Data itself. What the numbers say.
  • The End: What insights can we gain from the data, what is the data really telling us?

By sticking to this structure, we can ensure that each bit of information gathered is explained with the relevant context required to convey the most information possible. 

When looking at several pieces of data, it makes sense to think of these as chapters. They may tell their own smaller story, but in the wider context of an overall narrative, they need to be in the correct order to make sense and not leave anyone confused. 

Speak To Your Audience


When presenting data, it is crucial to remember who your audience is. Whey they’re a novice, expert, or the chairman of your company, each individual has their own vested interested in what you are showing them. As a Data and Analytics professional, your job is to serve as curator, creating a story that feels tailored to each unique person. 

In order to help understand how your audience might be best served by your story, it’s helpful to ask yourself the following questions:

  • What information are the most interesting in?
  • What information do they need to know the most?
  • What is their daily routine? 
  • Is this their big meeting of the day, or one of several back-to-back?
  • What actions will they take off the back of your insights?

By asking these questions, you should be able to curate your data in a way that is meaningful for your audience. 

Find Your Characters


The majority of data is based upon an initial human interaction. From a video viewed, to a product purchased, it’s easy to forget that at the end of the line is a real human being. By bringing this to the forefront of your insights you create a compelling new way to connect with your audience.

Consider what this data actually meant when it was first gathered; who was that person and what does this information say about them? If you are able to create ‘personas’ or ‘characters’ from this data, you can present something tangible that people can connect and, potentially, even empathise with. 

Even if you use existing data to reference a personal experience, you’re adding a sense of palpability that gives your insights depth. 

Painting The Right Picture 


As Data Visualisers will tell you, the most elaborate visual is not always the most appropriate way to convey your insights. The key is to always consider what tells the story best. A heat map may be perfect for telling a story of geographical differences but is likely to make no sense when conveying a customer journey. 

The beauty of utilising different visual techniques is that they allow you to create an emotional impact with data, fully emphasising the meaning of your insights. David McCandless showcases how data can be visualised in various dynamic ways that create the most amount of meaning possible. 

Start Big, Get Smaller


Data presentations have the difficult challenge of needing to be both accessible and detailed. By ensuring that you have the big picture covered with enough context, you can ensure that everyone gets the headline takeaway. 

Following this, you can highlight further insights that reveal more information for those who need to do a deeper dive. Much like in a good story, whilst you may understand the overall narrative the first time round, looking closer and revisiting certain parts should reveal more insights and nuances. 

If you have the skills to turn Data & Analytics insights into compelling stories then we may have a role for you. Register with us or search the hundreds of jobs available on our site. 

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WE HAVE TO TEACH SPECIALISATION, WE CAN’T EXPECT IT: A Q&A WITH VIN VASHISHTA

We recently spoke to Vin Vashishta, a consulting Data Scientist and Strategist who was named one of LinkedIn’s Top Voices in Data Science.  Having started off in the tech world 25 years ago and progressing from web design and hardware installation to Business Intelligence Analytics, Vin found for many years that enterprises were reluctant to adopt AI technologies and embrace the value of Data. In fact, it wasn’t until the beginning of the decade just passed that companies started to think about their Data more strategically and the world of Data Science was born, albeit hesitantly:  “When I first started, it was a lot of experimentation, everyone wanted a proof of concept,” he says. “A lot of work was creating models that could go from whiteboard to production and productise and show their value.” However, it wasn’t until halfway through the decade that he began to see businesses who had adopted Machine Learning move away from experimentation into incorporating it more deeply into their companies, relying more on analytical and optimisation models to make strategic business decisions.  “After that, in about 2017/2018 the maturity changed. It went from being a one off implementation to it being a comprehensive tool within an organisation where we have full lifecycles of model implementation and full models that were full views of the system. The key component of development was allowing users to access a small part of the system to do their job better without having to understand the whole thing. And that’s where we are now. We have this applied Deep Learning and we are seeing, especially this year, attempts to optimise that, make things go faster and make them more repeatable.” But, as we all know, with great power comes great responsibility: “There’s this whole depth we are getting into, the expectations are so much higher, people don’t just expect it to work they expect it to work the way they want it to and in a way they can adopt.” So, with so much expected and required of Data Scientists in 2020, building the right team is more important than ever. However, many businesses, Vin believes, are yet to get their hiring processes right: “A lot of the measures that we use to sort of evaluate employees are fictional – when you say years of experience, it has no correlation to employee outcomes or the quality of employee you get long term. It’s the same thing as college degree, there’s no correlation.” So when Vin is trying to build a highly specialised team, what does he do? “We have to teach specialisation, we can’t expect it. We can’t bring someone in and call them a Data Scientist and hope that they train up. You end up with teams that are exactly the same because they have hired the same people, people who reinforce the bias of what they do, and that is where true leadership needs to come in.” A specialised team made up of individuals who bring their own ideas to the table is more important than ever, particularly as businesses demand more from their Data teams. Gone are the days of one-size-fits-all models. Businesses now want something tailored to them: “Custom models are huge. The “import from…” Machine Learning development from three years ago adds value when it comes to wrangling and doing the Analysis, but when it comes to creating models companies are now expecting it to become a competitive advantage. Companies no longer want the same model that everyone else has, now it has to be differentiating.” These smart, customised models, he adds, will help businesses through the current pandemic. “The best models right now are adapting rather than reacting.”  However, he’s sceptical about the Data Science community becoming too preachy:  “When it comes to COVID-19 one message I want to send to the Machine Learning and Deep Learning community is ‘shut up’. We don’t have the Data! We have so many Data Scientists talking about something that’s very important to get right. If you get it wrong the consequences and the credibility we will lose as a field is enormous.” Indeed, discussions about the lack of quality Data on COVID-19 are widespread at the moment and raise concerns for Vin: “What the last two and a half months has revealed is the danger of bad Data, the danger of assumptions that are hidden in Data that hasn’t been looked over well or wasn’t gathered well and was fed into these models that now aren’t robust. Of course, no model can account for something this drastic, but they should still be performing far better than they are right now.” Despite these concerns, Vin believes any change in the world brings about opportunities for those in the Data and technology space. “What I’ve been trying to do ever since I joined the technology space is figure it out. It’s constantly evolving and it’s constantly changing. That’s really what has driven my journey. I’m always trying to figure out ‘what’s next’ over the next five years, ten years whatever it may be.” If you’re looking for your next Data Science, Machine Learning or Deep Learning role, or want to build out your own highly-specialised team, we may be able to help.  Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.   

The Search For Toilet Paper: A Q&A With The Data Society

We recently spoke Nisha Iyer, Head of Data Science, and Nupur Neti, a Data Scientist from Data Society.  Founded in 2014, Data Society consult and offer tailored Data Science training for businesses and organisations across the US. With an adaptable back-end model, they create training programs that are not only tailored when it comes to content, but also incorporate a company’s own Data to create real-life situations to work with.  However, recently they’ve been looking into another area: toilet paper.  Following mass, ill-informed, stock-piling as countries began to go into lockdown, toilet paper became one of a number of items that were suddenly unavailable. And, with a global pandemic declared, Data Society were one of a number of Data Science organisations who were looking to help anyway they could.  “When this Pandemic hit, we began thinking how could we help?” says Iyer. “There’s a lot of ways Data Scientists could get involved with this but our first thought was about how people were freaking out about toilet paper. That was the base of how we started, as kind of a joke. But then we realised we already had an app in place that could help.” The app in question began life as a project for the World Central Kitchen (WCK), a non-profit who help support communities after natural disasters occur.  With the need to go out and get nutritionally viable supplies upon arriving at a new location, WCK teams needed to know which local grocery stores had the most stock available.  “We were working with World Central Kitchen as a side project. What we built was an app that supposed to help locate resources during disasters. So we already had the base done.” The app in question allows the user to select their location and the products they are after. It then provides information on where you can get each item, and what their nutritional values are, with the aim of improving turnaround time for volunteers.  One of the original Data Scientists, Nupur Neti, explained how they built the platform: “We used a combination of R and Python to build the back-end processing and R Shiny to build the web application. We also included Google APIs that took your location and could find the closest store to you. Then, once you have the product and the sizes, we had an internal ranking algorithm which could rank the products selected based on optimisation, originally were based on nutritional value.”  The team figured that the same technology could help in the current situation, ranking based on stock levels rather than nutritional value. With an updated app, Iyer notes “People won’t have to go miles and stand in lines where they are not socially distancing. They’ll know to visit a local grocery store that does have what they need in stock, that they’ve probably not even thought of before.” However, creating an updated version presented its own challenges. Whereas the WCK app utilised static Data, this version has to rely on real-time Data. Unfortunately this isn’t as easy to come by, as Iyer knows too well:  “When we were building this for the nutrition app we reached out to groceries stores and got some responses for static Data. Now, we know there is real-time Data on stock levels because they’re scanning products in and out. Where is that inventory though? We don’t know.” After putting an article out asking for help finding live Data, crowdsourcing app OurStreets got in touch. They, like Data Society, were looking to help people find groceries in short supply. But, with a robust front and back-end in place, the app already live, and submissions flying in across the States, they were looking for a Data Science team who could make something of their findings.  “We have the opportunity,” says Iyer “to take the conceptual ideas behind our app and work with OurStreets robust framework to create a tool that could be used nationwide.” Before visiting a store, app users select what they are looking for. This allows them to check off what the store has against their expectations, as well as uploading a picture of what is available. They can also report on whether the store is effectively practising social distancing. Neti explains, that this Data holds lots of possibilities for their Data Science team: “Once we take their Data, our system will clean any submitted text using NLP and utilise image recognition on submitted pictures using Deep Learning. This quality Data, paired with the Social Distancing information, will allow us to gain better insights into how and what people are shopping for. We’ll then be able to look at trends, see what people are shopping for and where. Ultimately, it will also allow us to make recommendations as to where people should then go if they are looking for a product.”  In addition to crowdsourced information, Data Society are still keen to get their hands on any real-time Data that supermarkets have to offer. If you know where they could get their hands on it, you can get in touch with their team.  Outside of their current projects, Iyer remains optimistic for the world when it emerges from the current situation: “Things will return to normal. As dark a time as this is, I think it’s going to exemplify why people need to use Artificial Intelligence and Data Science more. If this type of app is publicised during the Coronavirus, maybe more people will understand the power of what Data and Data Science can do and more companies that are slow adaptors will see this and see how it could be helpful to their industry.”   If you want to make the world a better place using Data, we may have a role for you, including a number of remote opportunities. Or, if you’re looking to expand and build out your team with the best minds in Data, get in touch with one of expert consultants who will be able to advise on the best remote and long-term processes. 

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