Apprenticeship Levy, Data Analytics

Data Literacy – it’s not all about hard skills, soft skills matter just as much

06 October 2020 by Isla Lightfoot

The words ‘Data skills’ immediately bring to mind a range of hard skills – the convergence of mathematics and statistics, but data science is essentially a human pursuit. 

The importance of developing soft skills alongside these technical skills is essential – after all, technical excellence can only be of true value, if it is presented in a way that other – less technical people, can understand. Data Analysts must develop communication, negotiation and persuasion skills to lead teams and projects successfully in commercial environments.  

Soft-skills training is imperative within a Data Analytics programme.  

Here are the core soft skills that programmes should include:  

1. Communication: This is by far the most important and generally most neglected soft skill for Data Analysts. A data scientist should have a knack for linking business orientation with the scientific, analytical, and technical elements. They then need to present their findings in a non-technical, business orientated way, thereby promoting and increasing data literacy across their organisation. Simply put, if colleagues and senior leaders cannot understand the findings, they will not act upon them. 


  • Distilling the objectives and output of data analysis in to one or two non-technical sentences.  
  • Know your audience – not just whether they are technically literate, but also what’s on their mind. Senior Leaders, for example, focus on the bottom line, so how do your findings improve efficiency or increase profitability?  

2. Storytelling through Data Visualisation: Storytelling enables data scientists to communicate their results coherently and understandably. It takes data visualisation to another dimension, allowing decision-makers to see things from a new perspective. Successful examples of data visualisation storytelling include: 

The John Snow cholera map, which was essentially an early dot map visualisation, using small bar graphs on city blocks to mark the number of deaths in a neighbourhood. This visualisation was produced to discover why some areas had far higher death rates than others. This led to the discovery that these households were drinking from the same contaminated well. This hugely successful visualisation revealed a root cause problem and inspired a solution

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Obama’s Budget interactive treemap, visually broke down the US 2016 budget to put government programmes into context. This simple visualisation took a notoriously obscure and tough to understand concept and accessibly communicated with taxpayers about where their tax dollars would go.

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A compelling storytelling approach builds a strong data narrative so that stakeholders attain a new sense of clarity and identify the best course of action. 

3. Building communication networks to share findings: This comes under the ‘persuasion and negotiation soft skills area’. It’s a lot easier to persuade and negotiate with people you already have a relationship with. Networking isn’t always something that comes naturally to Data teams. Conversely teams who would find Data outputs extremely useful very often avoid Data teams because they believe the subject matter is too complex to comprehendData programmes should build analysts who can be the conduit between the data team and other departments. They should also ensure that by the end of the programme the Data Analyst understands their own organisation better and feels confident that they can build a network to disseminate their immensely powerful information, so that it translates into improved business performance.  

4. Critical thinking: Critical thinking is a core skill enabling data analysts to perform objective analysis for a given problem. Critical thinking skills involve framing questions correctly and determining how their findings can help an organisation chart a course of action. It is the art of the curious – looking from all angles before establishing a conclusion and therefore removing bias.  

5. Working within a team: Data Analysts generally work in teams, so effective team building, and collaboration skills are essential, and within any skills programme delivery there should be the opportunity to work in teams.  

Soft skills are future proofAutomated solutions including AI, Robotics and Machine Learning find it extremely difficult to replicate soft skills. They are an essential part of data science performance, so should be given considerable weighting within Data Analytics skills programmes.  

Core units within all Corndel’s Data Analytics Diplomas contain soft-skills development and the personalised coaching at the heart of every Corndel programme, enables Corndel’s Data learners to develop these skills supported by professional coaches.  

Find out more about Corndel Data Diplomas.

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