If you want to succeed as a data analyst, a core skill that you need to perfect is the ability to explain complex data concepts to people who lack your expertise. Yes, you’ll need to have knowledge, skills, and experience in working directly with data. But being able to communicate that expertise is just as important when you’re working in an organization.
However, developing communication and presentation skills can be difficult. People who are new to the world of data often focus on developing their technical competencies, meaning that communication skills are left behind.
That’s why we’ve put together this comprehensive guide to simplifying data concepts for non-technical stakeholders. By following our top tips, you can quickly improve your data communication skills to help grow the data literacy of your entire organization.
Why is it important to explain data concepts to non-experts?
When you’re working as a data scientist, you’ll find that you’ll need to use a variety of communication channels to speak with lots of different stakeholders. This means that you’ll need to adapt your communication strategies according to who you’re speaking to.
This is vital as everyone will have differing levels of technical experience: on a given day, you might begin by giving a presentation to a team of IT support assistants who have a substantial amount of non-specific data expertise and then end with a call to business leaders who lack anything above the basics in data knowledge.
It’s a crucial part of the job role of a data analyst to work with non-technical stakeholders in this way. Data is becoming increasingly central to the work of organizations, meaning that you will have to communicate with people from the bottom to the top of your company – all of whom deserve access to your expertise in a way that suits their prior and current knowledge.
Different communication channels for data analysts
Before we get into the details of simplifying data science for colleagues who aren’t as technically proficient as you, it’s important to consider the different ways in which you’ll be communicating with other stakeholders.
One-to-one meetings
If you’ve been assigned to work on a specific project, it can be common for you to give feedback on that project in a one-to-one meeting with the team leader. You’ll have to be prepared by knowing your work in detail.
These meetings generally require skills such as conversational abilities and emotional intelligence rather than presentation skills: you’ll need to speak about the data and guide the team leader through the information that you’ve analyzed.
Team meetings
Similarly to one-to-one meetings, you’ll also almost certainly be asked to contribute to team meetings. These are simply meetings where you’ll be speaking with multiple non-technical stakeholders, such as a formal board meeting or a more operational team meeting.
You’ll need to utilize a range of communication skills in these meetings. As well as being able to verbally explain data concepts, you might be asked to give short presentations or walk people through a data model in real-time. You might need to use VoIP technology to meet with people in different locations, so make sure you know how to use the platform and can answer to your own satisfaction “how does VoIP work for business?”.
Presentations
Rather than working one-on-one or with a relatively small group, you might also be asked to deliver presentations to large groups of employees or clients. This will be a less conversational format than meetings, as there will be fewer opportunities for the audience to interact directly with you.
You will need to make sure that you simplify the data so that everyone in the room can access it, and this can be quite difficult when you’re speaking to a large audience. You’ll also want to draw on images, models, and slides to help you to present the data.
Data visualization
While you’ll probably be using data visualization in all of the above forms of communication, this is a key skill that you’ll be using throughout your time as a data analyst. You’ll need to turn abstract data into simple and easy-to-understand visuals, such as graphs or illustrations, to help everyone understand the meaning behind the data.
Simplify data concepts: Best practices
Here are our top tips for simplifying data concepts for non-technical stakeholders.
Assess existing knowledge
Before you start any data communication, you need to make sure that you’re beginning your explanations from an appropriate level of expertise for your audience. After all, you don’t want to annoy a team of specialists by pitching your instruction too low – or pitch it too high and leave your audience completely clueless.
Practical ways to get more detailed information about your audience’s existing knowledge include sending out surveys before you give a presentation and collecting feedback once you’ve talked with an audience, which will allow you to adapt your communication in future.
Break concepts into chunks
When you want to simplify a complex data concept, it can be tempting to miss out on key information because you think it will be too difficult to explain. However, this simply leaves your audience with an incomplete understanding of the data that you’re trying to communicate.
Instead, you should break down each concept into manageable chunks. Explain each chunk in depth, using examples and allowing the audience to ask questions about each individual aspect. Then, link the chunks together to explain the concept as a whole.
Introduce web scraping
Web scraping is a technique used to extract large amounts of data from websites, which is then used for various business analyses. Understanding how data is gathered from the web can be crucial for stakeholders to appreciate the starting point of data analysis processes.
This can be essential for gathering competitor information, customer reviews, or even product details, which are critical for market analysis or strategic planning.
Practice explanations
Before you give a talk or lead a meeting, take a moment to plan how you will explain some of the core aspects related to the relevant data concept. Practicing this in advance will mean that you appear well-prepared and confident, while also ensuring that your explanations are matched appropriately with your audience’s prior knowledge.
Make the most of examples and analogies
Relating difficult concepts to everyday ideas can be a great way to convey complex knowledge about data to your audience. An example of this is by comparing machine learning to your experience of learning to drive – you got better through trial and error, just as machine learning algorithms make their predictions more accurate.
Base your metaphors and analogies around ideas that are genuinely relevant to the other stakeholders, which will help them to understand easily. You should also be careful to keep examples and analogies simple – just as jokes that need explaining aren’t funny, an analogy that requires another level of explanation simply isn’t worth it!
Avoid jargon if possible
Put yourself in the shoes of someone in the audience. How would you respond to a presenter if they kept using vocabulary that was essentially in a foreign language? That’s why it’s generally advisable to avoid jargon.
However, sometimes jargon will be unavoidable, such as when you’re trying to explain something like “What is CCaaS?”. If this is the case, you should use it only when necessary and prepare a simple definition of the term that will allow everyone in your audience to fully understand what you’re saying.
Of course, what counts as jargon will depend entirely on your audience. Delivering a talk to software procurement experts won’t require an explanation of the term ‘license management’, but you probably would need to avoid or explain data-specific phrases such as ‘data validation’.
Ensure that visualizations are effective
Visualizing data is a crucial part of explaining data concepts to non-specialists. After all, few things are more likely to put off a non-technical stakeholder than just presenting a load of raw data. Well-designed data visualizations can make complex data easily understandable for your audience.
You should be able to draw from a range of different visualization techniques, such as presenting data through bar charts or line graphs. You might also want to think about how you use color: a consistent color scheme can help people recognize patterns and become more quickly familiar with a new dataset.
Additionally, using data testing tools can ensure the accuracy and reliability of the data before it is visualized, thereby enhancing the trust stakeholders place in the visual representations provided.
Refine your general communication strategies
While most of our tips have focused on the specific issue of communicating data concepts, you should also keep in mind that there are some general ways to improve communication. Using storytelling and anecdotes, for example, enables you to maintain engagement while also ensuring that you’re not simply reading from some slides.
As well as this, consistently make eye contact with your audience. If you’re not working with a very large audience, try to engage in a genuine conversation with the other stakeholders – this will build trust and help you to see how well the audience is responding to your explanations.
Explaining data concepts: A key skill for data analysts
Effectively conveying complex ideas about data is a crucial part of the job description for most people who work with data. That’s why our practical guide to explaining data concepts is so useful – follow our key tips, such as using analogies and pre-planning your explanations, to improve your communication skills.
However, you might want some more in-depth training to boost your data skills. If that’s the case, Ironhack’s data analytics bootcamp is the right choice for you. This will help you improve all of your data skills, including data communication. So, what are you waiting for?
