How to communicate data insights

What are data insights?

Data can answer questions that intuition alone can’t, but unearthing crucial insights requires skill. Data analysts are trained in extracting clear, actionable information from a dataset.

Data insights inform decision-making of all kinds – from understanding buyer behaviour, managing assets and predicting movements in the market. Many, perhaps all, of the world’s top companies are case studies in the importance of acting on data insights.

But it’s not enough to simply generate data insights. The role of a data analyst is just as much about communicating findings to their team.

How do we communicate data?

Data communication is simply when a data professional shares their findings with their team. In the US, more than three-quarters of companies report making data-driven decisions. Sound decision-making hinges on the successful communication of data insights. A good insight communicated poorly becomes a poor insight, with the potential to mislead.

For that reason, data visualisations are important. Effective communication bridges the gap between data and the audience.

Visualisations are how data analysts turn a mass of numbers into something an audience can understand. They make it easy to spot and communicate patterns, trends and outliers in data.

Using patterns and indicators to simplyfy data

From childhood, we’ve all been subconsciously taught that certain things mean something. For example, green means good or go while red contains a warning. “More” can be expressed by “up” on a 2D plane. Because we read left to right in the West, we tend to picture time as passing left to right, too.

Data analysts use these deeply ingrained and widely shared patterns to make understanding data almost literally child’s play. If someone was presented with a chart where time ran right to left, they would immediately see it as a mistake, even if the graph was accurate. A stock price indicator that showed an up arrow, showing growth, but which was coloured red, would be confusing. In fact, that would be such a fundamental error that you might question the legitimacy of the provider.

Not all visualisations are so easy to grasp, though. Some require a bit of training. It might be hard to detect a trend in a scatter plot at first glance. Or understanding the difference between, say, a bar chart and a histogram might require training.

Avoid jargon and complex language

That’s why data analysts must also have strong verbal communication skills. It’s not enough to just put some graphs in front of someone and expect them to understand the point.

A good data communicator will have a good intuition about their audience’s level of data literacy and familiarity with the subject matter. When presenting to an individual, such as a C-suite member, a tailored communication approach is needed.

In nearly all cases, jargon is something to avoid. Jargon-free language improves communication because it lowers the barrier to understanding. Jargon can be exclusionary. It also inhibits storytelling, which is a critical part of communicating data insights.

Just like how salespeople use storytelling to make their pitches more persuasive, storytelling enhances data comprehension by contextualising data within a narrative.

A data analyst can spin up a story around how they developed a hypothesis, the various ways they tested it and how they reached a conclusion. Then their story can be illustrated with the help of impactful data visualisations.

The ability to tell the story around the data is valuable, regardless of an audience’s level of data understanding.

Technology and training

Nevertheless, data visualisations and storytelling are not a cure-all for the more difficult aspects of data communication. Certain types of data analysis are inherently complex and hard to communicate to a non-technical audience. It’s also not enough to identify and explain a problem. A solution is needed too.

An analyst might identify a problem with a user journey but might need a UX designer to suggest a solution to the problem. And skill is needed to make data visualisations legible. Quality screens and high-resolution images are needed, as well as clear captioning. Hard-to-read graphs and text make it likely an audience will mentally check out after a while.

However, in a data-literate organisation, the process of communicating insights through data is smoother. These problems will have been anticipated and mitigated.

Techniques for presenting your data analysis

Let’s dig deeper into some techniques and best practices you can apply now to data analysis presentations.

Storytelling

Storytelling allows you to weave data into a narrative that engages and informs. Narratives are easier to remember and connect with. Here are some pointers for creating an effective and persuasive narrative around data.

1. Start with a Clear Problem Statement or Question

Start your presentation with a problem statement, question or hypothesis. This prepares your audience to think about answers to the problem. It sets an expectation that will subsequently be met, providing a satisfying conclusion.

2. Use Data to Showcase the Journey

Take your audience on a journey through the data, highlighting key findings and insights along the way. Use counterexamples and lines of investigation that ended in a dead end. Doing so will head off questions that might be building in your audience and increase authority.

3. Conclude with a Call to Action or Recommendation

Wrap up your narrative with a clear call to action or recommendation. Here’s a pro tip popular with management consultants: give three recommendations – high-risk, medium-risk and low-risk. Put the best recommendation as the medium-risk option.

At Data Literacy Academy, “Storytelling with Data”, is a pillar module that all of our learners go through. Because even with the best tools and insights, we know from experience that nailing storytelling is still the hardest part for both business as well as data leaders.

Data Visualisation Best Practices

Now, let’s get into how you can maximise the impact of your data visualisations.

The cardinal rule for data visualisations is: the chart should fit the data. That means understanding your data and knowing the use cases for the common types of charts.

While there are dozens of types of data visualisation, there are a few core concepts to grasp.

• Bar charts (and their many variants) display different data points within the same category against the same measure.
• Line graphs show the change in a measure over time and allow for easy comparison with similar data.
• Scatter graphs plot data points defined by two metrics on axes and let you identify trends.
• Pie charts are well known but are frowned upon by many data professionals – there are usually better options. They are good for showing percentages of 2-3 categories.

Visualisations simplify complex data but only if the visuals are clear, concise and well-labelled with proper context and legends. Avoid misrepresenting the data by using consistent and sensical labels on the axes. We like this Polymer list of 10 bad data visualisations.

Colour and design are important to nail, too. Bear in mind the psychology of colour principles as well as your organisation’s own branding guidelines. Every colour should mean something.

Also remember to consider accessibility needs like colour blindness. There’s a reason why so many charts use orange and blue as primary colours.

Key steps to communicating data insights effectively

The effective communication of data insights hinges on four things:

1. Know your audience

It pays to tailor your language and level of detail to your audience’s level of understanding. For non-technical audiences, keep jargon to a minimum and stick to simpler visualisations. Emphasise the narrative aspect that we’ve talked about before. But for technical audiences, go ahead and use advanced data visualisations.

Consider the decision-maker’s position and what kind of information they will base a decision on. And if you know them well, or can ask someone that does, tailor your delivery to their preferred learning style – visual, listening or reading.

To present complex data, a few strategies exist. You could start with a simplified version of the data, before introducing the full data. Or, you can present the data into two different formats, the added context improving comprehension. But even with complex data, you should always start with the high-level overview before getting into the details.

2. Highlights key findings and actionable insights

In data visualisation, what you don’t show is as important as what you do. Restrict your charts to show only the data that makes your point. Clearly state your recommendations and how your data supports that recommendation. If possible, estimate the impact of your recommendation.

3. Use clear and concise language

When it comes to delivering key findings, verbal skills are paramount. Ultimately, your slick visualisation should serve what you say, so say your findings or insight boldly and clearly. Use active voice and illustrate your point with examples.

Jargon can be appropriate for certain audiences, but generally speaking, it’s a good idea to cut down the acronyms or technical terms.

Both of our modules “Interpreting and Communicating Data” and “Storytelling with Data”, are designed to improve verbal communication.

4. Ensure accuracy and credibility

Lastly, you want to project an air of credibility. Explain your methodology as clearly as you can. Don’t be afraid to point out areas of ambiguity or limitations in your data or method. Credibility builds trust in data-driven decisions by giving your audience confidence that you have explored all angles. It avoids suspicion that you’re trying to fit the data to your hypothesis.

Lastly, make sure to dot your i’s and cross your t’s. Be presentable in your appearance and posture. Eliminate all spelling and grammar errors in your presentation or other flaws that may exist. And re-check all your data points.

Benefits of effective data communication

The effective communication of data insights is vital to a data-driven business. Good communication means there is no risk of a wrong decision being made on misunderstood data. It also makes sure everyone is on the same page and working towards the same goals.

Good communication helps demonstrate the power of data and encourages other members of staff to buy-in to act in a data-literate manner. This fosters cross-team collaboration and drives better business outcomes.

Taking the next steps

In this blog, we’ve covered what data insights are and why they’re important. We’ve touched on how we use familiar concepts like colour and storytelling to communicate abstract data points. We’ve explored some actionable data visualisation and storytelling techniques. And lastly, we’ve explained how to maximise the impact of your data communication.

If you want to learn more about data communication and how to improve how your company communicates data, Data Literacy Academy can help. Our courses upskill enterprise teams on a wide range of data skills, including data visualisation tools like Tableau and Power BI.

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