There is no doubt about it: the way we do business, sell, promote, strategize and grow in today’s economy is data-driven. Every organization, from small start-ups to multinational corporations, is using the help of data scientists and experts to tap into the potential of information.

The importance became even more apparent with the emergence of big data.

Big data is a term coined by a computer scientist from the University of Pennsylvania in 1990. It denotes a set of data so huge that it cannot be processed by statistical software we knew thus far.

Now, any business that doesn’t employ big data analysts and experts will be a thousand steps behind those businesses that do. Without utilizing this vast pool of information, it’s practically impossible to succeed and develop in our quick-paced market.

But, what does this mean about the way we use data in day-to-day business? How do rapid developments in the field of data science deepen the gap between business and science?

All in all, there has been a huge rift in data analysis departments and other business sections of a company. Whereas non-experts previously had a general idea about what it all means and how data is handled, now it’s all mostly in the hands of scientists, and it’s absolutely not layman-friendly.

To bridge this gap, data scientists have to focus on nurturing a particular set of skills and assets to be able to portray their findings to their business colleagues. If a communication gap between the two exists, it can have a significant and noticeable effect on the business.

In this overview, we will present some of the best strategies that data scientists can use to present the scientific information they have in a business-friendly manner.

Using diagrams, charts, and visualizations

Visual representations of data have been around since business itself. You can have a throwback to TV shows from the 70s and see people at meetings pointing at graphs and diagrams. This is, by the way, the common representation of the data scientist.

As you very well know, there is much more that’s going on behind the scenes before those diagrams are actually produced and presented. The reason why this is the general association to data presentation is that it’s the most commonly used link between data scientists and business people.

Visuals have a miraculous capacity to present ultra-complex concepts and findings in a very simple manner. As a data scientist, you will have to learn to use these visuals for your colleagues at the same level you actually do data analysis.

Finding a common language

Like any other field, data science has its own jargon and terminology that’s easily understood by those who are proficient in the field.

What may be an everyday term in your work life can be a concept someone has heard of the first time in their life. This often happens when you’re a data scientist who’s presenting to sales managers or project managers.

Of course, this doesn’t mean that other businesspeople are uninformed or uneducated: they simply have specialized skills that don’t require them to have advanced knowledge of data. It’s the same the other way around: you probably have no idea about some terms the IT team uses on a daily basis.

The initial step in building a bridge between your work and business is to find a common language that everyone understands. Use words and phrases that accurately describe what you want to express, but that are completely understandable to any member of your team.

Each time you present a complex data concept to businesspeople, remember to shortly explain what it means. Of course, the problem is that these concepts are so common sense to you because of years of experience, so you might forget that not everyone understands what underliers are.

Simplify, simplify, simplify

The main culprit for the gap between data science and business is complexity. The fact remains that the data scientists manage is diverse and complicated, as well as the processes for analyzing it. But, that doesn’t mean that you have to present it that way to businesspeople.

It’s also not advisable to take an extreme route to oversimplification: you still want your insights to be valuable and relevant, but you have to find a way to make it simple enough so that anyone can understand it.

When you look at popular stats and numerical facts from scientific findings, you will find that they’re framed in a way such as “68% of people claim they prefer product X to product Y”. This type of conclusion is simple and clear. It doesn’t provide unnecessary information on how the information was obtained, what the process of surveying and analysis looked like and which surveys were rejected. If someone wishes to go into more detail, they can get a hold of the complete report.

The same goes for your data presentations. While they should be simplified, you should always a keep detailed background for everyone who wants to learn more.

Present only the core findings

When you perform thorough research and analysis, you will get tons of information and findings. Your job as a data scientist is not only to interpret them, but also to prioritize which ones will be the most important to your business counterparts.

This is a very important skill of extracting the most important data. Don’t be afraid that important information will be lost in the process – we are only talking about presenting to your business colleagues. You will still have your complete report with all the findings at your disposal.

But, when you present to someone who doesn’t have the skill of identifying core findings, you can confuse them and make your report much more complex than it has to be.

Let’s take the previous example: you’ve found out that 68% of people prefer X to Y. Now, in the sub-analyses of this survey, you’ve discovered a whole bunch of different stuff: gender preferences, age, locations, etc. According to your company’s objectives, you need to know which of these are actually the most important.

Present clearly and concisely: Information, causes, consequences

In order to make your data presentation easily digestible, it has to be structured in a logical and causal way. When you’re looking at data points related to events, show what caused them and what were the consequences of changes in, for example, customer behaviour.

A great way to visualize this is to compose infographics that will use icons and illustrations to show why, when and how something happened. This way, you can deepen your presentation by not only presenting your data facts, but also interpreting them.

You will also have to find the right balance between being too scarce with information and going too wide. To find out whether you’re going in the right direction, try a mock presentation for business with some of your colleagues outside the data department.

Develop presentation skills

You thought that, as a data scientist, you will be working only with data, numbers and figures? Well, think again. More often than not, you will actually be the one who will present the findings to management or other departments involved.

People usually assume that great communication and presentation skills are just something you’re born with, but that couldn’t be further away from the truth. Presentation skills can and should be honed, especially when you’re a data scientist who’s trying to get through to businesspeople.

Here are some of the main tips on how to improve your presentation skills for business purposes:

• Tackle Nervousness and Stage Fright – for most people, the problem with presenting has to do with stage fright. If you have this fear as well (which is very common and one of the most frequent human fears), you can take specific exercises to help you reduce your anxiety.
• Preparation is Key – this is also connected to the nervousness aspect: the more prepared you are, the more confident you will be, which also means that you will experience less anxiety and stage fright. Start preparing for your presentation as early as possible and aim for absolute preparedness for each of your presentations.
• Write it Down – many people find it helpful to write down what they will eventually present at a meeting or an event. This can range from a rough bullet point draft to word-by-word speeches, depending on how comfortable you are with improvisation.
• Use Presentation Tricks – if you look at keynote speakers, you will notice some common traits in their behaviour and attitude. The one thing they all have in common is that they ooze confidence. This is done through direct eye contact with the audience, a firm, confident stand and a moderately loud tone of voice.

Use technology for help

Luckily, there are many tools and apps that you can rely on to help you bridge the gap between science and technology in business. Here are some of the best ones:

• Venngage – this visualization service will help you create any type of diagram, chart, infographic or other types of visuals that you may need for your data report.
• Prezi – to make your presentations more dynamic, unique and engaging, you can use Prezi to take a breather from all those generic PowerPoint presentations
• Coursera – on Coursera, you can find many courses and classes on how to develop presentation skills, learn how to speak more clearly and concisely, present data to others and much more. Simply use the website’s search engine to find topics that you’re interested in.
• BIRT – a tool for creating data visualizations; supports web application integrations
• Google Charts – use the power of Google’s analytical and visualization tools and present your data in real-time by sharing it with your team members
• Visme Graph Maker – Visme is an easy-to-use online tool for creating graphs, charts, infographics, and a lot more. Visme allows you to create various forms of content as simple graphics but with a series of interactive features to bring static content to life such as the ability to animate any object and assign actions.  

Online apps and tools can also help you identify what your business partners might be interested in from a heap of data. You can also read blogs, forums and websites related to your industry to get an idea of what kind of metrics are the most important and what you should focus on when exchanging information with the business department.

Conclusion

In the ever-changing, quick-paced world of business, it’s important to find ways to bring it closer to science and technology. This combination can bring fruits in the form of better company culture, communication, sales and profits.

As a data scientist, it’s not enough to be an expert in your own field, but you also have to work on your soft skills to bring these ideas to your business colleagues.

Bio

Dorian Martin is a blogger and marketer at TrustMyPaper. He’s passionate about all things in the digital domain and excitedly follows all developments in tech. Next to creating content for writing platforms, Dorian also runs his own personal blog.