Data science and the outcomes it delivers can be complex and hard to explain. Presenting your approaches and findings to a non-technical audience, such as the marketing team or the C-Suite, is a crucial part of being a data scientist. You need to have the ability to interpret data, tell the stories contained therein, and in general communicate, write and present well. You may have to work hard to develop these skills – the same as you would with any technical skills.
It’s not enough just to have the technical know-how to analyse data, create predictive models, and so on – communication skills are equally important. You must be able to explain effectively how you came to a specific conclusion, and be able to rationally justify your approach. You need to be able to convince your audience that your results should be utilised – and you need to motivate how it can improve the business in a particular way. That’s a whole lot of communicating.
Here is a summary of the various types of communication skills that a data scientist should have a command over.
Presentation skills
Presentations may seem as old as the mountains, but they are not going away any time soon. As a data scientist you are going to have to at some time or another have to prepare and deliver a presentation.
There are different approaches and techniques to effectively handle different classes of presentations:
- One-on-one: This is often very intimate, where you sit down next to a single stakeholder in order to convey a very specific message very directly. Your engagement with the person is more important than the actual presentation’s appearance. Make sure you have a compelling story, justified by facts and figures, which this particular person directly relates to.
- Small intimate groups – the board: This has to be short, sharp and to the point, because the board often has a number of topics on the agenda. Make sure your facts are correct and that your numbers have been double-checked. Make sure you conclude by calling out the decision they need to make. If possible, it may be useful to lobby some of the board members beforehand – some decision-makers don’t like surprises.
- Small intimate groups – team meeting or a small group of prospects: This presentation is typically less formal and needs to be more collaborative and inclusive. Involve the key players in the discussion, and ask them leading questions to keep them involved. If possible, include the whole audience. Again, some up-front lobbying may pay off if there is a decision to be made.
- Classroom: When you start getting into the 20-40 attendee presentations, it becomes more complex to involve each and every attendee, hence your pitch needs to be relevant and exciting. This is typically to convey a specific message, so make sure the story frames the message appropriately – tell them what you’re going to tell them, tell them, and then summarise what you’ve just told them.
- Large audiences: This is typically at conferences, large seminars and other public events. In most cases, you will be doing some brand building in parallel to delivering the actual message you want to convey. Although a large part of the audience will see the slides better than they will be able to see you, remember you still need to be the focus. Dress appropriately. Use the 10-20-30 rule: 10 slides, 20pt font and 30 minutes. And remember in large halls people can’t read fine print and often can’t see the bottom 1/3 of the screen. Don’t ramble on and on – you can rather have people wanting more than what they are bored to pieces before you even got to the main point. Don’t try and squeeze in more than 3-5 key points.
There is a lot of psychology behind delivering a successful presentation, and you’ll notice I mentioned very little about the presentation “slides”. You, the person, must be the focus – you must convey the message – the “slides” are only a prop. And never, ever read verbatim off the slides or off a cheat sheet. Sure, slides and cheat sheets are useful to remind you of important points or to remember particular numbers, but never, ever read directly off them. Talk to the audience – engage with them.
Storytelling
Storytelling is equally as important as presentations when it comes to sharing data insights. A good presenter will in any case use storytelling techniques in his/her presentations. Especially with data science insights, which by themselves can be quite complex, you need a good storyline to make those outcomes more palatable for business users. I have discussed storytelling in a previous blog, so suffice to say, you need to:
- Frame the story: set the context for the audience to understand the story’s relevance.
- Convey the story: using interesting and exciting narrative to get the message across, and only using props where necessary to illustrate.
- Summarise: reiterate the highlights or the “moral of the story” at the end.
Data visualisation
Data visualisations are similar to the illustrations you find in a comic. They have to be used together with a good storyline. The “illustrations” are only used to visually convey the essence of the story. The story itself must still be told, be it interactively, during a presentation or through annotations.
There are many posts, books and websites devoted to the approaches you need to take to create useful and effective visualisations. For example, follow Stephen Few.
Business insight
Many articles on data science stress how important business insight and understanding are. In fact, most definitions of data science list business acumen as a crucial skill. I want to emphasise here that you must ensure that the business insight and understanding flows through very strongly when you present the results of your data science work, especially to business representatives.
For example, you need to:
- Convey the message in business terms.
- Highlight the business impact and opportunity.
- Correctly call out the right call to action.
At the end of the day, it’s those business representatives that have to understand what you are trying to say. They have to act on and possibly make far reaching decisions based on what you say. The least you can do is speak to them in an understandable language.
Writing / publishing skills
You may think in this modern era that the need for good business and scientific writing flew out the door the moment that little blue Twitter bird flew in through the window on your desktop… but that is not the case. Good writing skills are still required, for example to:
- Write a conventional report or white paper on your approach and findings.
- Frame a potential piece of work up in a proposal, or even as a more formal business case for the c-suite.
- Send formal emails, especially to the management levels. The shortcuts and slang used in social media has no place in the office, so keep your emails professional, well-worded, and properly punctuated to avoid being seen as a cowboy or a data geek.
- Develop or annotate presentations – sometimes you have to annotate a presentation before emailing it off to a senior stakeholder.
- Publish your content on a web page.
- Blog about your work in order to build up your own or the organisation’s profile. You never know, your next job may depend on it…
If your written communication skills are lacking, consider taking a refresher grammar course, doing a short course on business writing or asking a colleague to proofread your work.
Social media skills
Few people view social media interaction as a formal skill – except those operating in the digital marketing field – but you need to know how to communicate on semi-formal forums such as bulletin boards and LinkedIn, and you also need to know how to share ideas (without giving trade secrets away) on more social platforms like LinkedIn, and in the confined Twitter-sphere. These have all become widely used and accepted business communication channels. Don’t let your reputation drag you down because you didn’t know the appropriate way to communicate using these channels.
Listening
A huge part of communication isn’t about speaking, or persisting in getting your message across. If you want to deliver insights that the business can act on, you need to listen very carefully to what they have to say – what are their priorities, their challenges, their problems and their opportunities. Everything you do, deliver and communicate has to address these – so you need to pay careful attention to what they are first, and make sure you fully understand them and the impact they have on the business. Concentrate and make a genuine effort to receive what is being communicated.
Good listening means truly hearing and assimilating what the other person says, rather than thinking about what you will say next. Active listening is the art of repeating back what the other person said to make sure you have heard it correctly, and more importantly whether you understand it and its implications correctly. Active listening also involves asking questions to engage the other person and show that you’re truly interested in hearing that person’s thoughts on an issue. Take an active interest in what others have to say.
Stop and Think
This goes hand-in-hand with listening. You don’t always have to deliver an immediate response to verbal and written communications. An overly hasty response can cause a lot of problems, because the reality is – no matter what you do or say, you can’t take back. This is especially true if you’ve blundered out an immediate negative reaction. In most situations it’s quite acceptable to respond that you need a little extra time to think about the issue at hand and carefully plan and rethink your own response before giving it.
Soft issues
However, it’s not all about the specific communication skills. If you want to be a good and effective data scientist, you must be able to fit into the corporate culture as well. You need to know how to interact in particular business meetings, and in which manner to contribute ideas that can help the organisation move forward. You may even have to know how to play a bit or organisational politics as well – for example how to get support for an idea before you present it to management. Without effective communication skills, you may be a brilliant data scientist, but you could mistakenly be perceived as not being a team player, potentially damaging a very interesting and rewarding career.
There are number of “soft issues” related to communication too, especially in the workplace. Here are a few useful ones:
- Learn and remember peoples’ names; and spell them correctly.
- Address seniors politely.
- Don’t get involved in gossip.
- Dress appropriately, especially for presentations and meetings with seniors, clients and at public forums.
- Treat others with respect.
- Have good manners.
- Take the language of the audience in consideration.
- File your communication – received and sent – you never know when you need to refer to it again.
- Communicate openly and honestly.
- Watch the signals that your body language send out, and make eye contact.
- Be open-minded about other peoples’ commentary and interpretation of what you communicate, and learn from it.
- In fact, never stop learning….
Conclusion
As a data scientist, you’re going to need to collaborate and communicate with many different types of people with many different backgrounds. You have to be able to put yourself in their shoes and explain things in terms of what they’re interested in and what their background is. And remember, the average adult’s attention span is only 8 seconds…
If you can’t explain something clearly, it will appear that you do not understand it yourself. So, understanding and good communication go together. If you can spend some time and effort honing all these different types of communication skills, you will find that it is actually great when you succeed to explain something to someone in a clear way that makes perfect sense for them. It even makes it a lot better for your own understanding. A good quote I read said: “You’ve got to be able to talk to people who don’t have Ph.D.s about the thing that you have a Ph.D. in.”
Effective communication skills are important for everyone in an organization, from the CEO down to the interns. Effective communication is a self-marketing tool too. As a data scientist, you need good communication skills to round you off and make your work accessible to the rest or the organisation. Communicating well with those who work in other departments can help open up opportunities that will ensure your career longevity within the organisation. By focussing on the soft skills too, you can create a rapport with your colleagues and seniors that will be to your advantage in the future. In fact, you may even be liked and appreciated!
Reference
http://www.dataversity.net/how-data-scientists-can-improve-communications-skills/
