Lesson 10 - Presenting Findings to Stakeholders

Welcome back, and congratulations on making it to the final technical Lesson of the Module! This Module has been a doozy, but you’ve powered through it like a pro and learned a heck of a lot about data visualization along the way. 

Your final task focuses on an oral presentation of the data story you built in the previous Lesson. Some form of oral presentation is almost always necessary as part of a data project, and while public speaking takes a different skill set than the one you’ve been practicing, it’s also an excellent chance to be passionate about your topic! In this Lesson, you’ll learn some tips to improve your presentation skills, which will hopefully give you an edge when presenting the data you’ve worked hard on. You’ll start by looking at some presentation tips before exploring some best practices in designing and recording your presentation. Finally, you’ll walk through some potential next steps to follow after your presentation is done, for instance, how you might measure its impact and how you can get feedback on your work.

Let’s get started!

 

1. Oral Presentation Tips

As an analyst, you’ll inevitably have to conduct presentations of your findings. Without communication, how effective is your analysis? The purpose of a presentation is to communicate what you’ve learned to your stakeholders, allowing them to make informed decisions based on your results. While public speaking isn’t addressed nearly as much as written and scientific analysis, it’s just as important. A few simple tips will give you the skills you need to present in front of any audience.

 

1.1. Prepare

It’s important to know what you want to say before you start presenting to an audience. To that end, always create a script with structured notes on the key points you want to make in your presentation. When preparing this script, keep your audience in mind. What’s most important to your stakeholders? What will they be expecting to see in your presentation?

Figure 1. A good presentation starts with a good script!

One thing that will certainly be important to stakeholders is how your analysis addressed the objectives of the project. Refer back to any project documentation you created or referenced at the start of your project—for instance, your business requirements document—and ensure that every analysis you include in your presentation relates back to your motivations and objectives in some way, shape, or form (similar to what you did for your storyboard in the previous Lesson).

Keep in mind, however, that while a script can help guide you during your presentation, you should avoid reading it word for word. This can come across as mechanical, unengaging, and—let’s be honest—boring. Don’t put your stakeholders to sleep!

For your Cliqz project, you’ll be able to use your data story from the previous Lesson as the foundation for your script. You’ll explore how to prepare an effective script in the next section.

 

1.2. Practice

Like many things in life, the best way to get better at public speaking is to practice! The more you practice before your presentation, the more at ease you’ll feel during the presentation. Try reading your script out loud. This mere act will help you locate sections where your narrative doesn’t flow as well or that you have difficulty putting into words. Read your script aloud, record yourself giving your presentation, and listen for places where you stumble or lose your place.

Not only will your narrative come across as more polished and authoritative, you’ll also gain confidence. After all, some of the nervousness you might feel likely comes from a fear that you’ll forget what to say. Just like you develop muscle memory when learning how to ride a bike or play a sport, you develop verbal memory by way of repetition. Practice, practice, and practice some more. You control your presentation—not the other way around!

 

1.3. Pacing

Many people speak faster when they’re nervous. Presentations, however, should be presented at—or slower than—your normal speaking rate, meaning that you’ll need to make a conscious effort to speak slowly and calmly. One thing you can do is create landmarks throughout your presentation. These can help you assess your speed. Time yourself speaking slowly and calmly and keep track of how long it takes you to reach key points in your presentation. For instance, if you finish explaining the third chart before the first minute is even up, you know you need to slow down. Conversely, if you haven’t reached the statistical analysis section by the fifth minute, you need to skip some material to keep the presentation on track.

Figure 2. The proper pacing will help a presentation feel more authoritative—and less manic!

Pacing can also be a useful tool for emphasis. You can pause to emphasize key points or collect your thoughts. This also allows your audience more time to absorb the information. Practice with a stopwatch, keeping in mind any specific time constraints you need to work within.

 

1.4. Body Language

You’ve probably seen tabloids with headlines dissecting the strength of a famous couple’s relationship by looking at their body language, ascribing meaning to the smallest gestures or angles of the body. While some of this starts to feel a bit ridiculous, there is some truth to body language. For instance, you can often determine whether a person feels confident or nervous based solely on the way they hold themselves. This can be a big factor in the way your presentation comes across, too. Do you look confident in your data and results? Or will your body language make stakeholders question your authority?

Figure 3. What does your body language say about you?

Always stand in a natural position, facing your audience. Try not to look too relaxed—your feet should be apart and knees slightly bent, but you shouldn’t be slouching or leaning against the wall. Also, smile. Research shows that the act of smiling can actually improve your mood. Plus, it’s contagious. You can improve your mood, as well as that of your audience, with this simple gesture.

Second, breathe. Meditation and yoga have gained popularity in part because slow, deep breathing can help calm the mind and relieve stress. While you might not participate in either of these activities, you can still take advantage of the calming effect of deep breathing. Simply taking a moment to focus on your breath for a minute before you start to speak can provide a calming distraction to your nerves.

Once in front of your audience, take a deep breath, make eye contact, and say hello. Greet everyone and introduce yourself. Even if the audience is familiar coworkers, a greeting and introduction can add a bit of formality, conveying that you’re about to begin.

For some interesting insights on non-verbal communication, check out this video of a former FBI agent explaining how to read body language.

 

1.5. Passion

In a similar vein to smiling, passion can improve your presentation and positively affect your audience. Think about it: have you ever listened to someone talk about something they’re passionate about? For instance, when your colleague Oliver talks about his weekend bird-watching endeavors and you find yourself getting excited about the Eurasian sparrowhawk he saw? Despite your own lack of interest in birds, Oliver’s enthusiasm about the subject makes you excited as well.

Figure 4. You’ll never run “fowl” so long as you’ve got passion!

People can hear passion. If you weren’t passionate at the beginning of your project, you probably were by the end. After all, you’ve spent a great deal of time living with and exploring your data! Let that passion come through in your storytelling. Your storyboard may exist to display the information, but you’re the master storyteller.

Advocating for Data
While companies are pretty good at gathering and analyzing data, many still struggle when it comes to generating insights from that data and getting them to the right people. This leads to missed opportunities for innovation.

According to survey data, while 65 percent of respondents say their organizations are effective at capturing data, only 46 percent say they’re effective at disseminating information and insights.

Ensuring that insights flow to the front lines is more about having a data-aligned culture than overcoming any sort of technological barrier—it requires an organizational mindset that can nurture data’s metamorphosis from insight to value.

 

2. Designing for Your Audience

You spent a great deal of time at the beginning of your project defining its project stakeholders and, subsequently, its audience. You can also make your presentation more effective by incorporating a few considerations about your audience into your script.

Start by considering how to communicate with your stakeholders. These are people who won’t have the same skillsets as you—nor the same vocabulary—and weren’t part of the analytical process. Your presentation should be designed with the right level of technical detail for your audience. Additionally, you should ensure you’re keeping everything as relevant as possible to your stakeholders: there’s no reason to waste their time if the information you’re presenting doesn’t affect them. Remember to explain concepts you’ve come up with while working in the data if they’re not common knowledge.

 

2.1. Technical Detail

You learned in previous Lessons that you may end up presenting your project and research to a variety of different people. In the school district dropout example, results were to be presented to teachers and parents. In your student project, stakeholders include medical agency frontline staff such as nurses, hospitals and clinics, patients, and staffing agency administrators. On another project, you might also be required to present to executives—or even other analysts and technical staff such as data scientists and engineers.

Figure 5. Who you’re presenting to will influence how you do your presenting.

This wide variety of possible audience members equates to varying levels of data literacy. For this reason, it can be helpful to place your audience into three rough tiers of data and statistical comfort:

• At the highest tier is an audience of analysts. This can also include non-analysts with similarly technical jobs, such as data scientists, data engineers, and software programmers. When presenting to this type of audience, you don’t need to go into as much detail regarding the methods you used nor the technical jargon involved. You can talk about averages, standard deviations, and correlations and trust that your audience can follow. For instance, you can say that “at an alpha of 0.05, you found no significant difference in average rental assistance between students that drop out and those that graduate.”
• At the middle tier is an audience of people familiar with spreadsheets and charts and how to read them but who don’t know how to make these charts themselves. In this tier, you should include more-detailed explanations of your methods. For instance, rather than simply reporting that there’s a weak correlation between family size and rental assistance, you’d first discuss what it is that a correlation examines and why it makes sense in this instance. You might reword the hypothesis test results in the above example to something like “with 95% confidence, the students dropping out and those graduating had similar rental assistance levels.”
• At the lowest tier is an audience of people who don’t typically work with numbers, spreadsheets, or equations. You should approach presentations to this audience from more of a teaching perspective. In order to reference a standard deviation, you need to first explain what a standard deviation is. You might reword the hypothesis test results to something like “both groups of students received similar amounts of rental assistance.”

 

Then, there are the mixed audiences. In these instances, it’s best to err on the side of caution. Keep the language you use accessible by avoiding technical jargon and save any elaboration on your statistical methods for those who want to follow up later. You can include more-detailed resources in an appendix—or follow-up email—for those interested in the technical details.

 

2.2. Goals

Another way to tailor your presentation to your audience is by keeping it relevant to their goals. Is your audience a group of analysts interested in learning about the methods you used? If so, feel free to go into as much detail as possible when it comes to your analytical methods and processes. Or, is your audience made up of frontline staff members interested in knowing how your results will change their work? These stakeholders care more about the recommendations than the methods. And what about managers interested in how your results relate to company processes and systems? They’ll likely be most interested in your conclusions and next steps.

You should already have a rough idea of your stakeholders’ goals from your business requirements document and project plan, as well as from any communications you’ve already had with them throughout the project. Keep these in mind as you determine the structure of your presentation. After all, you don’t want to spend the majority of your presentation talking about things your audience doesn’t care about!

 

3. Video Recording

Figure 6. The tips in this section can help whether you’re recording your presentation or giving it live over a video call.

For the Exercise for this Lesson, you’ll be recording a video of your presentation. While this isn’t the norm (you’ll usually either be giving your presentation in person or via video call), it’s the best solution for this task as it will allow your mentor to give you feedback. Below, we’ve included a few tips specific to recording a video of your presentation:

• Use a script. Because you’re not speaking in front of a live audience, you can use your script to ensure you stay on track. Do be careful, though, that you avoid reading it word for word. Instead, have your script available in note format to prompt your key points.
• Record in a quiet, well-lit area. Try recording in a small, padded room in order to reduce echo. If you’re actually on camera (and not just using screen grabs), ensure the room is well-lit and that you aren’t sitting in front of bright windows.

Here are some basic video lighting tips you can follow to make sure your video is well lit.

• Create your video. You can choose to record your voice using tools such as ScreenPal or Loom (also a Chrome plug-in). Optionally, you can create a video with you in it. If you’re using a Mac, you can use QuickTime or download iMovie for free. If you’re on Windows, check out Windows Movie Maker.
• Export and upload. Export your video as an MP4 file, then upload it to a web-based video service like YouTube or Vimeo. You can then embed the video into your website or add it to LinkedIn.

 

4. Data Limitations

It is important to identify the limitations of your analysis. Most commonly, these limitations are due to your data sources—perhaps the data doesn’t tell the whole story, the data itself is biased, or there’s certain data you don’t have access to because of data laws and privacy concerns.

Your stakeholders need to know these data limitations because they may have impacted your results. For this reason, be sure to acknowledge these limitations when presenting your work to stakeholders. Treat them like footnotes. In a written report, they might actually be footnotes. In a visual presentation, you can note them verbally or in an appendix.

 

Stepping Back & Reflecting on Your Data

In this write-up I'm going to talk about the importance of stepping back in your analysis and reflecting on insights. It's easy to get a little lost in your data, especially if you are a student not used to dealing with large amounts of it. One of the traps that analysts fall in is having a tunnel vision when they go deep into the data. It is important to routinely take a step back and see if the analysis seems to make sense with respect to business objectives. 

Let me tell you a personal story. In my previous company, when we were a very early stage startup, we wanted to have more and more users. Hence we defined metrics and they were all with respect to number of installs. Dashboards were created and we invested a lot of money to get more users.

We would look at the numbers daily and be happy that they are rising. However, six months down the road, we realized that something was not right. The number of users were increasing, but there was no increase in revenue. We took a step back and noticed that although the numbers were rising because of our strategy to acquire customers, it was not very helpful because the customers either uninstalled or never used our product. 

Thus, from a data team's point of view, everything was fabulous, but for the real business, it was a wasted effort. For you, the data analysis student, this means take a step back on the analysis that you are doing and re-evaluate if your analysis still connects with the original hypothesis. Don't forget to do this regularly. And especially before submitting your report or presentation to the stakeholder. Doing this will mean you will avoid getting off track and will be able to keep your analysis in line with the business objective

I hope this helps in getting an overview to why it's important to step back in your analysis and reflect on insights, time to time. 

 

– A piece by Dr. Humera – Founder Lumen

 

5. Adhering to Privacy Laws

Before sharing the results of your analysis with a wider audience, you should always assess the privacy and ethical considerations of each data source. As an analyst, you’ll often find yourself working with sensitive information. In these cases, there may be privacy laws that limit what you’re able to share with stakeholders. Many such laws exist, so it’s important that you do your research before presenting the results of your analysis to a wider audience.

 

6. Next Steps

Now that you’ve completed your project and presented your results, what comes next? While the answer to this will depend on a number of different factors unique to your project, one next step common to many projects involves monitoring. Your project focused on a specific set of data. This data will likely continue changing over time. Your organization may want to monitor certain metrics to ensure they don’t fall below certain thresholds. Or, perhaps they’ll want to measure the impact of a change. In your project, for example, how will you know whether your recommended staffing levels work as predicted?

 

6.1. Ongoing Monitoring

Some projects, by nature, necessitate some sort of ongoing monitoring. This may be part of the project requirements, or it may be a recommendation you’ve come up with after analyzing the data. 

A common way to monitor impact or effectiveness is to relate a project or analysis to an organization’s or project’s metrics. Metrics are, in essence, anything you can count. They’re quantitative data used by organizations to track and monitor performance, and they help track the status or success of a process. Is graduation rate an important metric that the school district superintendent should monitor regularly?

Common metrics used in organizations are key performance indicators. Key performance indicators (KPIs) are a subcategory of metrics that have been singled out as the most important metrics for that organization. They track the progress towards a business objective or goal. KPIs are often formulated over a set time period; for instance, hitting a sales goal within the next six months. The objectives are informed by historical performance or industry standards.

KPI Example: Traffic Authority
The local traffic authority is concerned with highway safety. The head of the agency has a goal to decrease highway accidents by 10 percent in the next 12 months. She knows the historical accident count, and her key performance indicator tracks the count of highway accidents for the subsequent 12 months.

Figure 7. This car driving between the lanes is a highway accident waiting to happen!

The KPI provides a number the agency head can easily monitor. She can see how the numbers look month by month, then use those numbers to estimate if she thinks she can meet the 10 percent reduction goal. If it looks like she won’t reach her goal, she can look at other metrics that contribute to the KPI in order to find processes to improve.

Average driving speeds, for instance, is one metric her agency tracks. They show that highways with higher speed limits tend to have more crashes. Additionally, counts of on-duty highway patrol cars show that days with more patrolling officers report fewer accidents. She could use either of these supporting metrics to change a process and keep her KPI on track—for instance, by lowering the speed limit on some sections of highway or allocating more money for highway patrol officers.

 

From an analytics perspective, many of the data variables you profiled and categorized are, indeed, metrics. In fact, you probably studied these metrics quite thoroughly throughout your work, and you know which ones are useful to the business questions and aren’t missing much data. Think about your project. Which variables did you use multiple times? Which ones changed over time or showed some sort of trend (i.e., spatial)? These are metrics you might recommend that your organization continue to monitor.

Not all of your analyses will relate to business KPIs, especially as they tend to be more executive level. However, if they do relate, it’s important to bring awareness to your work in relation to that KPI. Ensure people who regularly see that measure know about your work.

 

7. The Role of Critique

One necessary aspect of data analysis, visualization, and storytelling is critique. You learned about some basic data visualization guidelines at the beginning of this Module—for instance, how to think about color, how to use size to convey meaning, and when to use line charts over bar charts. However, some design components were more like guidelines rather than absolute rules. Two analysts following the same guidelines aren’t likely to create the exact same end product. This is because design always involves some degree of subjectivity. In a similar fashion, two analysts creating a data story about the same project will likely create slightly different versions. This is normal and to be expected. It does, however, present an avenue for critique.

Figure 8. Some data stories will inevitably be more awesome than others.

Many people find it uncomfortable to receive criticism of their work. Done well, however, critique is an opportunity to learn and improve. You’ve been evaluating and updating your own visualizations using your design style guide throughout this Module. In the workplace, don’t be afraid to ask your colleagues or mentor for feedback on your visualizations and storytelling narrative. While there will always be some degree of subjectivity, and you may not agree with every piece of feedback, you’ll often receive valuable input for making your data story or presentation more effective. Just like your checklists help ensure all your axes have labels and your colors make sense, another person’s perspective can help ensure your story communicates what you’re trying to communicate. Proactively seeking out critique before creating the final product allows you to receive and respond to this input in a low-stakes setting.

In the Exercise for this Lesson, your mentor will be reviewing the data story you’ve built for your student project, as well as how you’ve presented it. Hopefully you’ve met with them before now, as your mentor is an excellent resource for bouncing off ideas and getting examples of good presentations in real-life analysis.