PRESENTING DATA

Here are seven effective methods to open a speech or presentation:

1. Quote. Opening with a relevant quote can help set the tone for the rest of your speech. ...

2. “What If” Scenario. Immediately drawing your audience into your speech works wonders. ...

3. “Imagine” Scenario. ...

4. Question. ...

5. Silence. ...

6. Statistic. ...

7. Powerful Statement/Phrase.

Data has value through the stories that it tells. In order to communicate your findings impactfully, you need to:

• Ensure that your audience is able to trust you, understand you, and relate to your findings and insights.

• Establish the credibility of your findings.

• Present the data within a structured narrative.

• Support your communication with strong visualizations so that the message is clear and concise, and drives your audience to take action.

Data visualization is the discipline of communicating information through the use of visual elements such as graphs, charts, and maps. The goal of visualizing data is to make information easy to comprehend, interpret, and retain.

For data visualization to be of value, you need to:

• Think about the key takeaway for your audience.

• Anticipate their information needs and questions, and then plan the visualization that delivers your message clearly and impactfully.

There are several types of graphs and charts available for you to be able to plot any kind of data, such as bar charts, column charts, pie charts, and line charts.

You can also use data visualization to build dashboards. Dashboards organize and display reports and visualizations coming from multiple data sources into a single graphical interface. They are easy to comprehend and allow you to generate reports on the go.

When deciding which tools to use for data visualization, you need to consider the ease-of-use and purpose of the visualization. Some of the popularly used tools include Spreadsheets, Jupyter Notebook, Python libraries, R-Studio and R-Shiny, IBM Cognos Analytics, Tableau, and Power BI.

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The data analysis process begins with understanding the problem that needs to be solved and the desired outcome that needs to be achieved. And it ends with communicating the findings in ways that impact decision making. Data projects are the result of a collaborative effort spread across business functions involving people with multi-disciplinary skills, with the findings being incorporated into a larger business initiative.

The success of your communication depends on how well others can understand and trust your insights to take further action. So, as data analysts, you need to tell the story with your data by visualizing the insights clearly and creating a structured narrative explicitly targeted at your audience. Before you begin to create the communication, you need to reconnect with your audience. Begin by asking yourself these questions—Who is my audience? What is important to them? What will help them trust me? Your audience is mostly going to be a diverse group—in terms of the business functions they represent, whether they play an operational or strategic role in the organization, how impacted are they by the problem, and other such factors. Your presentation needs to be framed around the level of information your audience already has.

Based on your understanding of the audience, you will decide what, and how much, information is essential to enable a better understanding of your findings. It’s tempting to bring out all the data that you’ve been working with, but you have to consider what pieces are more important to your audience than others.

A presentation is not a data dump. Facts and figures alone do not influence decisions and move people to action. You have to tell a compelling story. Include only that information as is needed to address the business problem. Too much information will have your audience struggling to understand the point you’re making. Begin your presentation by demonstrating your understanding of the business problem to your audience.

It’s easy to fall back on the assumption that we all know what we’re here for, but reflecting your understanding of the problem that needs to be solved, and the outcome that needs to be achieved, is a great first step in winning their attention and starting with trust. Speaking in the language of the organization’s business domain is another important factor in building a connect between you and your audience. The next step in designing your communication is to structure and organize your presentation for maximum impact. Reference the data you have collected.

Remember that the data, the very basis of everything that you are communicating, is like a black box for the audience. If you’re unable to establish the credibility of your data, people don’t know that they can trust your findings. Share your data sources, hypotheses, and validations. Work towards establishing credibility of your findings along the way – don’t gloss over any key assumptions made during the analysis.

Organize information into logical categories based on the information you have—do you have both qualitative and quantitative information, for example? Be deliberate in taking a top-down or bottom-up approach in your narrative. Both can be effective—depends on your audience and use case. Be consistent in your approach. It’s important to determine what communication formats will be most useful to your audience. Do they need to take away an executive summary, a fact sheet, or a report? How is your audience going to use the information you have presented, that should determine the formats you choose. Insights must be explained in a way that inspires action. If your audience doesn’t grasp the significance of your insight or are unconvinced of its utility, the insight will not drive any value.

A thousand-word essay will not have the same impact as a visual in creating a clear mental image in the minds of your audience. A powerful visualization tells a story through the graphical depiction of facts and figures. Data visualizations—graphs, charts, diagrams—are a great way to bring data to life. Whether you’re showing a comparison, a relationship, distribution, or composition, you have tools that can help you show patterns and conclusions about hypotheses. Data has value through the stories that it tells.

Your audience must be able to trust you, understand you, and relate to your findings and insights. Establishing credibility of your findings, presenting the data within a narrative, and supporting it through visual impressions, you can help your audience drive valuable insights.

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Data visualization is the discipline of communicating information through the use of visual elements such as graphs, charts, and maps. Its goal is to make information easy to comprehend, interpret, and retain. Imagine having to look through thousands of rows of data to draw interpretations and compare that to a visual representation of that same data summarizing the findings. Using data visualization, you can provide a summary of the relationships, trends, and patterns hidden in the data, which, if not impossible, would be very hard to decipher from a data dump.

For data visualization to be of value, you have to choose the visualization that most effectively delivers your findings to your audience. And for that, you need to begin by asking yourself some questions. What is the relationship that I am trying to establish? Do I want to compare the relative proportion of the sub-parts of a whole, for example, the contribution of different product lines in the total revenue of the company? Do I want to compare multiple values, such as the number of products sold, and revenues generated over the last three years? Or, do I want to analyze a single value over time, which in this example could mean how the sale of one specific product has changed over the last three years. Do I need my audience to see the correlation between two variables? The correlation between weather conditions and bookings in a ski resort, for example.

Do I want to detect anomalies in data—for example, finding values in data that could potentially skew the findings? What is the question I’m trying to answer is not just an overarching question in the data visualization design and process—you need to be able to answer this question for your audience with every dataset and information that you visualize. You also need to consider whether the visualization needs to be static or interactive. An interactive visualization, for example, can allow you to change values and see the effects on a related variable in real-time. So, think about the key takeaway for your audience, anticipate their information needs and the questions they may have, and then plan the visualization that delivers your message clearly and impactfully. Let’s look at some basic examples of the types of graphs you can create for visualizing your data.

Bar Charts are great for comparing related data sets or parts of a whole. For example, in this bar chart, you can see the population numbers of 10 different countries and how they compare to one another. Column Charts compare values side-by-side. You can use them quite effectively to show change over time. For example, showing how page views and user sessions time on your website is changing on a month-to-month basis. Although alike, except for the orientation, bar charts and column charts cannot always be used interchangeably. For example, a column chart may be better suited for showing negative and positive values. Pie Charts show the breakdown of an entity into its sub-parts and the proportion of the sub-parts in relation to one another. Each portion of the pie represents a static value or category, and the sum of all categories is equal to hundred percent.

In this example, in a marketing campaign with four marketing channels—social sites, native advertising, paid influencers, and live events—you can see the total number of leads generated per channel. Line Charts display trends. They’re great for showing how a data value is changing in relation to a continuous variable. For example, how has the sale of your product, or multiple products, changed over time, where time is the continuous variable. Line charts can be used for understanding trends, patterns, and variations in data; also, for comparing different but related data sets with multiple series.

Data visualization can also be used to build dashboards. Dashboards organize and display reports and visualizations coming from multiple data sources into a single graphical interface. You can use dashboards to monitor daily progress or the overall health of a business function or even a specific process. Dashboards can present both operational and analytical data. For example, you could have a marketing dashboard using which you monitor your current marketing campaign for reach-outs, queries generated, and sales conversions, in real-time.

As part of the same dashboard, you could also be seeing how the conversion rate of this campaign compares to the conversion rate of some of the successfully run campaigns in the past.

Dashboards are a great tool to present a bird’s eye view of the complete picture while also allowing you to drill down into the next level of information for each parameter. Dashboards: are easy to comprehend by an average user make collaboration easy between teams; and allow you to generate reports on the go. Using dashboards, you can see the result of variations in data and metrics almost instantly—and this can help you evaluate a situation from multiple perspectives, on the go, without having to go back to the drawing board.

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