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Category: Data Storytelling

A Simple Strategy For Asking Your Data The Right Questions

August 27, 2020

As companies collect more and more data, the ultimate goal is to translate this data into insights that can help them optimize business performance. For example, they may wonder which of their customer segments is most profitable? How they can reduce their customer acquisition costs? Which features of their flagship product are underutilized or need to be enhanced? Which of their best employees are potential flight risks? In most cases, answering one of these questions will lead to more questions.

When the questions you can ask the data are unlimited but the time and resources to find the answers aren’t, you need a strategy for how you query the data for meaningful insights. We often like to say, “There’s no such thing as a stupid question.” However, when it comes to analytics, some questions can be poorly phrased, ill-conceived or misguided. They can lead to costly, time-consuming expeditions into the data that don’t yield any actionable insights.

When the questions you can ask the data are unlimited but the time and resources to find the answers aren’t, you need a strategy for how you query the data for meaningful insights.

The French philosopher Voltaire said, “Judge a man by his questions rather than his answers.” This same standard can be applied to organizations. Companies that struggle to get meaningful insights from their data are often not asking the right questions. The better the quality of your questions, the more valuable your insights will be. Preparation is a key success factor to querying data effectively and discovering meaningful insights.

Why You Need a Plan, Not Just Curiosity

While a stirring question can launch you on a valiant quest to find answers in the data, it’s not enough. As we discover in the Greek legend of Theseus and the Minotaur, you need a plan if you’re going to come out of the data labyrinth alive. If you’re not as familiar with this ancient tale, Theseus is a young Greek prince who volunteered to be one of Athens’s sacrificial tributes for King Minos’s half-bull, half-man monster. Theseus’s goal was to slay the Minotaur in its labyrinth so Athens would no longer need to send young tributes to their deaths. While Theseus had the brawn and bravado, brains were another matter. Beyond killing the Minotaur, he didn’t put a lot of thought into the details of his quest.

…you can easily get lost in the analysis process and fail to return with valuable insights.

Fortunately, King Minos’s daughter, Ariadne, fell in love with the Greek hero, and she wanted Theseus to survive his mission. She knew he would get lost in the Minotaur’s maze, and he would need a weapon to defeat the beast. Ariadne secretly armed Theseus with a sword and gave him a ball of thread to find his way out of the labyrinth. While Theseus had a noble purpose (kill the Minotaur), he didn’t have a sound plan (survive the labyrinth). Similarly, if you enter the data maze without adequate preparation, you can easily get lost in the analysis process and fail to return with valuable insights.

A Framework for Finding Meaningful Insights

After more than 15 years working with various companies as an analytics consultant, I’ve developed a simple formula that can help point any analysis, dashboard, or other data-related project in the right direction. In addition, this formula isn’t just for analytics practitioners as it can be used by business professionals to help formulate the right questions. The framework is based on the underlying premise that the questions we need to answer are audience driven. For example, what the finance team cares about will be different than the interests or needs of the marketing or human resources teams. In order to generate impactful insights, you need to either understand the audience’s needs (analytics/BI teams) or clarify your needs if you’re the audience (leaders, business teams).

In my new book, Effective Data Storytelling, I introduced the 4D Audience Framework, which focuses on four interconnected dimensions (4D)—problem, outcome, actions and measures. These four dimensions can help you keep your bearings in the data (ball of thread) and give your questions a sharper focus (sword). The framework emphasizes the importance of gaining context and clarity from the audience on their key challenges, activities and goals. Each of the following dimensions contributes to enriching your analysis perspective and helping you to ask the right questions of the data:

image representing 4d audience framework

  1. Problem: A key challenge or issue your audience wants to solve. They may want to make an aspect of the business more efficient or effective than it currently is. For example, a problem may be that your marketing team is struggling to generate an adequate number of new business leads. The better you understand the problem and its consequences, the better prepared you are to find potential causes and solutions. When the problem is clear, you’re less likely to meander aimlessly through the data.
  2. Outcome: A strategic goal or desired end result your audience wants to achieve. If the problem represents the current state, the outcome represents the future or desired state. When the desired outcome is explicitly stated (a specific target), you know how much of a gap there is and what needs to be accomplished. For example, your marketing team may have established a goal of increasing the number of leads by 60% in the next quarter. If an outcome hasn’t been established by your audience, you may need to set a reasonable one on their behalf.
  3. Actions: The key activities and strategic initiatives your audience has implemented (or will be) to fix a problem or achieve an outcome. They represent investments of money, time and resources that will be relevant and top-of-mind for your audience. For example, your marketing team may be focused on expanding its virtual marketing events or enhancing its digital marketing efforts to drive more leads. Any insights you uncover on these activities or initiatives will be of strong interest to your audience.
  4. Measures: The key metrics and other data used to highlight the problem, monitor the performance of the initiatives and define the achievement of the desired outcome. Not all of the data will be relevant or useful to answering key questions. Understanding which metrics and dimensions matter as well as how to interpret what they mean will be essential to making sense of the numbers. For example, if you know the marketing team is looking at total inquiries, total qualified leads and cost per lead, you’ll want to keep your analysis centered on these key metrics so you don’t go too far astray from what your audience is focused on.

GPS Analogy and the 4D Framework

gps analogy for 4d framework

To illustrate how these different dimensions come together and can help you maneuver strategically through the data, I’d like to use a GPS analogy. You start by understanding the audience’s starting point (their problem or current state), and then you seek to learn what their intended destination is (their desired outcome or future state). You examine their route and mode of transportation (actions or activities), and then evaluate the progress to their goal (measures or key metrics). This simple formula ensures you don’t get lost in the data labyrinth and positions you to ask the right questions of the data.

In their ebook, Data-Driven: Creating a Data Culture, data scientists Hilary Mason and DJ Patil state, “Asking the right questions involves domain knowledge and expertise, coupled with a keen ability to see the problem, see the available data, and match up the two. It also requires empathy.” If you take a holistic approach with the audience-centered framework I’ve introduced, you’ll have both the empathy and contextual information to uncover valuable insights from your data. As inventor Charles Kettering (or philosopher John Dewey) said, “A problem well defined is a problem half-solved.” If you want to ask your data the right questions, you must first seek to understand the four audience-based dimensions I’ve mentioned. Only then will you be prepared to head into the data maze and uncover meaningful insights in a more efficient and productive manner. Good luck!

This article was originally published on Forbes.com.

Brent Dykes
About the Author

Brent Dykes is the Senior Director of Insights and Data Storytelling at Blast Analytics. He is also the author of Effective Data Storytelling: How to Drive Change with Data, Narrative, and Visuals. Brent has more than 15 years of enterprise analytics experience at Omniture, Adobe, and Domo. His passion for data strategy and data storytelling comes from consulting with many industry leaders including Nike, Microsoft, Sony, and Comcast. He is a regular Forbes contributor and has written more than 35 articles on different data-related topics. In 2016, Brent received the Most Influential Industry Contributor Award from the Digital Analytics Association (DAA). He is a popular speaker at conferences such as Strata, Web Summit, Shop.org, Adtech, Pubcon, RISE, Crunch, and Adobe Summit. Brent holds an MBA from Brigham Young University and a BBA in marketing from Simon Fraser University.

Connect with Brent on LinkedIn. Brent Dykes has written on the Web Analytics Blog.

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