The data-to-value process

How to turn data into business value with data visualization? We can position data visualization and storytelling in the overall data analytics process here. Okay, that links data to the actual value, and in this process, we can also observe three critical steps to driving value from analytics: data, then information turning into insight, leading to decision and actions, which then, ultimately, enhance value in terms of firm performance, for example. So, like a chain reaction, each step plays a role in driving towards value. Okay, and this is important: it starts with collecting raw data to serve as the foundation for gaining knowledge on a subject. Let’s look into this process quickly because this is not the main focus here of this module. But I want to show you where we could position data viz and storytelling in this process and where it becomes crucial potentially. 

Data as the foundation of the analytics process

So, the first step, of course, is the data, and I won’t go into detail here, but, of course, we have today multiple sources of data. Okay, in marketing, we often rely on surveys or focus groups, or interviews. We have a couple of those primary data collection methods. Today, in the era of big data, we often have data already available that we can extract or export elsewhere. Or there’s open data as well. Data sources today are big, small, and, in many cases, open as well. In terms of quality, there’s well your information must be potentially complete, accurate, up to date, for example. There are different questions about quality, and data should be reliable and valid from different perspectives. And it should be relevant. So you want to have data that somehow leads and then really kick-starts this chain reaction here. 

How to use data visualization to identify an insight

In the second step, we then link data to insights, where we turn data into information which then leads to insights. And there are a couple of approaches as well, of course. And we often refer to that in terms of data analytics and techniques in diagnostic analytics, descriptive analytics, predictive analytics, and prescriptive analytics. As a marketing analyst or data analyst, we can seize and use a couple of exciting methods to turn data ultimately – raw data – into an insight. Here’s already the first step for data visualization and storytelling. Data visualization and storytelling represent aspects of a more extensive analysis process that we go through to convert data into action and ultimately into value. So obviously, before you can communicate an insight, you must find one. Many of the data compositions we create help us or others analyze data and pinpoint meaningful insights. And this is actually where data visualization and storytelling already somehow interfere.

Data visualization for story framing

I label this step here: story framing. With story framing, you distill the vast amount of data to a more targeted set of key metrics and dimensions. We do so by limiting what data we focus on and choosing how we will visualize it. You frame the potential stories that can emerge from the data. Thus, story framing is primarily focused on providing helpful information to an audience that may or may not translate into meaningful findings. The audience could be you. The audience of the story of this first data visualization process can be yourself, okay, in the process because pictures will tell us much more than just the data and tables. Sometimes it’s helpful to see where is an insight, where something is actionable, in that something new, for example. We can use first, not polished ones, but use first data visualizations. Data viz for story framing is here one first aspect. 

What’s an insight?

Okay, I just mentioned already one key component that is: action-ability or actionability. What’s an insight? An insight has to contain new information: the information must be new, relevant, and non-trivial. Second, an insight must focus on understanding consumer behavior. We’re marketers. So, we’re in the context of somehow marketing in this setting. An insight has to quantify causality. An insight has to be about cause and effect because, as marketers, we need to lever to pull, right, something to do, to affect the change. Insight needs to measure how a change in one variable impacts a change in another variable. An insight has to provide a competitive advantage. An insight has to be a piece of intelligence that the firm’s competitors do not have. All intelligence is based on awareness of some information. An insight provides intelligence such that the firm is in a better competitive situation. Insights must generate financial implications: an insight should be measurable. First, whether a return on investment or contribution margin, or risk asset assessment, there should be some financial implications with any insight, right? Why the audience – why your audience – should care about the issue, right?

So, you think you got an insight. What’s next?

Suppose there is no measured increase in revenue or satisfaction or a measurable decrease in expenses, for example. In that case, we should question the validity of the analysis. Okay, it’s always the question: So what? What’s the effect? And, why should your target group care? Ultimately, insight is about actionability. All of the um above here drill down to one thing: actionability. Suppose an insight provides this actionability within the dimensions we see here on the slide. In that case, it provides marketers with what they need to make better decisions. If these decisions are based on data, then the chance of making the right decision – or an informed decision as well – increases. Then we move over to the step here between insights and action. The job of an analyst is to provide insights.

Data visualization and storytelling to drive actions and business value

The comments here attempt to put structure around the definition of an insight providing more than a mere observation in the hopes that executives will use the analytics output and make better decisions. There is a utilization gap that the CMO Survey. This utilization gap in analytics is still crucial within firms. And something that can overcome this gap is, again: data visualization. And this time, it’s data visualization for storytelling, okay, because here storytelling; if the objective, the goal, of a story, of telling stories based on data, is to instill action or push your audience to take to make decisions and take action. Okay, and then what is – and that’s the final step here – is: analytics and firm performance. And there is quite some research and some publications. In a study with several co-authors, we find that those big data-related capabilities, such as big data analytics, account for up to 13 of firm performance variance.

Conclusion

This brief post shows that we need to develop a systematic approach and follow an exact process to answer a crucial question: how to turn data into business value with data visualization? The Marketing Analytics Academy’s mission is to get you started on data analytics, its methods, and key concepts in various fields of marketing. The comprehensive course “Data Visualization: Storytelling With Data” provides a detailed overview to make the data-to-value process work for you. Start your data viz journey today and check out the online course!