Data-Driven vs. Data-Informed: Choosing the Right Approach

As marketers, we all understand the incredible power of data. We have a wealth of data and most of us use some kind of data nearly every day in our jobs. Typically, we are leveraging that data to understand and analyze business, industry, customers, initiatives and more. We use data as information. We may consider ourselves to be data-driven, or data-informed, using this information to help guide decision-making, improve customer experience, enhance return on marketing investment or support other business goals. We use it to segment customers, to optimize campaigns, to build personalized experiences, to evaluate our efforts, to understand trends and to do so much more.

But data also can be used as inspiration, making us data-inspired. Data can spark great creative ideas, whether those ideas are needed for building a marketing strategy, pricing a product, developing the next marketing campaign or driving the next ad. Ideas are the fuel of marketing, needed for nearly every facet of what we as marketers do. Strong inspiration can set the good ideas apart from the great ideas. If harnessed correctly, data can be a powerful inspiration and driver for marketing greatness.

Let’s start with first distinguishing between the terminology of “data-driven,” vs. “data-informed” vs. “data-inspired.” These terms are similar, often used interchangeably, but if we make clear distinctions between the three, we can harness the power and effectiveness of each.

The most popular and widely-used of these phrases is “data-driven.” This overwhelming popularity is evidenced by Google Trends search data, showing searches for “data driven” owning considerably more search volume than “data-informed” and “data-inspired.” “Data-driven” tends to be used as an all-encompassing term to describe data being used to make or help make any decision. But, a data-driven approach in its most pure form is one where the data makes the decisions for you, truly and singularly driving the decision-making. There is a focus on statistical analysis and reliance on quantitative data. We use pure data-driven decision making when we execute an A/B test, personalization, programmatic buying, dynamic creative and the like. This approach only leverages data.

A data-informed approach is similar to a data-driven approach in that data is used and decisions are made, but there is a clear difference between the two. With a data-informed approach, one does not singularly rely on the data and execute exactly what the data says. Instead, there is an application and layer of interpretation before decision-making. Data informs us, but it is not the only driver. A data-informed approach takes the data into consideration, but a human being makes the decision. With this approach we can leverage both quantitative and qualitative data. We use data-informed decision making when we need to make a decision that requires taking other information into consideration. For example, an advertising director may need to decide on which creative execution to proceed with, but they cannot rely on the concept testing data alone. They will need to take competitive insights, budget and media plans into consideration as well. Data-informed decision-making is likely the decision-making that marketers use the majority of the time. We can use data-informed decision making to help with a variety of marketing decisions including creative campaigns, creative executions, media plans, product features, new products, pricing, budgeting and more.

A data-inspired approach is much different than a data-driven approach or a data-informed approach. Being data-inspired is not necessarily about decision making, it is about using data to serve as the catalyst for idea inspiration. A data-inspired approach brings together multiple types of information, similar to a data-informed approach, and any kind of data can be leveraged. However, with data-inspiration this information typically comes from a variety of sometimes disparate sources, and the information is used to make connections and ultimately tell a story that can be used to inspire ideas. Data is used as a spark and inspiration for the ideas but the story comes from a human. A data-inspired approach can be used in any situation where ideas are needed. It can be used for problem solving, during ideation, when working on creative concepts, while exploring product innovation, and more.

The key to data-inspiration is having many sources of data available to aid in making connections and finding the story. Exploration for one challenge may include reviewing transactional data, platform data, product reviews, social media commentary, customer data and Google Trends. The goal is to collect information from a multitude of sources to determine relationships, connections and shared themes that become the story to spark creative ideas.

Taking this approach may come naturally to some marketers, but others may need to be more deliberate. Data is not viewed as a traditional source of creative or idea inspiration, so we need to be prepared and intentional in our efforts and goals to do so. This means looking at data differently—not just as a source of information, but a source to tell a story. So, look beyond the typical uses of data. Look to data to answer a “why,” to expose truths, to find hidden gems and to help see and explore the bigger picture. Use it to help discover trends, to challenge beliefs and to examine those interesting outliers.

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Watch Dyani Marvel's presentation on data and creativity to get more insights and an inspirational case study.

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Taking a data-inspired approach comes with many benefits, including the capacity to account for the larger picture, culture shifts and other contexts that cannot be accounted for in more narrow uses of data. Data-inspiration can reveal new trends and connections, which can help lead to new and unique insights. It can encourage new ways of thinking about a business, product or a problem and can help lead to amazing and powerful ideas.

There are also limitations. Because data is not traditionally used as inspiration for creative ideas or ideation, this may require adjustment in many environments or simple resistance to change. In addition, it is not an exact science so inspiration may not be automatic and trying this approach may require experimentation and time to find a path to meaningful insights. But the effort in determining how data-inspiration can work is well worth the effort given the potential benefits it can bring. We have an incredible wealth of data at our disposal and an unlimited number of ideas waiting to be inspired by it.

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