Data literacy requires collaboration between marketers and analytics experts in a process that yields understandable, repeatable, and usable information sets.

As Socrates once said, “the only true wisdom is knowing you know nothing.” In many ways, the concept of data literacy reflects this premise. The strongest data analytics teams start each project with an understanding that there are millions of potential variables, unknown to them, that may impact the consumer data they are about to receive.

But from a client’s perspective, data experts are expected to answer questions, not ask new ones. That’s why basic data literacy — or the ability to understand, use, and communicate data effectively — is essential not only for professional data experts, but for everyone who works alongside them. When members of every business division are able to engage with and understand the nuances of data analytics, the experts have the necessary flexibility to draw comprehensive, meaningful insights — both in the short term and over time.

Asking Critical Questions

At its core, data-driven marketing is a process whereby certain data sets, by virtue of critical review and analysis, are deemed significant enough to motivate investment in certain campaigns or actions. Some data sets will inevitably be discarded during this process. The only way to decide what is important and what is not is to ask critical questions.

For example, marketers will ask about things like sample size, date ranges, consumer populations, and factors that may have influenced consumer motivations in the past. In data analytics, however, those tend to be the easy questions. Where things become more difficult is in understanding the unknowns that are unique to each particular client or customer.

Take, for example, the marketing of healthcare services. Marketing initiatives may be heavily impacted by things like legislation (think HIPAA), patient populations (e.g., those seeking general medical care vs. highly specialized services) or regulatory disclosure requirements (we’ve all seen advertisements for medications that include paragraphs of fine print). These are all factors that: (a) can impact data; and (b) may be unknown to analytics teams at the outset of an engagement.

Collaborating on the Unknowns

Given the highly specific nature of consumer data sets, it’s important that marketers and analysts remain in constant communication about potential influencing variables. Once the client or core business team understands how data is read and interpreted, they can assist their data teams in setting the right course.

Again using the healthcare field as an example, suppose a marketing analytics team noticed a heavy spike in sales of a particular brand starting in March and continuing through June of that same year. Without client collaboration, that team might set off to analyze a plethora of data points that might account for the sales increase, in hopes of replicating those sales in other months.

The client, however, could immediately look at that same sales spike and write it off as insignificant. For example, a major competitor may have had a supply/manufacturing disruption that drove a major increase in utilization of the brand in question for a short period of time. By sharing contextual information, business knowledge, and pursuing a collaborative interpretation, both parties save time and resources that might otherwise be spent chasing rabbits down the wrong data holes.

Analytics Experts Must Lead the Way

It’s up to the analytics team to lead the way in this collaborative effort. Most clients will not come to an engagement with a high degree of data literacy — that is, after all, why they’ve chosen to work with data experts.

For example, marketers may not fully realize the significance of seemingly imperceptible actions — like small tweaks to a spreadsheet column that have a huge impact on big picture data interpretation. Likewise, they won’t necessarily know to tell their data partners about critical information like supply disruptions, regulations, off-label uses for drugs, or other variables unless prompted by the analytics team to help account for patterns of interest.

By working together, analytics experts and their clients can help each other bring the highest level of data literacy to each individual engagement. Once each party understands what they do not know, collaboration will become more efficient and marketing initiatives will be likely to yield more meaningful insights.

Written by Andrew Ghosh

I lead the Data Engineering practice at Saatchi & Saatchi Wellness. Since September 2013, I’ve has worked to deliver analytical insights to clients, with a focus on efficiency and repeatability. My prior expertise in software development, client services, database architecture, and predictive analytics has enabled me to provide my clients with a unique blend of data-focused rigor and strategic insight. My experience includes work in digital, personal, and non-personal promotion channels. I graduated from Bucknell University with a degree in Neuroscience.

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