How Can Companies Boost Organizational Data Literacy? Demystify the Data by Democratizing the Data.

Companies must work to demystify analytics in order to boost data literacy as more employees start to engage with data on a regular basis.

In the span of just two decades, data has become a pivotal part of nearly every aspect of organizational decision-making. From sales leaders to HR professionals to, perhaps most directly, marketers, professionals occupying a wide variety of roles and industries can benefit from integrating data-driven insights into their work.

As such, it’s incumbent upon companies to find ways to help each and every one of their employees become comfortable handling — and, better yet, experimenting with — the data that pertains to their day-to-day operations.

Achieving this is less about teaching salespeople and marketers to use Tableau than facilitating their use of data in a way that allows them to level-up their strategic decision-making — on both an individual and organization-wide scale. This, ultimately, forms the foundation of a “data literate” organization, one capable of leveraging data as a meaningful differentiator in the increasingly crowded, increasingly noisy corporate world.

Jumping in the Deep End

Especially for larger, more siloed organizations, fostering data literacy begins with making a concerted effort to democratize data. Generally speaking, much of the struggle stems not from innumerate employees’ misinterpretations of data, but from the organizational mythology that data is for data experts’ eyes only.

Indeed, general reluctance to engage with data is often a defense mechanism with respect to the fear of the unknown more than some deep-seated disinterest in the upside of analytics. Realistically, data analytics is still something of an emerging practice, and a surprising number of professionals still harbor the misguided belief that analytics is — and should be — the exclusive purview of quants and data scientists.

The easiest way to disabuse everyday employees of this belief is to put data directly into their hands. Once people have access to data (and intuitive tools with which to decipher and manipulate it), the mystery fades away, and data becomes just another mechanism with which to produce better results. Granted, it may take some time for these results to manifest, but there is absolutely no harm — and in fact, a great deal of benefit — in enabling employees to develop data literacy through a process of (strategically directed) trial-and-error.

In my experience, it usually makes the most sense to center this process around the development of key business questions (KBQs). While analytics experts are the only employees with the requisite expertise to lead the KBQ creation process, organizations should actively encourage as many of their employees as possible — data literate or not — to contribute to the process in some way, shape, or form.

For instance, once an organization has clearly defined the purpose of a new project, it should funnel its data into the hands of analysts and non-analysts alike, empowering everyone to experiment with it in accordance with their unique competencies. In addition to ensuring that stakeholders across the organization share a mutual understanding of both the facts themselves and their import, this open, collaborative experimentation authorizes employees to pose new, outside-the-box KBQs. Sometimes these questions will end up being analytically untenable — that is, mere curiosities or pipe dreams — but on occasion, they will open analysts’ eyes to a novel approach to which they’d been blinded by force of best practice.

The Value of Cross-Expertise Input

While experienced data scientists will always be the ones responsible for the most advanced analyses and development of sophisticated predictive models, it can be helpful — if not critical — for them to actively receive periodic input from colleagues who might not be formally trained, but who are data literate enough to ask the right questions. In truth, it’s remarkably easy for data professionals to find evidence of a job well done in the seamless execution of a highly complex analysis, and a pair of fresh eyes can go a long way toward aiming an analytics program in the right direction.

Because ultimately the goal of data analytics must always be to produce real, tangible business value. Analytics is not an end, but a means, and ensuring these means are wielded effectively takes a team effort. More often than not, unlocking this effort is a matter of creating an organization-wide “culture of analytics enablement” in which the value of data is not only recognized and supported, but sprinkled throughout the organization’s core processes.

By committing to this kind of culture, by encouraging employees to “get their hands dirty” regardless of their background, organizations can lay the groundwork for an end-to-end data-driven apparatus that will distinguish them from their competition for many years to come.

Written by Kevin Troyanos

I lead the Analytics & Data Science practice at Saatchi & Saatchi Wellness. I have focused my career within the healthcare marketing analytics space, empowering healthcare marketers with data-driven strategic guidance while developing innovative solutions to healthcare marketing problems through the power of data science. I’ve worked to measure, predict, and optimize marketing and business outcomes across personal, non-personal, digital, and social channels. I’ve led engagements with brands that span all stages of the product lifecycle, with a particular focus on established brands. My role is to guide the departmental vision and lead innovation initiatives, effectively positioning marketing analytics as a competitive differentiator and organic growth driver for the agency at large.

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