Bridging the Data Fluency Gap: Strategies in Digital Thinking for Enterprises Approaching AI

By Data Science Salon

While digital fluency may involve some aspects of data fluency, digital fluency is a broader concept that encompasses a range of skills such as using software applications, navigating platforms, and optimizing workflows via digital means.

Data fluency, considered to be a specialized area within the broader domain of digital fluency, centers more on understanding details like how data is gathered or cleaned, how models abstract and transform data, and most importantly- how to communicate data processes and results in a way that can be understood by others. In the enterprise, data fluency helps to derive insights and supports informed decision-making that can lead to better business outcomes.

With enterprises looking towards AI as a means to sharpen a competitive edge, adapting and evolving digital and data fluency with the use of AI technologies should aid businesses in leveraging the most from their investments and efforts. As companies rely on more data-driven decision-making and digital technologies to drive success, both digital and data fluency are being recognized as increasingly important and valued.

Breaking down the basic pillars of digital fluency

MJ Petroni, Chief Exponential Officer with Causeit, co-hosted a webinar with Anna Anisin, Founder at Data Science Salon earlier this month, to discuss “The Data Fluency Gap in Enterprises & the Impact on AI Strategy.” The conversation began with Anna discussing the shared experiences of community members within the Data Science Salon Community around data fluency and the nature of its presence or absence within an organization. She reported a common experience among professionals in the industry was recognizing the need and value in improving data fluency between their own departments and any associated executive tiers or decision-making seats.

Overall, community members affirmed positive experiences and greater impacts when high levels of data fluency existed within the whole of an organization- between individuals and teams, supervisors and departments, CEOs and investors. With an eye towards improving fluency throughout an enterprise, MJ identified key pillars about digital fluency to help companies ground their efforts and more surgically address bridging gaps by revising perspectives on 1) thinking, 2) data, 3) business models, 4) tools and skills.

Screenshot 2023-04-12 at 11.23.17Graphic by Causeit. Print screen taken during Data Fluency webinar.

1. Embrace shifts in thinking culture

When organizations are willing to embrace opportunities that come with acknowledging the digital era we live in, companies can shift more intentionally into digital thinking across the whole of their organization. Newer companies, free from the inertia of long-standing habits, may be quicker to adopt a digital thinking perspective than organizations with more history, priming them to fluently move forward with less resistance. 

2. Revisit the relationship with the data and clarify digital value

Companies could further reconcile the relationship they have with their data, more specifically the value they are creating or yielding from it. Having a data strategy in place and understanding the facets of working with data, from how it is gathered, to how it is structured, to how it is further abstracted and interpreted can derive clearer relationships with the digital value at hand, guiding a more comprehensive and ethical monetization of it for business interest. 

3. Be open to new concepts of business models

Business Models represent another area where shifts in perspective can assist in adopting new thinking on who creates value and how it is delivered. Citing newer business models like IP monetization and platform economies, MJ pointed out the value to be gained when organizations have the capacity to shift perspectives on what a working business model can be.

4. Adopt tools and develop skills-sets with intentionality

Selecting the best fitting tools and cultivating the appropriate skills to utilize those tools are areas organizations are further encouraged to apply critical discernments towards; investing in a thoughtful adoption of tools and precise upskilling relevant to those tools will aid organizations in avoiding unintended tangents, supporting and streamlining an organization’s desired direction.

With AI in mind, having an open mind to digital thinking and unrealized business models in tandem with data integrity, tool efficacy, and fitting skill sets can serve to differentiate businesses from being generic in their applications of AI to being genuinely competitive.

Three archetypes in the network of digital thinkers

Another framework to aid companies in establishing more organized perspectives on the roles individuals might act out, occupying a certain frontline on the spectrum of data fluency within an organization, MJ outlined three human archetypes and their roles within the network of digital thinkers: the Champion, the Explorer, and the Maker.

The Champion

Champions are typified as leaders who bring resources and the push to make things happen. They are key to influencing the necessary buy-in from senior management and other stakeholders, having a more dialed-in and grounded perspective on the trajectory and interests of a company. With AI adoption, champions are likely to be more in-tune at navigating the balance between risk and reward and effective at communicating complexities in more relatable formats.

The Explorer

Explorers, on the other hand, have a penchant for sighting new opportunities and emerging trends. They are able to identify gaps in the market and tend to promote innovative solutions towards unmet needs. With AI technologies, this archetype is likely more adept at identifying where the greatest impact could be had within an organization.

The Maker

Makers possess the technical know-how to make the digital prototype. Makers are able to evaluate and test feasibility, satisfying the business instinct and intent outlined by the champion while bringing to the ground and further testing the explorer’s ambitions and curiosities. With AI initiatives, makers bring the technical expertise to designing and developing solutions that deliver measurable results.

Together, these archetypes form a powerful network of digital thinkers who are able to support each other in driving innovation and transformation within an organization. By leveraging their respective strengths and working in orchestration, champions, explorers, and makers demonstrate different nuances in the application of their data fluency, ushering in new products and services and delivering value to customers and stakeholders.


Questions for Quants

Quantitative thinkers are a more diluted and broader embodiment of the common denominator shared between the archetypes; MJ defines a quant as, “anyone rigorously applying the scientific method to quantitative data.” Ultimately, quants within an organization are individuals with the greatest potential to establish bridges in data fluency, thus evolving stakeholder perspectives, while also steering the direction and development of analytical programs and initiatives. 

To help quants establish a more robust impact in the embodiment of their capacity for fluency, MJ encourages quants to proactively tackle key questions such as:

  • What should be known about a business for the most useful analysis? 
  • What trends do leaders need to be made aware of? 
  • What data will inform better decision-making?
  • Within an organization, where are initiatives operating on “hunches” rather than proof?  

Taking the time to address questions like these can aid quantitative thinkers and doers with establishing more precision in their efforts. Understanding the strategy of a business, for instance, can make or break the business utility of an analysis that uses meaningful time and resources to develop.

One day at a time, road mapping a plan

Moving an organization forward is best done by outlining clear goals and establishing realistic benchmarks. The value of deploying a plan is no different when it comes to developing data fluency throughout an organization. 

Anna pointed to the experience of the Data Science Salon community members, affirming that ethical reflections and assessments of legal implications regarding the data and its related process, were priorities in fluency that when overlooked could be consequential to a business. 

In the early stages of developing data fluency, assessing the scope of fluency within a team, and educating involved parties on any ethical and legal considerations will go a long way towards avoiding misuse, clarifying a team’s capacity, and highlighting vacancies in skills. Further identifying any archetypes that exist within the organization can outline avenues already embedded for more nuanced activations of fluency.

In the later stages, committing to data principles throughout an organization, and logging or publishing an organized source of reference for available datasets, any API’s and AI functions or technologies used, as well as notable insights gained through initial trial and error will aid in charting achievements of past efforts, helping to mitigate redundancies in the use of the resource as well as properly orienting new parties.


In conclusion, bridging digital and data fluency gaps within enterprises can be approached with less overwhelm and more precision by having a clear roadmap and breaking down evolutions of fluency into narrower areas of focuses an organization can aim to fortify or encourage beneficial shifts in, such as an organization's thinking culture, relationships to the data itself, perspectives on business models and monetization, and the library of tools and accompanying skill-sets

Organizations can further identify behavioral personalities and the talent inclination of individuals within the company, intentionally connecting relationships that facilitate a more data-fluent social network.

As enterprises look to more deeper explorations and integrations of AI technologies, being intentional about leveling up the scope of fluency within teams, and cultivating activations of fluency throughout an organization offers the value of differentiating a generic application of AI in the enterprise from a more disruptive, impactful, and uniquely competitive one.

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