At first pass, data evangelism may sound more like an oxymoron than a corporate function. Most of us (and our dictionaries) associate evangelism with faith, while data & analytics is core to the scientific method. Evangelism is predominantly qualitative while data & analytics is the definition of quantitative.
In practice, data evangelism has become synonymous with spreading the good word of data. Need to inspire your team to balance their gut-based approach to problem solving with data-driven insights? Call in a Data Evangelist.
However, if we delve beneath the surface, data + evangelism reveals a richer value proposition. Evangelism teaches us to practice what we preach. Lead by example. Be the change we want to see in the world. Data & analytics teaches us to measure what matters. Hypothesize, test, minimize our biases, refine, and always let our data be our guide.
If we marry the tenets of data + evangelism, the result is: Practicing the data & analytical methods we preach. Leading others to leverage data as an asset via a data-driven approach. Challenge ourselves as data evangelists to be at the forefront of data-driven models and insights, especially in the most qualitative domains.
Data Evangelism Needs a Model
In data science, once you understand the data and its significance to the business, the next step is to create, stress test and refine a model which presents a simplified version of the business problem or opportunity you’re seeking to address. This model is a first attempt to explain the workforce’s relationship to data and provide actionable insights into creating (or maintaining) a data-driven enterprise.
- Data IQ — The level to which a person is capable of leveraging data & analytics relative on his or her role and goal. For example, a food coordinator who is data literate and comfortable using a simple forecasting model will have a high Data IQ. If, however, s/he wants to lead an engineering team responsible for a machine learning-based technology, a Master’s or PhD in AI will be the new standard for a high Data IQ.
- Data Enablement — The level to which a person is enabled (or unable) to leverage data & analytics relative to his or her role and goal. For example, a people manager in HR may be fully Data Enabled via: data literacy, foundational data science for leaders, a dashboard which provides him/her the relevant people analytics and insights about their team, access to data & analytical talent on a project-by-project basis, and a steady stream of curated content including training, best practice sharing, and success stories. However, someone managing a data science team would need all of that and much more, including tools and platforms which allow for reusable asset (i.e. models and code) sharing, to be Data Enabled.
- Enthusiasts — Low Data IQ; Data Enabled: Well connected to their data & analytics community, fluent in its success stories but unsure how to begin leveraging data. Example: A marketing new hire with a degree in literature who marvels at chatbots.
- Data Illiterate — Low Data IQ; Data Unable: Lack of understanding regarding the value of leveraging data & analytics as well as how to do so. Example: An experienced technical writer who leans into his/her qualitative strengths.
- Siloed High Performers — High Data IQ; Data Unable: Limited by their isolation. Typically start from scratch instead of having a library of assets at their fingertips and peers with whom to collaborate. Example: a data scientist working on a non-data science team without access to mentorship, peers, enterprise tools, platforms and data products/services.
- Data-Driven — High Data IQ; Data Enabled: Individuals have the platforms, infrastructure, tools, services, and knowledge to leverage data & analytics in their role. Connections into the larger community provide them with a constant stream of ideas, best practices, and opportunities to collaborate as well as share their work. This is the target state.
Data-driven workforces, whose employees have High Data IQs and are Data Enabled, power the most digitally disruptive companies in the world.
Should we start looking to data evangelism as a business driver?
Data-Driven by an Evangelism Engine
How does this play out? Let’s say a Customer Success Executive leverages data that is 22% more accurate than previously possible to enable 96% adoption of the collaboration tools his/her customer purchased. The customer wins by realizing a high ROI; Because the customer wins, the Customer Success Executive wins. Evangelism’s “win” is in enabling the person or team behind the 22% increase in data accuracy and the Customer Success Executive to leverage said data to achieve (and know s/he achieved) 96% adoption.
Our efforts to influence Data IQs take the form of a multi-pronged (and evolving) strategy of recruiting, learning & development, and continuous education.
We approach Data Enablement more broadly. Success in this domain doesn’t just take a village, but rather the support of the entire Data & Analytics business unit in addition to strong cross-functional partnerships. Data Enablement encompasses building, buying, supporting and/or co-creating the data products and services needed to enable each role- as well as those products’ and services’ adoption.
While far from an exhaustive list, Data Enablement includes global virtual and live events, Kaggle-style data science competitions, collaboration platforms for technical and non-technical best (and worst) practice sharing, an enterprise data science platform with reusable asset libraries, and democratized trustworthy datasets… and as data & analytics (and data evangelism) matures, who knows?
For those of you who have worked with Data Evangelists (or are one yourself!), how does this approach compare with your experience? What have you found to be successful? For those of you who haven’t worked with Data Evangelists, do you see value in doing so? Good, bad, or indifferent, please leave your comments below. I’d love to hear them!
Jennifer Redmon is Cisco’s Chief Data Evangelist. Her charter is to enable a data-driven Cisco culture through globally scaled data platforms, services, and community enablement.
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