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When organizations training and support their power users, they usually focus on individuals. But organizations aren’t just composed of individuals. Humans are social creatures who are often part of rich, complex networks.

Data Chefs argues that we should take advantage of that.

Part of the way we get there is by changing our tools so they support and encourage a smoother learning curve. But equally important is the role of community:

  • Within Organizations: growing an organization’s internal ecosystem that helps different types of users flourish and support each other, using an iterative approach that racks up small, strategic wins while building towards larger victories
  • Across Organizations: creating an ecosystem across organizations so they can share knowledge, pull resources, and collectively learn to smooth the learning curve
  • Across Societies: expanding this ecosystem so it connects large organizations to the community so the community can also benefit and doing so, developing a model and laying the foundation to help communities from Harlem to Harlan County benefit from and have a say over the explosion of wealth that will be created by AI, augmented and virtual reality, and other forms of emerging tech over the next 20 years

how do we help a vendor’s users create a network of allies at their organization and then how do we create it across organizations

If you are just starting up with a vendor, what’s your organizing starter kit? If you believe purple is important, what role does a vendor play, not as a tool but as a gardener, as a Julia Childs, in helping to build a community that nurtures purple people? One ways to create a cookbook of patterns and case studies, unlike machine learning we don’t need a bazillion

multiple on ramps by sector

Makers All, the parent organization for Data Chefs, argues that our society would be far more effective in training community members to become emerging tech developers if we learn from the example of agricultural’s Extension Services, which leveraged the social nature of people to radically transform agricultural practices. For example, Extension Services employed at least one Extension agents in every rural county, who would:

  • Identify and develop Natural Leaders. Extension agents often focused on identifying and developing natural leaders: farmers who were already widely respected in their community. These natural leaders had preexisting social networks and relationships they could use to recruit other famers.

And they were also likely to understand the concerns and fears that agents needed to address if farmers were to be convinced to adopt new techniques.

  • Nurture Neighborhood Clubs. Extension agents helped farming communities form neighborhood clubs and worked with clubs to ensure farmers got a steady stream of ideas about how they could improve their farming. As a result, farmers weren’t just hearing about an idea brought in by an outsider, they were learning while surrounded by their peers who spoke the same language and understood the realities they faced.
  • Foster Community Events. Extension agents helped foster state fairs and other places where farmers could see demonstrations, compete to see who could use new techniques to grow the best crops, etc.

Data Chefs argues that:

  • Individual organizations should use a chopped down, lightweight version of Maker All’s strategy that’s designed to fit the needs of their organization and staff
  • As organizations begin to collaborate across ecosystems, start taking small steps towards the more robust approach envisioned by Makers All

Data Visualization Ambassadors(Divas): At the American Speech-Language-Hearing Association (ASHA), we created an initiative to increase the organization’s data visualization literacy. To do so, we developed a network of users who we calledData Visualization Ambassadors (“Divas”), who we trained and supported in advocating for the effective use of data viz in their department.

  • Code and Community -Multiple on ramps
  • Operate at scale

  • Key isn’t everyone, it’s Natural-born leaders and Upstarts (Blue Flamers)
  • key products: standards, templates, process, mentoring, cheat sheets (share resources across groups)

Introspection: data-driven as x-ray of your organization: where is it broken?

Foster Diversity

Data science, data engineering, analytics, etc. aren’t very diverse. If your organization is committed to diversity and inclusion, how do you ensure you don’t replicate this problem? The key: don’t make diversity an afterthought. As you are trying to grow an ecosystem of power users, design in diversity from the ground up.

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