The Leadership Illusion: The Importance of Context and Connections
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But in community organizing work, the data are all about the people. They describe the lives they live and the factors that impact their ability to thrive. But much of their life stories will not be found in the data we currently have. The stories of people are messy; their lives are more complex than the few data points that record a version of their lives in data. But much of the knowledge in this work lives outside of a cleaned dataset. In the search for certainty, we as data scientists often clean out much of this complexity, and in the process lose a lot of context, missing the true narrative.
In my summer work, I did all kinds of math in an attempt to delete bias or assumptions that could impact the certainty and purity of my results. However, when that failed, I ended up using a combination of google maps and community-created neighborhood boundaries to inform my work. In this case, understanding the data as places, with people and different stories, was more important than knowing the exact numbers and census-generated identifying features. Local history and policies shape the neighborhoods of Durham in ways not obvious from my data; for instance, Braggtown, was a separate, unincorporated community until , when it became a part of Durham.
While this neighborhood has a strong identity, it is divided across several block groups in my data.
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By looking more closely at the dataset as places rather than identifiers, I was able to locate the outliers and areas where small or divided numbers were causing exaggerated rates. With revisions based on community knowledge from websites such as Durham Hoods and Open Durham , I ran the analysis a second time.
These revisions provided significant results. Once I eliminated the perspective of mathematically-generated clusters, the relationships between chronic disease outcomes and evictions became clear. Specifically, the revised models demonstrated that neighborhoods with higher eviction rates and higher uninsured rates also had higher rates of poorly managed diabetes. It is scary to me as a future data scientist to know how easy it is to let models stand as truths, without being checked by cultural reality.
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Rather than bringing the tools and a solution to DataWorks NC, I find myself leaving my internship with the seed of an idea: the idea that the data science community needs to think outside of certainty. As ethical data scientists, it is important that we understand that the way we communicate findings has real consequences. Data science as a field would benefit from finding ways to appreciate the complexity of the lives data describe, and connect with them in a meaningful way.
The Leadership Illusion : The Importance of Context and Connections
If nothing else, we must acknowledge the limitations of the data we have and recognize the community as the experts in these stories. Community members, organizers, and non-profits fight for a place to be heard, so their knowledge can be shared and understood. I ask my fellow data scientists, do our data processes have the capacity to pick up on this community intelligence?
Do the data that we work so hard to wrangle illuminate these voices? Messy data can be scrubbed into tidy models.
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But a strong model is not the same thing as a true model; this comes with an in-depth understanding of the data. The wisdom of these truths lies with the people; and the knowledge of people is an asset we are still learning to harness. This definitely resonates.
She has consulted to a wide range of organisations on a number of topics from change management to the assessment and development of senior executives. Her current focus is upon enabling leaders to achieve their potential and their goals through the development of diverse social networks. Tony and Karen are co-founders of a specialist consultancy that provides tools and development to leaders and their organisations in order to deliver their strategic goals through effective social networks. They can be contacted via: enquiries four-pi. Du kanske gillar.
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