Understanding the Fundamentals: A introduction of how to use data accurately to solve complex social issues for multi-cultural groups
Its 2020 and data is easily accessible. It’s available right at our fingertips….. yet why do we keep getting it wrong? In this report, we will explore alternative approaches to using data to transform and drive supportable social change.
Your firm may have the “right” data but may not know how to use it correctly.
Data can not only help you tell more of a complete story about your product/initiatives, but it can demonstrate its value to your audience as well as help you understand their unique needs and be able to help you discover solutions to difficult and complex issues—and that’s critical.
But in today’s data-driven market, there can be an overwhelming amount of data you’re expected to know and digest.
However, before building solution driven models, here is something to consider…
The blueprint for our civilization is conventional theory. Regardless of political party allegiance, our government views and interprets the economy using CT to develop assumptions, models, and conclusions. The majority of decisions are made from this linear perspective, and our trading systems, educational systems, and popular philosophies are all formed from this type of reasoning.
These theories are composed of coordinating supremacy and anti-Black components.
These ideologies are frequently employed to provide a simplified explanation of how the economic system behaves, however it oftentimes serves the opposite purpose by using such language and elements to address the many economic problems that these same belief systems/ principles have actually caused. Also many of these concepts are inadvertently adopted by social justice community leaders who mean well. From this frustratingly confusing paradigm, counterproductive procedures are commonly extracted from this belief system.
A theory's base is its underlying assumptions, and we will explore what those entail in more detail, how they impact ethnic groups' decision-making systematically, and how they relate to quantifying facts and solving problems.
Most data summaries produced by large advisory firms are simplified conclusions, theories and/or philosophies in response to the assumption fundamental concept of inequality of value relative to another group. This also can be viewed somewhat as empiricism.
Although there is ample evidence that inequality exists, when taking a broad/generalized approach to executing challenging summary measures and developing long-term remedies for marginalized groups, this concept is faulty and inaccurate.
Segmenting marginalized populations must be done carefully. When a category is isolated in study, such as Black diaspora groups, fresh information discovered through observation and/or testing must take the place of the original, foundational knowledge ( Faulty and impartial data summaries often generalized & published by capitalistic firms who actively engage in professional practices derived from conservative business models).
I know that was alot!
Let’s think of this concept like cooking, let’s say a recipe recommends measurements, but has omitted information regarding how pot size variations impact the outcome of the recipe…. Although you followed the suggested recommendations to perfection, there is too much water in your pot and your meal is ruined. The refusal to adjust your measurements by replacing the primary source of information with new information that has been observed and analyzed; will continue to make your measurements disproportionate and your dish will come out incomplete in comparison to the people who used the “correct” pot size and/or accurate measurements (unaware/intuitively/intentionally).
In this illustration, the "right" pot size would be consistent with the idea of presumption foundations in data summary models and how people interact and ascribe such information.
If we compared all the cooking participants, then we would see a spectrum of inequalities. In order to create solutions and new methodologies, to address those disparities, we would have to start by grouping pots similar in size and conducting or applying experimental research to each respective group. We would also have to explore other variables such as compositional factors like material, durability and etc. Once we have gathered updated and relevant research, it can be compared to the existing generalized theory( in this case, the original recipe). From there, the right adjustments can be made to perfect the recipe for each pot group and then the recipe would become inclusive for everyone and viewed as equal/fair at a macro level.
Through HelloBritney, I want to make sure you understand the "recipe," are able to use each of these data points of reference to your advantage, and develop tactics that are effective and further your social objective.