Kerstin Frailey, who leads data science at market research company Numerator, recently participated in data analytics school Promotable’s webinar series. While presenting concepts such as Machine Learning and the overly propagandized “Big Data,” there was this sign-of-the-times statement from her:
“It’s hard not to care about data quality when you see what happens with data. Because data underlies every algorithm that is automatically approving or denying you a mortgage, that is automatically dismissing or accepting your application to go on to a recruiter to see. It underlies all of the automated admissions that next or current generations are having … That is all built on data. As soon as that data starts to get a little sticky, oh, the world we create in there.”
Data and the modern era do stir wonder. One constant is that data keeps accruing—becoming its own multiverse where the possibilities of use are grand and endless. In the startup ecosystem, “data-driven” is a popular prefix to distinctly qualify a product or service. When elegantly executed, it demonstrates how business, design and technology can be systematized. The emphasis by Kerstin on data’s “underlying” nature feeds into visualizing data as a shifting, sprawling tectonic layer (which, no doubt, it is) influencing everyone and everything. In its composition and expanse, data (for all its content, support and magical potential) is infrastructure.
The last line of Kerstin’s proclamation includes this poetic phrase: “the world we create.” In context, it sparkles with analytics aspiration, coupled with prospective capabilities—for the better. The wellspring here is data—running through several, practical, important applications she noted: mortgages, hiring submissions, school admissions, among a great many processes. The data-propelled world, shaped humanely, co-exists with a world energized by data that’s steered toward inflicting alternative effects—when viewed through a literary lens, they can be characterized precisely as Kafkaesque, even Orwellian.
Though not surprising, it is refreshing to hear Kerstin speak about the importance of critical thinking. Working with data makes it a must-do (as opposed to a no-brainer) for Data Quality to undergo rigor in how it’s managed. From Kerstin, this body of scientific disciplines consists of these principles:
- Accuracy
- Timeliness
- Validity
- Consistency
- Completeness
If quality of data suggests the quality of decision-making, then critical thinking is essential. More so, when data faces duality, exacerbated by cross-generational disparity uncovered by these pandemic times, which exposed data-driven systems not behaving as data-driven solutions. From breakages in delivering public education, to filing unemployment claims, to receiving healthcare, to booking a vaccination appointment, and so on.
Kristen's focus on Data Quality hones in on making reality an honest one—these days, a collective movement reinforced. With the beauty of objectivity in mind, here’s to the people having at it to create a world—where data helps bring out the best in everyone.
Thanks again to Promotable who pair their virtual workshops with talks organized regularly online! Explore their channel on YouTube.
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