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Feeling compelled to get to the bottom of this misunderstanding, I asked Bee if her physician had actually explained to her why she received a diabetes diagnosis. She said no, and when I offered to explain she was willing to hear me out.
For the next 7 or so minutes, I walked Bee through her chart and explained what an a1c is and what it represents, discussed her specific a1c, and helped her connect the dots to understand how it all contributed to her diagnosis. I also spent a significant amount of time discussing the prevalence of diabetes in Black communities and the importance of managing blood sugar levels as optimally as possible to prevent serious complications. And we both shared about our families' experiences with the condition.
While Part One of this essay will explore several (though not nearly all!) the reasons trust has declined, Part Two helps clinical healthcare providers like you reimagine patient education through a refreshing take on an undervalued, underutilized tool that you can apply in any clinical setting and any type of patient interaction.
While HCPs are well-equipped to inoculate against and correct misinformation through patient education efforts, we must admit that in a post-pandemic, social media-driven world, we run the risk of no longer being well-positioned to effectively educate patients because of declining trust in the public health and healthcare system.
Disinformation, on the other hand, is intentionally spreading misinformation with malicious intent and often through complex, deliberate campaigns. Entire communities can be and are targeted with disinformation.
The problems of misinformation and disinformation, coupled with gaps in our shared language and perspectives create a weak base for HCPs to be able to effectively promote behavior change through patient education with the public.
Scottsdale Museum of Contemporary Art (SMoCA) presents Language in Times of Miscommunication featuring artwork that incorporates various forms of language (poetry, speculative fiction, and slang), modes of communication (propaganda, protest, social media, and advertising), and research materials (archives, political documents, and the news) that together form a timely exchange about the slippery relationship between opinion, fact, and fiction, within the construct of our collective reality.
Conceptual exhibition catalog
The studio Polymode designed the Language in Times of Miscommunication catalog as a twofold object, publication and artwork, that clearly relays information while simultaneously conveying notions of intentional and unintentional miscommunication. Through this experimental approach, Polymode progressively complicates the visual reception of information in the catalog, oscillating between moments of clarity and distortion.
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To further drive this point, in this article, we will utilize the fake company Sell Online Corp to illustrate how data perspectives differ. In this use case, Sell Online Corp needs to present to their board how their major push for user reviews of products led to increased sales. This is a deceptively simple question that often uncovers the gaps in which different teams understand the semantics of their data.
For our use case of Sell Online Corp, we are going to utilize a monolith e-commerce architecture for its simplicity, as our focus is on data architecture rather than software architecture. Below is an example of what data the end user sees when viewing an item for purchase and the simple infrastructure behind it:
In the given scenario, the data is no longer utilized for straightforward CRUD operations that have explicit requirements tied to the product. Instead, the data is now used for complex analytical queries, where the requirements are refined as the comprehension of the underlying data improves. This differentiation results in a decoupling of perspectives regarding data usage between OLTP and OLAP users. This results in varied understandings of the data that eventually give rise to complexity and data quality issues.
Now this is a very specific use case, but imagine the level of complexity in logic that can be found amongst numerous product features and workflows within a mature product. Managing this complexity becomes quite the challenge as an organization scales and iterates in different directions.
In our Average Stars use case, why would the OLTP side opt for a simple calculation of all average stars rather than adding the additional logic to filter to the latest reviews by a user? This goes back to HOW data is operationalized among data producers (OLTP databases) and data consumers (OLAP databases), as well as the constraints of those teams.
Under these constraints, a valid reason for calculating Average Stars is for simplicity within the product codebase. The use case of someone updating the review for a product is a small fraction of overall reviews for Sell Online Corp; thus the added complexity of filtering for first or latest reviews by a user was not worth it. In other words, it was a product decision over a data decision, as the OLTP database serves the product with respect to user transactions.
Often the data producers on the OLTP side and the data consumers on the OLAP side both have valid reasons for how they view the same data differently. What matters less is which side is right, and instead, we need to focus on what tradeoffs from both sides best serve the business via data.
If you want to review a real-world use case of these tradeoffs, then I highly encourage checking out Uber Freight Carrier Metrics with Near-Real-Time Analytics | Uber Blog where they describe the pros and cons of various OLTP and OLAP configurations on their Carrier App.
Therefore, the solution to this divide between OLTP and OLAP database users is not to force the benefits of one side or the other but to instead foster empathy so that both sides can translate the needs of each other. At this HR tech job, we made great strides by including the data consumers in the product requirements document (PRD) writing phase. It worked well because we added the OLAP perspective before data producers were constrained by existing requirements to deliver a product on a tight deadline. For the data consumers, we finally had an opportunity to see upstream and learn the product constraints that impacted our business logic.
OLAP and the "need" for denormalisation is a misconception. OLTP/OLAP refer to the physical aspect of SQL database products but underlying it we suppose the relational data model and this is the same independently of OLTP/OLAP. An "analytical table" should also be normalised (5N or 6N not 3N [or more commonly Boyce-Codd Normal Form]). The "denormalisation" might appear as an intermediate logical step (not part of the data model per se) before an aggregation (or other function) is calculated but once data is aggregated the result relation should be normalised too.
\uD83D\uDC4B Hi folks, thanks for reading my newsletter! My name is Chad Sanderson, and I write about data, data products, data modeling, and the future of data engineering and data architecture. In today\u2019s article, I\u2019m inviting Mark Freeman to share his experience in managing the relationship between data producers and consumers. Please consider subscribing if you haven\u2019t already, reach out on LinkedIn if you ever want to connect, and join our Slack community Data Quality Camp for practitioner-led advice on Data Contracts and Data Quality at scale! 152ee80cbc