Literature Review

[bruce1edward]

Here are collections of reads I found may be worth to take a closer look :

Data Observability : monitoring, tracking, and triaging of incidents to prevent data downtime (or provide reliable data)
https://towardsdatascience.com/what-is-data-observability-40b337971e3e

Data as a product :
https://www.montecarlodata.com/blog-how-to-treat-your-data-like-a-product/#:~:text=%E2%80%9CData%20as%20a%20Product%20(DaaP,personalized%20products%2C%20or%20detecting%20fraud.

Data as a product vs Data product:
https://towardsdatascience.com/data-as-a-product-vs-data-products-what-are-the-differences-b43ddbb0f123

Data as a Product vs. Data as a Service:
https://medium.com/@itunpredictable/data-as-a-product-vs-data-as-a-service-d9f7e622dc55

How big data can revolutionize pharmaceutical R&D :
https://www.mckinsey.com/industries/life-sciences/our-insights/how-big-data-can-revolutionize-pharmaceutical-r-and-d

Gower's distance :
https://daesoolee.tistory.com/112

Dolt is Git for Data! :
https://github.com/dolthub/dolt

lumberjack: Track Changes in Data :
https://cran.r-project.org/web/packages/lumberjack/index.html

Nice blog regarding compare data :
https://bookdown.org/Maxine/r4ds/comparing-two-data-frames-tibbles.html

dissimilarity metrics :
https://towardsdatascience.com/17-types-of-similarity-and-dissimilarity-measures-used-in-data-science-3eb914d2681

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Unsupervised Anomaly Detection:

blogs :
https://towardsdatascience.com/unsupervised-anomaly-detection-in-python-f2e61be17c2b#:~:text=Unsupervised%20anomaly%20detection%20involves%20an,the%20%E2%80%9Cnormal%E2%80%9D%20data%20points.

https://towardsdatascience.com/unsupervised-learning-for-anomaly-detection-44c55a96b8c1

papers :
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection
https://arxiv.org/pdf/2206.15476v1.pdf

Nice blogs on PCA with Anomaly Detection :
https://www.oreilly.com/library/view/hands-on-unsupervised-learning/9781492035633/ch04.html