Magna Notitia

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The Rise of Data Scientists

- August 12, 2022

Data science has been used in physics and astronomy, but it is being applied to businesses and finance, manufacturing, and other fields. The demand for data science professionals exploded about two decades ago when businesses began collecting massive amounts of data, and a shortage of talented individuals, or data scientists, with a specific set of skill sets able to extract the most benefit from the available data (World Data Science, 2022). Data science is a multi-disciplinary field that converges programming, mathematics, statistics, computer science domain expertise, and communication skills, yet seldom organizations are able to find a data scientist that excels at all of them (Bansal, 2021; World Data Science, 2022). The World Data Science Initiative (2022) reports data science jobs were expected to grow by 364,000 in 2020, but the real number came in at 2,720,000. Data scientists became part of the solution to address the challenges of growing organizational data sets, and w

Big Data Analytics at Netflix

- March 23, 2022

Netflix services fall under the subscription video-on-demand (SVOD) services category as a so called over-the-top (OTT) provider, which streams content over the web directly to its customers’ devices (Iordache et al., 2021; Pajkovic, 2021). Netflix’s revenue is mainly generated by users’ subscription fees (Kasula, 2020), and to keep customers from cancelling their access, the company relies heavily on the Netflix Recommender System (NRS) which combines big data analytics with powerful machine learning (ML) algorithms (Jackman & Reddy, 2020; Pajkovic, 2021) to keep users engaged and satisfied with the service. The aim of the NRS is to offer a personalized experience and provide viewing recommendations to each customer (Pajkovic, 2021) based on preferences generated by their rating history (rating the titles they watch), Netflix’s requests for feedback, a “similarity” approach across subscribers, movies, genres, etc. (Kasula, 2020). The NRS is driven by powerful machine learning (

Big Data vs. Conventional Data

- March 23, 2022

New, innovative, and more efficient technologies have enabled the management of big data for analytics to extract meaningful insights for decision-making (Minelli et al., 2013), emphasize the many differences between big data and conventional data. Edd Dumbill as cited in Minelli et al. (2013), defines big data as “data that becomes large enough that it cannot be processed using conventional methods,” and Gartner’s definition in 2001 reads “Big data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity” (Treehouse, 2022), featuring big data’s three V’s: variety, volume, and velocity (Minelli et al., 2013). Kumar et al. (2021) explains that with big data, it is possible to store large volumes of data, but not in a conventional data setup. Conventional data reflects a centralized database architecture, and big data is found in distributed databases. The data sources are many when dealing with big data, but severely limited with conventiona

Human Digital Twin: Sociotechnical Plan Highlights

- February 28, 2022

Introduction The digital twin (DT) concept can be traced back to the National Aeronautics and Space Administration (NASA) Apollo program in the 1970s, where two identical vehicles built for space efforts, and one was able to mirror the conditions of the other during a mission (Wei, 2021). This “mirroring” concept was called “Mirror Space Model,” and later “Information Mirror Model,” until NASA’s John Vickers introduced the new “digital twin” name in his book. Grievers, a professor at the University of Michigan who put forward the concept of “Virtual Digital Expression Equivalent to Physical Product” in a 2003 product lifecycle management (PLM) course, began using Vickers’ DT name in 2011 (Wei, 2021). DT has been successfully applied mainly in the fields of manufacturing and PLM to perform simulations, optimization, make predictions and provide suggestions, performance improvement, and others (Wei, 2021). From these applications, the DT concept expanded to “augmented digital twin” t

ANIMOTO Video: Human Digital Twin (HDT) in Healthcare

- February 17, 2022

How The LEGO Group Dodged Bankruptcy

- February 13, 2022

The LEGO Group (LEGO) had been doing everything right since the introduction of injection-molded building blocks in the late 1940s. The company was always on the lookout for new products, inserted themselves in different markets (video games, amusement parks, education centers, jewelry, etc.), they listened to customer feedback, and gave their development engineers free range to innovate and create new products (Oliver, et al., 2007; Wharton, 2012). LEGO sales plummeted in 1993, and although some of it was attributed to China’s manufacture of similar products at a fraction of the cost, the big-box store (e.g., Wal-Mart) phenomenon, and the merging of such big-box stores (Wharton, 2012), these and other challenges resulted from significant market changes. Production costs were rising in Denmark, Switzerland, and United States, pushing LEGO to outsource plastic production to México and the Czech Republic (Oliver et al., 2007; Wharton, 2012). Another challenge was LEGO’s over-diversifi

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