This is Not a Pipe

All data is semiotic. That is, it is representative and lacks meaning without context. A basic example of a datum is a count. This can be expected to be a positive integer, but in any case, it stands in for something else (e.g., my cat weighs 10 pounds).

Choosing to count something is an act that introduces bias, as is choosing not to count something. Creating data without some form of bias is impossible. An example of this problem can be seen in crime forecasting. We can use the number of arrests as a metric on a map to predict where more arrests will happen in the future, but we would not be seeing crime, but rather where arrests tend to happen. In this case, the number of arrests is a poor representation for crime.

Magritte’s The Treachery of Images has the label “this is not a pipe,” and he was correct, it is a painting of a pipe. (Magritte, 1929)

All Models Are Wrong

Due to the semiotic nature, it can be dangerous to deploy systems that make decisions based on data. In the above example, allocating resources based the on number of arrests will only reinforce existing structures. You may see more arrests, especially if there is a mechanistic relationship between resources and the number of arrests, but you are not actually addressing crime. This is not to say that we cannot use data towards beneficial ends.

For example, if I regularly weigh my cat and the scale always says she is 10 pounds, I can use that as a non-invasive health diagnostic going forward. If I put her on the scale and it says she is 12 pounds, I know that I might be overfeeding her; if the scale says she is 8 pounds, it is probably time to bring her in to see a veterinarian. My scale does not need to be accurate for this to work, and just because her weight fluctuates does not mean that there is a health problem. The aphorism, “all models are wrong, but some are useful,” (Box, 1987) reflects this. Data is always standing in for something, but we can still use it to make better decisions.

A Ship in Harbor is Safe

Your organization might have outstanding data management practices, but concerns about data security or the idea of offloading executive decision making to a machine. The only perfectly secure data is that which cannot be accessed by anyone, and if you don’t build models, you never have to use them. “A ship in harbor is safe, but that is not what ships are built for,” (Shedd, 1928) applies here. If an organization is so concerned about security that it does not tolerate its use, and there are no regulatory requirements, it should not spend resources keeping the data. On the other hand, if the hesitation is about relying on a novel approach, or making data-driven decisions, we can address that!

Written By: Travis Dawry, MS, Strategic Analytics Faculty

For more information on the Strategic Analytics MS or other online master’s degrees available at GPS, please visit brandeis.edu/gps.

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Works Cited

Box, G. E. (1987). Empirical Model-Building and Response Surfaces. Wiley.
Magritte, R. (1929). La trahison des images [Ceci n’est pas une pipe]. Belgium.
Shedd, J. A. (1928). Salt from My Attic. Mosher Press.

The search for the ideal master’s program is difficult, as one must consider many factors: timing, location, and, of course, unique program features and requirements. The search becomes even more complicated when the field you are interested in is closely linked to a wide variety of industries. Marketing, being an industry sheltered within the much larger, broader world of business, is one of these fields.

Pursuing an MBA may be alluring if one is interested in entering the field of marketing. On the surface, a program teaching the ins-and-outs of business management seems like a natural next-step towards marketing director and marketing management positions. However, depending on your learning goals, a specialized program in marketing may be the better step for your education.

Many MBA programs offer specialization in marketing, but they rarely provide a dive deep into the marketing industry and its functions. Much of an MBA program consists of management, finance, and accounting courses, which leaves little room for electives that would be more relevant to your intended career path in marketing. 

Marketing is an ever-changing industry with new optimizations, trends, and regulations to stay up to date on. The field is now almost completely digital, with social media and the internet gaining a larger and larger role in marketing strategies. Positions like the role of Social Media Manager and Digital Marketing Strategist require individuals to be highly skilled in the areas of copy-editing, graphic design, brand strategy, social media optimization, videography, and more. 

Digital marketing is a high growth field – which is why a degree in digital marketing can be extremely useful. The core classes in our Digital Marketing and Design master’s degree gives students a much deeper understanding of the inner workings of digital environments. Courses centering around concepts like Search Engine Optimization, Conversion Rate Optimization, and User Experience Design give students the tools necessary to run more effective digital marketing campaigns. 

The Brandeis GPS program in Digital Marketing and Design also stands out due to its inclusion of design. Despite its usefulness, design is rarely incorporated into marketing programs. With offerings like User Interface Design, Design Ideation & Prototyping, and Universal Design & Digital Accessibility, the Brandeis GPS program in Digital Marketing and Design offers students a competitive edge, helping them to gain expertise in both the technical and creative components of marketing.  

The importance of degree specialization should not be underestimated. If you are planning to further your education and enhance your career by pursuing a master’s degree, it is crucial that you keep this in mind.

For more information about the Brandeis GPS master’s in Digital Marketing and Design, visit the program webpage.

Evidence-based assessment design (ECD) provides a conceptual framework for identifying observable evidence that supports the measurement of unobservable constructs, defined as as knowledge, skills and attributes by Mislay and his colleagues in their 2004 article A Brief Introduction to Evidence-Centered Design. Course analytics is micro-level learning analytics that validates the assessment outcomes and elicit desirable behaviors via harnessing learning data. 

In the original ECD framework formalized by Mislay and colleagues (2003), the initial phase is to define what to measure in terms of KSA (Knowledge, Skills and Attributes). Since the KSA cannot be observed directly, we need to come up with the measurable, which can constitute evidence about KSA variables. The next step is to identify tasks or situations that can elicit observable evidence about latent KSAs. In some cases, attributes are referred to as ‘psychological constructs.’ The noncognitive factors, such as engagement and perseverance, are often considered as strong predictors of academic outcomes.

Although the fundamental ideas of ECD framework remain solid, the current digital revolution has changed the content of each model within the framework, which introduced new possibilities of what evidence we are able to observe, what data we are able to track, how we are able to mine the data (EDM) and make inferences of it (LA).

Here is an example of demonstrating the conceptual linkages between observable evidence and inferences in the rapidly-evolved digital world.

The data used was fabricated from group communications, which consists of directional weighted links that represent a direct message from one to another, message to the entire group and length of a reply. The tasks are composed of reflective writing and knowledge sharing. The task model elicits the evidence from individuals in response to given prompts about their understanding of topics. The technologies support us to harvest and harness the evidence, which otherwise, could not be fulfilled. Temporal networks are network representations that flow over time. In educational settings, they are useful for visualizing how a learning community develops or evolves through time. The time indices are an ordered sequence. This ordering can reveal information about what is occurring in the network through time. Below is an animated temporal network diagram, I generated in R using the fabricated data, that shows the dynamic of interactions over several weeks. Each node represents an individual, the links denote sending a message from one to another or addressing to the entire group, the color corresponds to the sender.

Written by: Jing Qi, Ed.D., Faculty Learning Experience Design

For more information on the Learning Experience Design program or other online master’s degrees available at GPS, please visit brandeis.edu/gps.