Tag Archives: Machine Learning

Effectiveness vs. Efficiency

austin-distel-WtXcbWXK_ww-unsplash.jpgThese two “E-F-F” words are what drives a lot of what happens in society today. If you want to run a successful business or a successful organization, you’ve gotta find a way to increase your efficiency, while maximizing your effectiveness. But which one is more important? As is often the case in life, it depends on the context.

~

For me, I’ve often defaulted to trying to be as efficient as I can (mostly). In the last few months, I’ve come to realize that my worshipping at the feet of efficiency might actually be costing me in effectiveness. Huh? Yeah, if you’re too efficient, it might mean that you’re lowering your effectiveness. In my head, I’m imagining one of those graphs where efficiency is plotted on the x-axis and effectiveness on the y-axis. As efficiency goes up, so does effectiveness. However, there’s a point towards the right of the graph where an increase in efficiency leads to a decrease in effectiveness. Becoming more efficient is no longer in yours (or the company’s best interest).

That’s a lot of words — how about an example. Artificial Intelligence and Machine Learning. A recent episode of The Next Big Idea illustrated how you can have a wholly efficient system in AI/ML, but this increase in efficiency is lowering the system’s effectiveness. For example, let’s say you’ve got an algorithm that’s screening out candidates for specific qualities in your hiring process. Using AI/ML can accelerate the efficiency — it can read 1,000’s of applications in the time it would take a human to read a handful of applications. However, the system probably won’t be well-calibrated for racial bias (as has often been showing on numerous occasions). Increase in efficiency, decrease in effectiveness.

I liked the way that Prof. Eberhardt framed her suggestion: “add more friction to the system.” This will lower the efficiency of it, but it will increase the effectiveness. By adding friction, you’ll slow down the processing speed, but the slow down in time will build-in an opportunity to correct misgivings before they become official.

~

This also reminds me of a Farnam Street article about the difference between speed and velocity:

Velocity and speed are different things. Speed is the distance traveled over time. I can run around in circles with a lot of speed and cover several miles that way, but I’m not getting anywhere. Velocity measures displacement. It’s direction-aware.

A lot of people think in terms of one dimension (speed). Almost all of those people are passed by people who think in multiple dimensions (velocity).

What is Data Science?

There’s no question that “data science” is becoming more and more popular. In fact, Booz Allen Hamilton (a consultancy) found:

The term Data Science appeared in the computer science literature throughout the 1960s-1980s. It was not until the late 1990s, however, that the field as we describe it here, began to emerge from the statistics and data mining communities. Data Science was first introduced as an independent discipline in 2001. Since that time, there have been countless articles advancing the discipline, culminating with Data Scientist being declared the sexiest job of the 21st century.

Unsurprisingly, there are countless graduate and undergraduate programs in data science (Harvard, Berkeley, Waterloo, etc.), but what is data science, exactly?

Given that the field is still in its proverbial infancy, there are a number of different perspectives. Booz Allen offers the following in their Field Guide to Data Science from 2015: “Describing Data Science is like trying to describe a sunset — it should be easy, but somehow capturing the words is impossible.”

Pithiness aside, there does seem to be consensus around some of the pertinent themes contained within data science. For instance, a key component is usually “Big Data” (both unstructured and structured data). Dovetailing with Big Data, “statistics” is often cited as an important component. In particular, an understanding of the science of statistics (hypothesis-testing, etc.), including the ability to manipulate data and almost always — the ability to turn that data into something that non-data scientists can understand (i.e. charts, graphs, etc.). The other big component is “programming.” Given the size of the datasets, Excel often isn’t the best option for interacting with the data. As a result, most data scientists need to have their programming skills up to snuff (often times in more than one language).

What’s a Data Scientist?

Now that we know the three major components of data science are statistics, programming, and data visualization, do you think you could identify data scientists from statisticians, programmers, or data visualization experts? It’s a trick question — they’re all data scientists (broadly speaking).

A few years ago, O’Reilly Media conducted research on data scientists:

Why do people use the term “data scientist” to describe all of these professionals?

[…]

We think that terms like “data scientist,” “analytics,” and “big data” are the result of what one might call a “buzzword meat grinder.” The people doing this work used to come from more traditional and established fields: statistics, machine learning, databases, operations research, business intelligence, social or physical sciences, and more. All of those professions have clear expectations about what a practitioner is able to do (and not do), substantial communities, and well-defined educational and career paths, including specializations based on the intersection of available skill sets and market needs. This is not yet true of the new buzzwords. Instead, ambiguity reigns, leading to impaired communication (Grice, 1975) and failures to efficiently match talent to projects.

So… the ambiguity in understanding the meaning of data science stems from a failure to communicate? Classic movie references aside, the research from O’Reilly identified four main “clusters” of data scientists (and roles within said “clusters”):

Within these clusters fits some of the components described earlier, including two additional components: math/operations research (including things like algorithms and simulations) and business (including things like product development, management, and budgeting). The graphic below demonstrates the t-shaped-nature of data scientists — they have depth of expertise in one area and knowledge of other closely related areas. NOTE: ML is an acronym for machine learning.

 

NOTE: This post originally appeared on GCconnex.