Data Literacy: What It Is and Why It Matters

This article was written by Bill Schmarzo, the Dean of Big Data

What should you do to increase your organization’s data literacy?

In a world where your personal data (and the preferences and biases buried in that data) are used to influence your behaviors, beliefs, and decisions, data literacy is a fundamental and indispensable skill. And it’s not just companies that need this training. Data Literacy should be taught in universities, high schools, colleges, and even adult training centers and nursing homes.

Data literacy training must educate EVERYONE about how their personal data is collected, analyzed and used, so that they are not fooled by actions, behaviors and beliefs by organizations and people who understand how manipulate your personal data to their advantage.

But what are the educational requirements that make up a data literacy program? To facilitate this data literacy education for my students, I created the Data Literacy Education Framework which provides a holistic synopsis of the data and analytics training requirements – thematic areas – for everyone to become data literate.

Let’s explore each of the domains of the Data Literacy Education Framework in a two-part series on Data Literacy. Then, using this framework, we can build an educational program that includes tests to measure and then increase everyone’s Data Literacy IQ (which I’ll have to cover in a future article…or maybe a future book).

1. Data Awareness

In “The growing importance of data literacy and AI – Part 1I discussed the importance of data knowledge and how everyone should be aware of how organizations collect, analyze and use their personal data.

Data awareness understands how organizations capture, analyze, and use your personal data (e.g., demographics, business transactions, financial holdings, health and exercise, entertainment, political and social data) to identify and codify behaviors and personal preferences that can be used to influence your actions, behaviors and beliefs.

Although most of us know inherently that organizations capture data about us, it is the “invisible data” (or “obfuscated data”) that is buried in the fine print of this website or this mobile application end-user license that are most troubling.

This is the case of Google, which uses or “monetizes” your data in the following way:

• Google Ads. Allows companies to target their online products based on your personal activity and interests. Google uses AI to profile customer behaviors and leverage the insights to target the right person with the right ad.
• Gmail. Google has also integrated several AI and ML algorithms to improve customer experience. A feature of AI is intelligent response. Google AI scans entire Gmail and offers an answer.
• Google Assistant. Based on your requests, this voice assistant can learn your interests to search for anything – music playlists, restaurants, best beaches or hotels – and make product and service recommendations based on your interests.
• Google Maps. Google Maps uses AI to track the driver’s route, estimate where they are heading, and guide them to their destination. It offers recommendations based on nearby restaurants, gas stations, and more. according to your interests.
• Google Photos. Google uses AI to mine your photos to suggest images and videos that users can share with friends and family.

Many organizations, like Google, offer “value” in exchange for your personal data, such as free email, free social media platforms, personalized web experiences, free online games, free browsing services and discounts on products and services (in the case of loyalty programs). It’s just that users should be aware that there is a “price” for these “free” services, even if the price is not as obvious as a monthly subscription.

What can you do to protect yourself? This data literacy framework can help you find the answer. The first step is to know where and how organizations capture and use your personal data for their own monetization purposes. Be aware of the data you share through apps on your phone, the loyalty programs you belong to, and your engagement data on websites and social media. But even then, there will be dubious organizations that will circumvent privacy laws to capture more of your personal data for their own nefarious acts (spam, phishing, identity theft, ransomware, etc.).

2. Decision literacy

Whether we realize it or not, everyone creates a “pattern” to guide their the decisions. In my blog “Make informed decisions in imperfect situations“, I discussed how humans naturally create decision models to support their decisions, whether it’s deciding which route to take home from work, what to buy at the grocery store, or how to present to a baseball powerhouse hitter like Mike Trout. And the overall nature of the decision model depends on the importance of the decision and the costs associated with making a bad decision.

• With a high-impact decision like buying a house, buying a car, or deciding where to go on vacation, we build quite extensive models by gathering and evaluating a wide variety of data to help make an “optimal” decision.
• Other decision models have less impact, so we use “rules of thumb” or heuristic decision models to support decisions like changing your car’s oil every 3,000 miles, seeing a dentist every 6 months or change your underwear at least once a week. .

Decision literacy is an awareness of how humans build decision models – some very comprehensive and others using “rules of thumb” based on the cost of making the wrong decision – to help us make more informed, more accurate, more cost effective and safer.

When making decisions, the way you frame the decision is essential. If you enter this process with your decision already made (i.e. to prove or validate a decision you have already made), then you will gravitate towards data that supports your position and fabricate reasons to ignore the data that goes against your position. If you have a vested interest in a certain decision outcome, your objectivity is at risk and the results of your analysis are likely to be biased.

Moreover, the human brain is a poor decision-making tool. Human decision making has evolved from millions of years of survival in the savannah. Humans have become very good at pattern recognition and extrapolation: from “It looks like a harmless log behind that patch of grass” to “Yum, it looks like an antelope!” to “YIKES, it’s actually a saber-toothed tiger!!” Necessity dictated that we got very good at recognizing patterns and making quick, instinctive survival decisions based on those patterns.

To make matters worse, humans are bad number crunchers (I guess we didn’t need to calculate a lot of numbers to spot that saber-toothed tiger). Therefore, humans have learned to rely on heuristics, intuition, rules of thumb, anecdotal information, and intuition as decision models. But these decision models are inherently flawed and fail us in a world of very large, very varied, and high-velocity data sources.

One need only visit Las Vegas to see our flaws in human decision-making at work. Yes, casinos don’t build these magnificent monuments to human stupidity because they give away money.

Beyond Data Literacy to Prediction Literacy: A Framework

What do we need to do to increase our organization’s data literacy?

In this article, I introduced the thematic areas of the Data Literacy Educational Framework, a framework that organizations, universities, high schools, and even adult education can use to create a holistic data literacy curriculum. . Next, I delved into the first two areas of the Data Literacy Educational Framework:

• Domain #1: Data Awareness which talked about the need for everyone to know how their personal data is captured and used to influence or manipulate the way we think and the decisions we make.
• Domain 2: Knowledge of the decision who discussed how humans create models of varying complexity to make more informed and accurate decisions.

In a world where your personal data and the preferences or biases buried in that data are used to directly influence our behaviors, beliefs, and decisions, we need to teach EVERYONE data literacy.

Otherwise, we might be persuaded to believe that the earth is flat…

This article is part of a two-part series.

Bill Schmarzo is an author, educator, innovator and influencer with a career that spans over 30 years.