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This article was contributed 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 behavior, 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 colleges, high schools, high schools, and even adult education and nursing homes.
Data literacy training should teach EVERYONE how their personal data is collected, analyzed and used, so as not to be misled into actions, behavior and beliefs by organizations and people who understand how to manipulate your personal data to their advantage.
But what are the educational requirements that a data literacy curriculum encompasses? To facilitate that data literacy education for my students, I created the data literacy education framework that provides a holistic summary of the data and analytic training requirements – subject areas – so that anyone can become data literate.
Figure 1: Framework for data literacy education
Let’s explore each of the subject areas of the data literacy education framework as part of a two-part series on data literacy. Then, using this framework, we can construct an educational curriculum, including tests to measure and then raise 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 and AI Literacy – Part 1” I discussed the importance of data awareness and how everyone should become aware of how organizations collect, analyze and use their personal data.
Data awareness is understanding how organizations record, analyze and use your personal data (e.g. demographic, commercial transactions, financial interests, health and exercise, entertainment, political and social data) to identify personal behaviors and preferences that can be used to determine your actions, behavior and influence beliefs.
While most of us intrinsically know that organizations record data about us, it’s the “invisible data” (or “obscured data”) buried in the fine print of that website or mobile app End User License Agreement that is most troubling.
Figure 2: Your personal data captured by third-party data aggregators
A good example of this is Google, which misuses or “money” your data in the following ways:
Google Ads. It allows companies to target their products online based on your personal activities and interests. Google uses AI to profile customer behavior and use the insights to target the right person with the right ad. Gmail. Google has also integrated several AI and ML algorithms to improve the customer experience. An AI feature is the smart answer. Google AI analyzes the entire Gmail and proposes a response. 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 he is going and guide him to his destination. It provides recommendations based on nearby restaurants, gas stations, etc. based on your interests. Google Photos. Google uses AI mining of your photos to suggest images and videos for users to share with their friends and family.
Many organizations, such as Google, offer “value” in exchange for your personal data, such as free email, free social media platforms, personalized web experiences, free online games, free navigation services, and product and service discounts (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 isn’t 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 an awareness of where and how organizations capture and exploit your personal data for their own monetization purposes. Be aware of what data you share through the apps on your phone, the customer loyalty programs you belong to, and your engagement data on websites and social media. But even then, there will be dubious organizations that circumvent privacy laws to capture more of your personal information for their own nefarious acts (spam, phishing, identity theft, ransomware, and more).
Whether we are aware of it or not, everyone creates a “model” to guide their decisions. In my blog “Making Informed Decisions in Imperfect Situations” I discussed how people naturally create decision models to support their decisions, whether it’s decisions about the route home from work, what they get at the grocery store, or how they pitch for a power baseball batter like Mike Trout. And the comprehensive nature of the decision model depends on the importance of the decision and the costs associated with making the wrong decision.
With a major decision making, such as buying a house, buying a car or deciding where to go on vacation, we build fairly comprehensive models by collecting and reviewing a wide variety of data to help make a decision. “optimal” decision.Other Decision models have less impact, so we use rules of thumb or heuristic decision models to support decisions such as changing the oil in your car every 3,000 miles, a visit to the dentist every 6 months or changing your underwear at least once. at least once a week.Figure 3: Informed decision-making framework
Decision literacy is an awareness of how people make decision models – some very elaborate and others use “rules of thumb” depending on the cost of the wrong decision – to help us make more informed, more accurate, more profitable and safer decisions.
When making decisions, the way one formulates the decision is everything. If you come into this process with your decision already made (that is, to prove or validate a decision you’ve already made), you’ll be drawn to data that supports your point of view and make up reasons for using the data. ignore those that conflict with your position. If you have a vested interest in a particular outcome of a decision, your objectivity is threatened and the results of your analysis are likely to be biased.
Also, the human brain is a poor tool for making decisions. Human decision-making evolved from millions of years of survival on the savanna. People got really good at pattern recognition and extrapolation: from “That looks like a harmless log behind that patch of grass,” to “Yum, that looks like an antelope!” to “YIKES, that’s actually a saber-toothed tiger!!” Necessity dictated that we became very good at recognizing patterns and making quick, instinctive survival decisions based on those patterns.
To make matters worse, humans are crappy number crackers (I guess we didn’t have to crack a lot of numbers to spot that saber-toothed tiger). Consequently, people have learned to rely on heuristics, gut feeling, rules of thumb, anecdotal information, and intuition as our decision models. But these decision models are inherently flawed and let us down in a world of very large, highly varied, high-speed data sources.
You only need to visit Las Vegas to see our human flaws in decision-making at work. Yes, casinos don’t build those beautiful monuments to human stupidity because they give away money.
Figure 4: Human Decision-Making Errors
Beyond data literacy to prediction literacy: a framework
What should we do to increase our organization’s data literacy?
In this article, I’ve introduced the subject 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 educational curriculum. After that, I delved deeply into the first two fields of the Education Framework Data Literacy:
Topic #1: Data Awareness which talked about how everyone should be aware of how their personal data is captured and used to influence or manipulate how we think and the decisions we make. 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 behavior, beliefs and decisions, we need to educate EVERYONE about data literacy.
Otherwise, we could be persuaded to believe that the Earth is flat…
This article is part one of a two part series.
Bill Schmarzo is an author, educator, innovator and influencer with a career spanning more than 30 years.
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This post Data literacy: what it is and why it matters
was original published at “https://venturebeat.com/2022/03/12/data-literacy-what-it-is-and-why-it-matters/”