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This article was contributed by Bill Schmarzo, Dean of Big Data
What do you need to 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 corporations alone do not need this training. Data literacy should be taught in universities, high schools, middle schools and even in adult education and nursing homes.
In data literacy training everyone needs to be educated about how their personal data is collected, analyzed and used, so that organizations and people who understand how to manipulate your personal data for their benefit Don’t get caught up in their actions, behaviors and beliefs.
But what are the educational requirements that include an information literacy course? I have created that data to facilitate literacy education for my students Data Literacy Education Framework Provides a comprehensive overview of the data and analytical training needs – content areas – for everyone who becomes data literate.
Let’s explore each Data Literacy Education Framework subject areas as part of a two-part series on data literacy. Then using this framework, we can create an educational curriculum including testing to measure and subsequently increase everyone’s data literacy IQ (which I will have to cover in a future article કદાચ or maybe in a future book).
1. Data Awareness
I discussed its importance in “The Rising Importance of Data and AI Literacy – Part 1” Data Awareness And everyone needs to be aware of how organizations are collecting, analyzing and using their personal data.
Data Awareness Understands how organizations are capturing, analyzing and using your personal data (e.g., demographics, business transactions, financial holding, health and exercise, recreation, political and social data) To identify and codify individual behaviors and preferences. Influence your actions, behavior and beliefs.
While most of us know inwardly that organizations are capturing data about us, it is the “invisible data” (or “obscure data”) that is buried in the fine print of the end-user licensing agreement of that website or mobile application that most Puts in trouble.
One case is Google, which exploits or “monetizes” your data in the following ways:
- Google Ads. Allows businesses to target their products online based on your personal activity and interests. Google uses AI to profile customer behavior and take advantage of insights to target the right person with the right ad.
- Gmail. Google has also integrated some AI and ML algorithms to enhance the customer experience. An AI feature is the smart answer. Google AI analyzes the entire Gmail and proposes answers,
- Google Assistant. Based on your requests, this voice assistant can learn your interests – 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 a driver’s route, guess where they are going and guide them to their destination. It makes recommendations based on nearby restaurants, gas stations, etc. Based on your interests
- Google Photos. Google uses your photos to suggest images and videos that users can 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, personal web experiences, free online games, free navigational services and product and service discounts (in the case of loyalty programs). It’s just that users need to be aware that this is a “price” for “free” services, even if the price is not as clear as the monthly subscription fee.
What can you do to protect yourself? This data literacy framework can help you find the answer. The first step is awareness of where and how organizations are capturing and exploiting your personal data for their own monetization purposes. Be aware of what data you’re sharing through apps on your phone, the customer loyalty programs you belong to, and your engagement data on websites and social media. But nonetheless, there will be suspicious entities that will skip privacy laws to obtain your more personal data for their own nefarious acts (spam, phishing, identity theft, ransomware and more).
2. Decision Literacy
Whether we are aware of it or not, everyone makes a “model” for their own guidance DecisionsIn my blog “Making wise decisions in imperfect situations”, I discussed how humans naturally create Decision model To support their decisions, whether it’s a decision about which way to take them home from work, what to take from the grocery store, or how to pitch a power baseball heater like Mike Trout. And the broader nature of the decision model depends on the importance of the decision and the costs associated with the wrong decision.
- With high performance decisions like buying a home, buying a car or deciding where to go on vacation, we build a fairly comprehensive model by collecting and evaluating a variety of data to help make the “best” decision.
- Other decision models are less effective, so we use the “rules of merit” or heuristic decision model to support decisions such as changing the oil in your car every 3,000 miles, seeing a dentist every 6 months, or changing your underwear at least once a week. .
Decision Literacy There is an awareness of how humans model decision-making – using some very broad and other decision-making “thumb rules” – to help us make more informed, more accurate, more profitable and safer decisions.
When making a decision, it is all about how the person frames the decision. If you make up your mind in the process (that is, to prove or disprove the decision you have already made), you will be attracted to the data supporting your position and the reasons for ignoring the data running against you. Status If you have a vested interest in the outcome of a particular decision, your objective is at stake, and the results of your analysis are likely to be biased.
Also, the human brain is a weak decision making tool. Human judgment has evolved over millions of years of existence at Savannah. Humans have become very good at pattern identification and extrapolation: “A harmless log appears behind that patch of grass” to “Yum, it looks like an antelope!” “Hey, it’s actually a suede toothed tiger !!” Necessity has determined that we become very good at instinctual patterns and make quick, instinctive decisions based on those patterns.
To make matters worse, humans are luzi number crunchers (suppose we don’t have to crunch many numbers to find that suede-toothed tiger). As a result, humans have learned to rely on heuristics, gut feeling, rules of thumb, inferred information, and intuition as patterns of our judgment. But this decision model is inherently flawed and fails us in a world of very large, widely varied, high-velocity data sources.
Just need to visit Las Vegas to see our human decision making mistakes at work. Yes, casinos don’t make those grand monuments to human stupidity because they pay.
Data Literacy Beyond Forecast Literacy: A Structure
What do we need to do to increase our organization’s data literacy?
In this article, I have introduced the content areas of the Data Literacy Educational Framework, a framework that institutions, universities, high schools and even adult education can use to create a holistic data literacy curriculum. Then I delved deeper into the first two subject areas of the Data Literacy Educational Framework:
- Subject Area # 1: Data Awareness In which everyone talked about how they need to be aware of how their personal data is captured and how we think and use it to influence or use the decisions we make.
- Subject Area # 2: Decision Literacy In which it was discussed how humans create patterns of varying complexity to make more informed and accurate decisions.
In a world where your personal data, and the choices or prejudices buried in that data, are used to directly influence our behavior, beliefs, and decisions, we should all teach data literacy.
Otherwise, we assume that the earth is flat …
This article is part of a two-part series.
Bill Schmarzo is a writer, educator, researcher and influencer with a career spanning more than 30 years.
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