The use of AI and data is too widespread for a small number of specialists to handle it alone. Every member of an organization is responsible for the safe and productive use of data in workflows and decision-making. To achieve this, they need a certain level of data literacy.
Data mastery is a person’s ability to derive meaning from information collected from internal or external sources. Data scientists and analysts are the most skilled in this field. Less technically proficient users need to understand the data they are viewing and how to use it effectively. Everyone needs to understand data governance policies to properly secure and protect company data.
Bill Schmarzo opens his book, AI and data literacy: empowering citizens in data science, with a focus on why every person needs AI and data literacy education.
“We must prepare everyone to become data science citizens and understand where and how AI can transform our personal and professional lives by reinventing industries, businesses and societal practices to foster a better quality of life for everyone,” Schmarzo wrote.
One of the most difficult aspects of data literacy is measuring your current level and what skills need to be improved. Schmarzo encourages readers to actively evaluate themselves using six criteria while reading the book.
He created the AI and data mastery framework. It contains the six elements that citizen data scientists need to succeed:
Click on the book coverto find out more.
- Data and privacy awareness.
- AI and analytical techniques.
- Make informed decisions.
- Prediction and statistics.
- Value engineering skills.
- Ethics.
Each category has key concepts that everyone needs to use data and AI responsibly and ethically. Schmarzo provides a chart in Chapter 1, shown here, to help you measure your data literacy levels.
| Category | Weak | AVERAGE | High |
| Data and Privacy Awareness | Simply click and agree to the terms and conditions of the website and mobile app without reading them | Attempt to determine the credibility of the site or application before agreeing to the terms and downloading | Read the privacy terms and conditions of websites and mobile apps and validate the credibility of the app and site before committing. |
| Informed decision making | Rely on their favorite TV channel, celebrities, or website to tell them what to think; prone to conspiracy theories | Research the issues before making a decision, although it always trumps the opinions of people who “think like me” | Create a model that takes false positives and false negatives into account before making a decision; practice critical thinking |
| AI and analytical techniques | Believing that AI only applies to large organizations and three-letter government agencies | Understand how AI is part of a spectrum of analytics, but not be sure what each analytical technique can do | Understand how to collaborate to identify KPIs and metrics across a wide variety of value dimensions that make up the AI utility function. |
| Predictions and statistics | Don’t try to understand the probabilities of events happening; blind to unintended consequences of decisions | Consider probabilities when making decisions, but do a careful assessment of potential unintended consequences. | Actively seek information from credible sources to improve the chances of making an informed decision |
| Value creation | I don’t understand the dimensions of “value” | Understand the dimensions of value, but have not identified the KPIs and metrics against which value creation effectiveness is measured | Understand the dimensions of value and have identified the KPIs and metrics against which value creation effectiveness is measured |
| Ethics | Thinking that ethics only applies to “others” | Recognize the importance of ethics, but don’t know how best to address them | Proactively consider different perspectives to ensure ethical decisions and actions |
Data literacy is not just an individual endeavor. Organizations should take the initiative to assess literacy levels from top to bottom, identify what needs to change, and provide resources to help their employees achieve the the training they need.
Discover the extract from chapter 1 of AI and data literacy: empowering citizens in data science to learn more about AI and data literacy assessment, its importance and challenges. Visit the Packt Publishing press page to learn more about the book.
Editor’s note: The TechTarget editors thank author Bill Schmarzo for his insights on the topic and contributions to this coverage.
Peter Spotts is the Site Editor for SearchBusinessAnalytics and SearchDataManagement, writing and managing content for both sites. Before joining TechTarget in July 2021, Peter worked for Turley Publications in Palmer, Massachusetts, as a writer and editor covering local events and news for six years in western Massachusetts.
