Data Literacy is the ability to read, work with, analyze, and argue with data. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data.
Both workers and organizations need to become data literate. A meager 24% of the global workforce is fully confident in their ability to read, work with, analyze and argue with data.
To compound the challenge, many organizations have so much data they do not know how to use it and are not sure when not to use selected data.
The Data Literate Organization
Until an organization becomes data literate it cannot be data-driven. The data literate organization (see Figure 1) has many traits including:
- Has invested into enterprise data governance
- Has enterprise data that is secure, accurate, timely, consistent, authoritative, reasonable, well defined, accurate syntax, and appropriately formatted
- Has invested into world-class metadata management
- Has built a sound data science team
- Has enabled data-driven insights though advanced analytics, AI/ML (artificial intelligence and machine learning), business intelligence, and visualizations
- Has decision makers who understand how to access data sources, work with data, interpret data and avoid data analysis errors
- Has an executive level Chief Data Officer (CDO) with a significant team, authority, and budget
- Is adhering to or exceeding their industry’s data privacy and security requirements
- Has defined and implemented its data ethics
Data Literate Thinking
Most organizations erroneously believe that data literacy is nothing more than a training class that they record and ask their employees to view, whenever they have some spare time. This approach fails to have any quantitative component and is ineffective at best. A properly built data literacy program needs to be automated, quantitatively measure its own performance and be scalable to the enterprise. The data literate program must target the development of the six types of data literate thinking:
- Critical Thinking
- Scientific Thinking
- Statistical Thinking
- Visual Thinking
- Skepticism
- Ethical Thinking
Critical Thinking
Critical thinking is being open to different assumptions or conclusions; to be willing to adopt new ideas and perspectives on a subject.
Scientific Thinking
Scientific Thinking is the knowledge of the scientific method, scientific principles, and the ability to apply a scientific approach to formulate and test hypotheses.
Statistical Thinking
Statistical Thinking is understanding how statistics work and how to apply them and to apply quantitative reasoning to problems.
Visual Thinking
Visual Thinking is the ability to understand and interpret information conveyed visually, through graphs, charts, and other means.
Skepticism
Skepticism is the willingness to question the data, to go beneath the surface, to understand the context in which data is created and presented.
Ethical Thinking
Ethical Thinking is understanding the potential for good or for harm of any actions or conclusions from data; to actively prevent harm.
The combination of these forms of thinking will be unique to each organization but all data literate organizations have all these forms – and they all operate in concert to enable the effective use and management of data and information.
Conclusion
Data literacy is an evolutionary journey and not a short-term project. Building a world-class data literacy program is vital to any organization that values its data and endeavors to become data-driven.
