The Data Literacy Guide
Get a complete introduction to data literacy.
Get a complete introduction to data literacy.
Data literacy is the ability to read, work with, analyze, and argue with data.
Reading data is understanding what data is and what it represents. Examples include understanding a chart, reading a report, or navigating a dashboard.
When you are creating, acquiring, cleaning, or managing data, you are working with data. Analyzing data means that you are filtering, aggregating, or comparing data, or analyzing it in other ways.
Finally, you can argue with data through visualizations or storytelling - it includes everything you do to communicate your message to an audience.
Data literacy goes beyond AI, machine learning, and data science. Data literacy is about any professional using data to drive better decisions. For example, an Assortment Manager at a supermarket who decides about what products to add or remove from the assortment. Or an Operations Manager at a micro mobility company who needs to decide whether to outsource or inhouse e-scooter maintenance operations.
Where traditional organizations rely on HiPPOs (“highest paid person’s opinion”, a term invented by Avinash Kaushik) or simply intuition, modern organizations are data-driven. A great example is Booking.com, where a combination of diagnostic analysis, hypotheses and experimentation has led to 2-3x higher conversion rates than the industry average.
Highly data-driven organizations are three times more likely to report significant improvements in decision-making compared to those who rely less on data. In turn, quality and speed of decision making are strongly associated with overall company performance.
Not only do organizations need to change their culture and adopt data literacy skills to become data-driven, they also need to have a modern data stack so anyone in the organization can use data for decision-making. Analytical tools like Looker, Power BI and Tableau can be used without coding for creating and consuming dashboards, reports and charts.
Data preparation, a task performed to turn raw data into value, on the other hand often still requires manual programming.
The data pipeline platform DataCater offers a first-in-class approach to building data pipelines without coding, allowing entire organizations to participate in preparing data for downstream use cases, like reports, dashboards, or charts. Due to its full preview support for Python functions, non-coding domain experts can benefit from custom code shared by engineers, which bridges the gap between business and engineering and allows modern organizations to fully embrace data literacy.
