Data Literacy [DATALIT]

OVERVIEW

Get to know the power and pitfalls of data.

Clear insight into a data-driven world.

We are overloaded with data every day. One of the main challenges organisations face nowadays is a lack of data literacy. Do your employees have enough knowledge to read data properly, interpret it and turn it into valuable insights? This is crucial to be able to make the right decisions. Only then will your organisation be able to respond to the needs of your customers and opportunities in the market.

Are you curious enough?

Data literacy is the most important competence for people who work with data. It enables you to constantly look for new or existing data, question it and ask critical questions. It is therefore the skill that allows us to draw the right conclusions in today's data-driven reality. This will not only lead to faster decisions, but also to better results for your organisation.

From manipulation to information

In this training you will learn, on the basis of practical examples and exercises, not only about the power of data, but also about its pitfalls. You will get an answer to the question of how to read and interpret information correctly and which steps you should take to thoroughly analyse data. After completing this training Data Literacy, you will have several tools at your disposal to distinguish information from manipulation, based on your curiosity.

OBJECTIVES

In this training you will learn:

• How to get more value out of your data;
• interpret data correctly;
• recognise the pitfalls of data;
• Improve and maintain the quality of your data;
• the most important steps to take in order to analyse data in depth;
• convince others with data-based arguments.
  

AUDIENCE

This training is designed for anyone who wants to understand and analyse data more quickly and communicate effectively.

CONTENT

Block 1: Reading data

• Reading data presented to us in the media, company reports, political claims and advertisements
• How to distinguish information from manipulation
• Preventing misinterpretation

Practical assignment: applying the data checklist to a number of practical situations to promote our Data Literacy.

Block 2: Working with data

• Improving the quality of your data
• How do you ensure that you retain this quality?
• Discussing the most important data processing steps.
• Identifying the consequences of data processing

Practical assignment: estimating the consequences of the different processing steps on the results on the basis of a number of cases.

Block 3: Analysing data

• What are the most important steps to analyse data thoroughly?
• How do you prevent deception?
• What can we learn from the "scientific method" when it comes to data analysis?

Practical assignment: performing the most important analytical steps on a practical example.

Block 4: Arguing with and communicating data

• Being right is one thing, but being right is quite another.
• Transition and difference from analysis to explanation of data
• How do you convince others with data-based arguments?
• What can we learn from storytelling to better communicate our data?
• The 7 essential principles of good data storytelling

Practical assignment: Applying the 7 principles to a real-life example