1. Course Overview


This lesson introduces the course and outlines what will be covered in the coming lessons.

To explore more Kubicle data literacy subjects, please refer to our full library.


  1. Data in Organizations (00:13)

    Data can bring value to modern companies, but there can be a disconnect between people in an organization who understand data and people who don’t. Data analysts may have good technical knowledge, but might not understand the company as a whole. Non-data people may understand how the whole company works, but may struggle to get analysts to generate insights that meet their needs.

  2. Course Outline (01:53)

    This course aims to bridge the gap between people who understand data and those who don’t, by introducing non-technical principles on how to think and communicate with data. It contains three main sections.

    1. Data in Organizations

    2. Example of a Data Project

    3. Good and Bad Practice With Data

  3. Case Study (02:17)

    In this course, we study a technology company called OneWall. They’re aiming to use their app usage data to decide on what features they should add in the future.


Over the past few decades, companies have continued to collect and analyze increasingly large amounts of data in all areas of their businesses. They can use this data to make decisions based on clear, factual criteria, instead of human criteria, which can be subject to errors and biases, among other failings. While many people can understand these principles, this doesn't mean everyone understands how to harness their data for the benefit of the company. In fact, there's often a significant disconnect between people in the organization who get data, and people who don't.

Each group struggles to understand the other's perspective on how to create value from data. In this course, we'll aim to bridge this gap by introducing some fundamental, non-technical principles surrounding how to think about data, and communicate using data.

As a result, this course should be useful for people in either of these two groups. Let's consider these two groups in a bit more detail. In one camp, we have a company's data analysts. These people probably have a lot of technical knowledge. Depending on their area of expertise, they might know a lot about self-service business intelligence or programming languages, but they might be less familiar with the company's overall performance. They may not be as proficient in communicating their data analysis to non-technical people, or in understanding where their work fits in the big picture. In the other camp, we have non-data people. Perhaps these are managers, or people who work in areas without intensive data work. These people have a wide understanding of the company as a whole, but probably aren't experts in data analytics techniques. They may struggle to understand how to get the analysts to produce insights that meet their needs. They may also struggle to convert the results produced by their analysts into decisions that help improve the business. We'll cover three main areas in this course. In the first few lessons, we'll look at what we really mean by data, and how it fits into the information contained in an organization. Next, we'll learn about the various, non-technical issues that you should consider in the course of a data analysis project. Finally, we'll spend a few lessons considering some examples of good and bad practices when using data and visualizations. Before we finish this lesson, let's introduce the company that we'll be looking at throughout the course.

OneWall provides an online application where clients can manage their various social media accounts. Users can post to multiple accounts from one source, and read comments and opinions from customers in a single place. OneWall is currently deciding what features to add to their application in the coming months. To do this, they need to analyze the popularity of current features, and consider client needs that are not currently being met. Throughout this course, we'll see how they can successfully use their app usage data to decide what features to add. In the next two lessons, we'll look at two frameworks that show us where data fits in an organization's performance.

We'll start with the information value chain in the next lesson.