The Data Journey: What you need to know for successful navigation - Transcript
(The Statistics Canada symbol and Canada wordmark appear on screen with the title: "The Data Journey: What you need to know for successful navigation")
Data 101: Data Journey
The training videos in this series are organized around a data journey. This video tells you what you need to know for successful navigation.
In this video you will learn about the steps and activities in the data journey as well as the foundation supporting it. No previous knowledge is required.
Steps of a data journey
(Diagram of the Steps of the data journey: Step 1 - Find, gather, protect; Step 2 - explore, clean, describe; Step 3 - analyze, model; Step 4 - tell the story. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)
The data journey represents the key stages of the data process. The journey is not necessarily linear, it's intended to represent the different steps in activities that could be undertaken to produce meaningful information from data.
Not everyone who uses data will do all these steps. For example, you might already have gathered and clean data ready for analysis. Therefore you might only need to do the last two steps.
Step 1: Find, gather and protect
(Diagram of the Steps of the data journey with an emphasis on Step 1 - Find, gather, protect.)
(Text on screen: Showing relationship between two things)
The first step is to get some data. Maybe the data is already there for you, or maybe you have to figure out what data you need and then where to find it. Once you get the data, it's also important to have security measures in place to protect the integrity of the data. In this first step you will use one or more of the following competences, data discovery, data gathering and/or data management and organization.
Step 2: Explore, clean and describe
(Diagram of the Steps of the data journey with an emphasis on Step 2 - explore, clean, describe.)
Once you have the data, the next thing is to get to know it. If the data is flowing normally, then you might already know what to expect. On the other hand, if the data is new to you, then you should spend some time exploring the formats, variables, looking for errors and missing values. It may be necessary to clean the data before using it for analysis. It's important to document what you found and what you did to clean the data.
The product at the end of this step is data ready for analysis. In this step you will use one or more of the following competencies: data cleaning and/or data exploration.
Step 3: Analyse and model
(Diagram of the Steps of the data journey with an emphasis on Step 3 - analyze, model.)
If you are doing analysis to describe a phenomenon, draw conclusions about a population or make predictions about future events than your data journey continues. The purpose of doing analysis and modeling is to use statistical techniques to turn the data into information to provide meaningful insights. In this step you will use one or more of the following competencies: data analysis, data modeling and/or evaluating decisions based on data.
Step 4: Tell the story
(Diagram of the Steps of the data journey with an emphasis on Step 4 - Tell the story.)
The statistical information that comes from analysis and modeling is easier to digest if it's presented in some sort of story, it could be a research paper, an infographic, an article for the media, or some combination of these and other data presentation methods. In this step you will use one or more of the following competencies: data interpretation, data visualization and or story telling.
Build your data journey on a solid foundation
(Diagram of the Steps of the data journey. The data journey is supported by a foundation of stewardship, metadata, standards and quality.)
In order to successfully follow the steps of the data journey, it's essential to build your work on a solid foundation of stewardship, metadata, standards, and quality.
Stewardship encompasses all activities to govern, safeguard and protect data. Metadata should describe all the processing and manipulation that the data has undergone. Standard methods, practices and classifications should be applied throughout. Quality should be proactively managed throughout the process and relevant quality indicators should accompany all deliverables.
Recap of key points
The data journey steps are find, gather, protect; explorer, clean, describe; analyze and model; and tell the story. Not everyone who uses data will do all these steps themselves. For example, you might get already gathered in clean data ready for analysis. The data journey is supported throughout by a foundation of stewardship, metadata, standards, and quality.
You're welcome to watch the videos in any order you choose. If you're not sure where to go next, we recommend types of data and gather data.
(The Canada Wordmark appears.)