1. Data life cycle
Welcome back. Let's have a look at the data life cycle, and why it is important.
2. What is the data life cycle?
The data life cycle is a framework to regulate data from its collection to its use, analysis, and disposal.
We will look into each step in more detail, but at a high level the framework starts with the planning and creation or collection of data.
Next is data storage and management, preferably in a secure and organized manner, typically using databases or data warehouses.
This so-called raw data often needs cleaning and processing to eliminate errors and inconsistencies and improve its quality and usefulness.
Cleaned data can then be analyzed and visualized, to extract insights and answer questions.
In order to effectively communicate findings with your stakeholders, the results of the data analysis are shared with others.
The final stage of the framework depends on the initial plan. Does the data need to be stored for future use, or can it safely be destroyed to ensure data privacy?
3. Why is the data life cycle important?
The data life cycle is important because it can help companies to ensure data is regulated in a responsible manner. By understanding the data life cycle, organizations can also identify potential areas for improvement in their data management practices, which can help to improve the efficiency and effectiveness of their operations.
By following the stages of the data life cycle, organizations and researchers can ensure that they are properly handling and leveraging the data they collect and generate. Let's look at each step in detail.
4. Plan and collect
During the planning stage, a (business) question should be prepared that answers the need of your stakeholders. This will affect other stages of the data life cycle, since you'll decide on the type of required data, how it will be managed throughout its life cycle, who will be responsible for it, and how to achieve the most effective results.
Whether you'll need to collect or create data from various sources, such as surveys, experiments, or sensor readings, will also be determined at this stage.
5. Store and manage
The collected data needs to be stored. This ensures that the data is easily accessible to the right person and that it can be properly managed over time. Additional concerns around how to handle PII or other sensitive data types should be addressed here.
6. Clean and process
Before proper data analysis can start, the data should be cleaned and processed. This may include formatting data, dealing with missing values or errors, or transforming data into a more usable form. Cleaning and processing the data often represents a large portion of effort in the entire data life cycle.
7. Analyze and visualize
Once data is cleaned properly, you can perform analyses. Data analysis refers to the process that attempts to get new meaningful insights from raw data. Visualizing these insights effectively makes it easier to interpret them.
Various methods are used to analyze and visualize data. They may involve statistical methods or machine learning algorithms, using various programming languages or software tools. DataCamp offers many courses on both of these topics.
8. Share
Doing an insightful analysis that is not used by someone else has basically no value. Successfully communicating your results is a vital but often overlooked step in the data life cycle. Examples of sharing insights are publishing dashboards, reports, or papers, presenting findings at conferences, or making data sets available to other colleagues or researchers.
9. Archive or destroy
Once you've gained and shared the required insights or answered the initial (business) question, the next and final step is to decide whether the data should be archived or destroyed.
Data archiving may involve backing up the data, maintaining proper documentation, or applying digital preservation techniques to preserve the data in a usable format.
In the rare case when destroying the data is critical for protecting private information from any accidental loss for example, a permanent deletion of the data is an option. Deletion of data might also free up resources.
10. Let's practice!
Now that you are familiar with the stages in the data life cycle, let's do some exercises. After that, we'll talk about potential issues that can arise.