The data life cycle is a high-level overview of the processes involved in effective data management and preservation for use and reuse. Because the lessons learned and insights gained from one data project often inform the next, the data life cycle is sometimes portrayed as a loop. The process’s final phase feeds back into the first.
The life cycle is divided into eight stages, steps, or phases:
Data must be generated before a data life cycle can begin. Organisations, customers, and third parties all contribute to the data. Every transaction, purchase, conversation, and interaction generates data. This data frequently yields valuable insights, allowing you to better serve your clients and become more effective in your work. Otherwise, you won’t be able to start the next steps.
Not all of the data created on a daily basis is collected or utilised. Data teams must determine whether or not data is relevant to the project at hand. Forms, questionnaires, interviews, Direct Observation, and other methods can all be used to collect data.
Data must be processed after it has been collected.
- Data Wrangling, in which data is cleaned and changed from its raw form into something more accessible and usable,
- Data compression is the process of transforming data into a format that can be stored more efficiently.
- Data encryption is the process of converting data into another form of code in order to protect it from privacy concerns.
The most popular way to do this is to create databases or datasets. These datasets can then be saved on the cloud, on servers, hard drives, CDs, cassettes, and floppy disks, among other places. Backup the database, create a disaster recovery strategy, and keep security up to date.
It involves storing,organising,and retrieving data as needed. It’s a continuous process that runs from the start to the finish of a project. It covers everything from data storage and encryption to the implementation of access logs, audit logs, and change logs that track who has accessed data and made changes.
Data Analysis refers to processes that attempt to gather meaningful insights from raw data. Analysts and data scientists may use statistical modelling, algorithms, artificial intelligence, data mining, and machine learning.
Processes that aim to extract relevant insights from raw data are referred to as data analysis. Statistical modeling, algorithms, artificial intelligence, data mining, and machine learning are all tools that analysts and data scientists can utilize.
It provides a sense of analysis and visualisation.It may involve not only a description or explanation of data, but also a demonstration of the potential consequences.
Comments are closed, but trackbacks and pingbacks are open.