12 Data Science for Librarians Although it is possible that specifics vary, data management specialists usually identify at least six stages in the data life cycle. Keeping that in mind, here is one example: 1. Data Generation or Capture: During this initial phase, data comes into a company or organization, often via data entry, signal reception such as transmitted sensor data, or acquisition from a secure external source. 2. Maintenance: Data is processed before its use in this phase. It is worth mentioning that the data might be subjected to various pro cesses like integration, scrubbing, as well as extract-transform-load (ETL). 3. Active Data Use: During this phase, organizations use data to support their objectives, goals, and operations. 4. Publication: In this important phase, data is not necessarily made public, but it is sent outside the organization. Keep in mind that publication is not necessarily a component of the life cycle for a spe- cific data unit. 5. Archiving: Data is removed, in this phase, from active production environments. It is no longer used, processed, or published but is stored in case the data is needed again in the future. 6. Purging: Every copy of the data is removed or deleted in this phase. In most cases, this is done for data that is already archived. Since the explosion of both Big Data and the continuous development of the Internet of Things, data life cycle management has become increasingly important in many organizations. Enormous data volumes are being pro- duced by an increasing number of devices, such as smartphones and tablets, all over the world. It is worth mentioning that proper data oversight throughout its entire life cycle is vital to optimizing its value and usefulness while minimizing the risk for errors. Finally, deleting or archiving data at the end of the use- ful life helps ensure that it doesn’t consume more resources than necessary. The data life cycle often serves as a great navigation tool and facilitates users in coming up with recommendations and suggestions on how to effec- tively and efficiently work with data across the various stages in the data life cycle. Era of Big Data Society is transitioning from the information age into the era of Big Data, where the discipline of data science is now taking center stage. Data librarianship in academic libraries has its origins in various social sciences. This is especially true in the case of the creation of data services as well as data archives in the United States and Canada (Data Library Ser vices) and the United Kingdom (Data Archives Services). In this digital age characterized by Big Data and artificial intelligence, many of the competencies and skills that academic librarians develop in order to perform “core” services could directly serve both the research life cycle as well as the work flow (Gessner et al., 2017). Competencies like digesting