Six data verification, organisation, transformation, integration, and extraction

Six phase data life cycle models: Five data life cycle models with
six phases were identified; (i) United States Geological Survey (USGS) Science
Data Lifecycle Model, (ii) University of Virginia, Steps in the Data Life Cycle,
(iii) International Leader in Data Stewardship (ICPSR) Data life cycle,  (iv) UCSD Libraries Data life Cycle, and (v)Generic
Life Cycle Model.

According to Faundeen and Hutchison 48 data life cycle models are fundamental to
communication and data management and ensures adequate long-term preservation
and accessibility. After reviewing more than 50 data life cycle models the
later four above inclusive they came to the conclusion that none of the
existing models was entirely consistent with the USGS data management
requirements. It was imperative to USGS that their
functional processes and workflows were adequately capture in a model. Furthermore, like other organisations, the USGS developed
its own data management life cycle with the aim of reducing complexity and
removing redundant or irrelevant steps or phases that were not in sync with
their scientific workflows and processes. The USGS opted for a linear and
easily operated illustration their new model. The model 10,48 included the basic classical
data life cycle phases and laid emphasis on three parallel phases; metadata,
quality management, backup & Security.

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      i.       
Plan: The organisation should identify the resources, methods,
techniques, functional and technical system requirements and generate both
plans for either data acquisition, data entry or signal reception and data
management.

    ii.       
Acquire: This is the data capture phase which can either data
acquisition, data entry, signal reception or all three activities combined.

   iii.       
Process: Raw as well as derived data verification, organisation,
transformation, integration, and extraction in appropriate format takes place
in this step.

   iv.       
Analyze: This encompasses demonstrable quality requirements
fulfilment, data analytics, modelling and evaluation test results as well as
methods and activities carried out to facilitate definitions of facts,
identification of forms and trends, developing interpretations and testing
hypotheses.

    v.       
Preserve: Data storage for Long term access and reuse. The
purpose of this phase is the guarantee long-term preservation, ease of search
and retrieval, accessibility and usability of the data. This step employs
multi-copy/storage locations, long-term usefulness, accuracy and consistency,
information security, metadata and file formats.