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Methodology
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Data Quality controls can be implemented by adopting either a rule-based or a statistical / ML based approach. The following figure presents the alternatives:

.. image:: ../_static/img/dqf-controls-alternatives.png
   :width: 500
   :alt: Controls alternatives
   :align: center

.. raw:: html

    <p style="text-align: center;font-style: italic;font-size:12px">Approaches to implementing DQ Controls</p>


The specification and implementation of Data Quality controls involves various personas:
    * **Data Owners** who are accountable for the Datasets that are under their scope
    * **Data Stewards**, with potential help from **Data Quality analysts**, who are responsible for the identification and specification of controls
    * Technical stakeholders such as **R&D Teams**, **Data Engineers**, **Data Quality teams** who are responsible for the implementation of the controls

.. image:: ../_static/img/dqf-controls-sequence-diagram.png
   :width: 90%
   :alt: Controls sequence diagram
   :align: center

.. raw:: html

    <p style="text-align: center;font-style: italic;font-size:12px">Process to implement Data Quality Controls</p>


The following table presents the detailed steps involved in the specification, implementation and review of Data Quality controls:


.. image:: ../_static/img/dqf-controls-methodology.png
   :width: 90%
   :alt: Controls methodology
   :align: center


.. raw:: html

    <p style="text-align: center;font-style: italic;font-size:12px">Data Quality Controls activities</p>


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        <p style="font-weight: bolder;color: #3A8BFF;">(*)Data Quality Controls templates</p>
        <p>Please refer to the <a href="../dqf-resource_center/5-controls/dqf-resource_center-controls.html">Data Quality Framework documentation center</a>.

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