Data Governance

data-governance

Governing your Data, Governing your Business

Transforming a company into a data-driven company is no easy task. One needs to have a very clear strategy at an executive level that allows Big Data use cases to be prioritised and respond to business needs. Furthermore, a strict definition of policies and rules governing the use of data are needed. This is called data governance.

Data governance establishes a framework of reference, which is needed to maximise the value of any information available across the board throughout the organisation, by defining policies, procedures and roles that facilitate effective management of the data life cycle.

At Synergic Partners, we offer solutions to our customers that guarantee data integrity and data management efficiency:

  • · Data procurement and operation procedures for users at the company with access to data.
  • · Advice on technology tools that enable information to be centralised and various types of access and users to be defined.
  • · Definition of the data manager and user hierarchy, with specific roles and duties.

Furthermore, at Synergic Partners, we offer a response to the four disciplines that underpin data governance: Security, Data Quality, Metadata and Data Life Cycle.

Data security is one of the most important aspects to consider when processing information. This ensures authorised access to the Big Data platform and the information it contains, preventing unauthorised access.

Data quality is defined as the degree to which a data set meets a series of characteristics, such as completeness, validity, accuracy, uniqueness, availability and consistency. In Big Data Governance, data quality verifies the information in the data as from the moment they are procured.

The administration of metadata gives context to the data, as well as helping to know its meaning. It enables the coherence of data used by various groups to be maintained, optimising storage criteria and facilitating searches. Metadata are data about data. For example, the description of a field, how it has been calculated, how many entries a table contains or when it was last populated.

The data life cycle corresponds to the flow of usefulness that data should have once contained in the data lake and processed. It relates to the time during which we use the data for a certain purpose. The goal is to avoid saturating IT applications with data that are no longer useful or have become irrelevant, as well as to prevent and control the publication of sensitive data in development and/or production environments.

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