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Data Governance Services
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What is Data Governance?
Data governance is the definition of the company authority principles in relation to data and its alignment with business value. It is directly related to the data integration and data processing approaches that a company follows, from the moment that data enters the data storage system to when they go through transformation logic, data quality checks and to when they become available for downstream consumption through an accessible data catalog and secure channels . Companies use data governance in order to ensure that data which are securely distributed to their consumers will enable prompt positive value delivery for the organisation.
How Companies Benefit From Data Governance
Businesses need data governance so that they can efficiently leverage the power data give them for accurate business intelligence reports, while remaining compliant with various data regulations, i.e. most notably GDPR. At the same time, having implemented strong data access policies across the data storage mediums, with state of the art methodologies is an invaluable part of data governance strategy since it ensures robustness in the data system.
If not enough emphasis is given on securing the access to data, a data breach can happen which means businesses data can be exposed. Having a strong data governance in place will mitigate this risk, since only named owners will be able to access data such that value can be delivered. Also, not having a clear data architecture across the whole of the business can lead to data being all over the place. This can lead to a lot of complexity in processing the data which in turn will make taking value out of data nearly impossible, or worse produce conclusions with incorrect data.
In order to have a resilient data governance strategy, all of the parts of data governance should be addressed. A specialist with experience in the area should be consulted and optionally third party tools can be utilised which help in the proper implementation of data governance. Since data governance will overlap with data integration, extra attention should also be given to storing data securely with strong encryption algorithms, implementation of authentication and authorisation policies, ensuring data is clean before entering the data warehouse and introducing a concise data catalog which describes what data are available and their meaning to consumers such as data analysts or machine learning engineers.
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