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Data governance is the management of a variety of different aspects of data outside of the core storage and management of data, including availability, security, use, and trustworthiness. Effective data governance tries to strike a balance between preventing misuse and maintaining regulatory compliance with democratizing data.
While many companies have embraced data governance processes for years, it has taken on a much bigger role in the past 15 years. As many organizations expanded the volumes and types of data they captured, governments instituted regulatory controls around data to protect consumers and the increased threats of breaches or misuse created financial risks to these organizations.
The role of data governance has grown over time, moving beyond controls to helping organizations make more effective use of data. For example, while understanding how certain datasets are used helps prevent misuse, it also helps other teams envision other related ways to effectively use and gain greater value from the data.
In the early days of data governance, most data was centralized putting governance in the domain of the core IT and data management teams. As regulatory controls increased, some organizations made governance the responsibility of the Head of Risk or Chief Risk Officer (CRO). More recently, as the role of the Chief Data Officer (CDO) has expanded, the CDO often owns or shares responsibility for data governance.
Most well-designed governance programs are steered by a committee that combines technical, operational, and business owners. This committee will work together to create the guiding principles for governance within the organization and assess risks. Data stewards are typically the people responsible for carrying out the day to day implementation and enforcement of the policies.
It is important to recognize that data governance is not simply a packaged piece of software. It is a framework that implements policies and processes that are a part of the overall governance program. Underlying software and technologies can provide the tools to help implement the framework, but it is up to the data governance team to put in place the policies and processes that best suit their organization.
A good framework will often include the following principles:
Data security and governance are often intertwined. Most frameworks start and end with determining the access controls and security needs of datasets. This is especially critical in highly regulated industries such as financial services, telecommunications, healthcare, and insurance.
Security is also intertwined with all the other principles in a data governance framework. For example:
Data governance tools come in four general categories:
Datameer provides a deep suite of security and governance features and is intentionally designed NOT to replicate already-in-place governance mechanisms but instead work with these controls. These capabilities are well matured from Datameer’s ten years of experience working with large enterprises in highly regulated industries requiring deep security and governance.
Cloud and hybrid environments can create security and governance gaps due to capability mismatches between on-premises enterprise and cloud platforms. Datameer’s deep security and governance features ensure you get the same robust governance in the cloud and you would expect on-premises.
Datameer also integrates with enterprise and cloud security, offers asset-level controls and encryption on the wire and at-rest. It fully integrates with Snowflake security to protect all the data.
From a governance standpoint, Datameer provides many key capabilities: