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Data Governance Tools Summarized

Data Governance, data management, data quality, data preparation, data integration


What is data governance? What are data governance tools? Why is data governance important? Are data governance tools as beneficial as they claim to be?

These are questions that need to be asked and answered. Asking these questions allows enterprises to riddle out their data management needs. Very often, organizations do not realize until it is too late that they are in dire need of a data governance policy. This is why we have taken it upon ourselves to ask and answer them.

Dipping our feet into the pool of knowledge, let us understand what data governance is.

The term typically refers to processes pertaining to the administration and regulation of data, its organization, maintenance of consistency, and management of usage, availability, accessibility, and authenticity.
Data governance is generally carried out by governance teams that put together a data governance program. This program is a rough outline of organizational policies, rules, and goals. It also aims at defining mission statements, success metrics, consistency standards and creating a framework of organizational structure. Data governance policies also help improve decision making, operation architecture and resource sharing.

“Data governance tools are software solutions that aid data governance teams to automate facets of program  generation. They have in-built capabilities that can facilitate collaboration, policy creation, data classification, data cataloguing, and intelligent documentation.”

Having answered the first two questions, let us consider the importance of data governance and data governance automation tools.

In an age of digital advancement, the fear of ‘robots taking over’ is prevalent. While this fear is irrational, the truth is that as technology advances, the risks involved in utilizing it also increase manifold. In the case of digital data management, the organization is responsible not only for customer privacy, but also ensuring that sensitive operational data is not accessed or misused. Data can be misplaced, manipulated, or abused if proper care is not taken to prevent this from happening. This is where data governance policies emerge as extremely important.

A data governance program establishes rules and policies to supervise the collection, storage and usage of data. It ensures that data is gathered and consumed ethically and safely. The policies in data governance also extend to risk management, auditory compliance, and data quality maintenance. With policies and rules in place, business operations become streamlined, inconsistencies and errors reduce and resource sharing, data integration improves.

The goals of incorporating a system-wide data governance program widely range from harmonizing data formats, centralizing data architecture and demolishing the silo structure to monitoring data usage, ensuring data security and enforcing the regulatory policies put in place. Violations of these policies are easily flagged, and suspicious activity can be highlighted. The use of data governance tools also makes it possible to hold individual users or even departments accountable for mishaps as errors in data handling are easily identifiable.

Now, having understood data governance and its vitality in data management, we can explore data governance tools.

Data governance tools allow data management teams to create specific, customized governance programs that suit the enterprise’s requirements perfectly. They help businesses keep up with progresses in risk management, data security and compliance trends.
By automating several aspects of the process, data governance software makes it infinitely easier for governance teams to put together fool-proof policies. Some of the procedures that can be automated are –

  • Policy Creation – One of the primary roles of a governance tool is the creation of policies. With analysts and steering teams deciding the rules and regulations, the process of creating a systemized usage plan falls on the software.
  • Collaboration – These tools can also intervene in how users interact and alter their collaboration style. From a siloed approach, governance tools transform the collaboration scheme to a centralized data sharing system.
  • Workflow management – Creating a workflow for data management processes and ensuring that the transfer and sharing of data occurs in a seamless fashion.
  • Metadata Discovery – Scanning through datasets, governance tools can discover and understand metadata. In collaboration with data quality tools, they can create order out of chaos based on identifiers in metadata.
  • Catalog Creation – Based on the metadata properties, these tools can create extensive catalogs of all types of data.

Data governance tools can also undertake data classification, data mapping and glossary creation independently or in collusion with other enterprise automation software.

Data governance tools can also access social media and mobile platforms, fetch data, and improve data understanding. They can manage data storage on and off the cloud, create inventories and control correction and deletion of resources.
With their varying abilities and the fact that they are extremely time and cost effective, data governance tools can be exploited to reveal a gold-mine of data management solutions. They can, in essence, resolve all of an organizations’ data security and storage issues while also providing insight and solutions to better the enterprise’s data management strategy.