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Highlights
- An effective governance framework can streamline data and combine different datasets to create new value and insights for stakeholders.
- It can help set up policies to secure data from multiple sources and create contingency plans for quick response and recovery in the event of a security attack.
- A robust data governance strategy should hold an organization accountable for the creation, consumption, and control of data and analytics.
In this article
Why data governance framework?
Every organization maintains a central repository of data, which forms the core of business intelligence and decision-making processes.
With the rise in cloud adoption and digital transformations, companies need to grant teams across the organization access to that central repository. However, to effectively navigate through huge volumes of data and extract value for quick decision-making, an organization needs a data governance framework to hold the data accountable. It can help increase the transparency around data and standardize the way it’s managed, so that all departments are on the same page on how data should be used. As Gartner puts it, an accountability framework ensures appropriate behavior in the valuation, creation, consumption, and control of data and analytics.
Data governance goals
Meet your business goals with a customized data governance framework.
A one-size-fits-all solution is not the answer. To build an effective data governance strategy, you need a governance framework customized for your business. A customized data governance framework should aim for the following:
- Reduce data risk – An organization’s inability to govern and effectively use data assets could lead to potential business loss. Data governance aids in identifying risks, and a framework enables the organization to keep track of these risks and better manage integrity and security.
- Increase data value – An effective governance framework can streamline data and combine different datasets to create new value and insights for stakeholders. When an organization has data coming in from multiple sources, it will need to be integrated into a larger dataset to provide a clearer picture on its value. Value can be measured monetarily (for instance, reduced costs, increased revenue, or generated income), or by how the data is helping optimize and innovate processes.
- Improve data quality – Inaccurate or inconsistent data can yield poor analytics, which can lead to poor decision making. On the other hand, good quality data gives your business a competitive advantage, allowing stakeholders to improve customer experiences and relationships. A solid framework monitors the completeness and accuracy of data by setting rules in place to determine which data to keep or remove. Once the rules and processes to identify errors, ensure completeness, and update automatically is defined, there will be improvements in data quality.
- Enhance data security – Data being an organization’s most valuable asset, data security is a topmost priority. Data security involves deploying measures that enhance the organization’s visibility into where the data is stored and transferred, how it is used, and who has access. A good data governance strategy aims to protect the data collection systems and entry points with established security controls, such as alerts or multi-factor authentication. A governance framework would help set up policies to secure data from multiple sources as well as create contingency plans for quick response and recovery in the event of a security attack.
- Ensure compliance – Data compliance ensures that data assets are organized, stored, and managed to avoid misuse, in line with organizational policies and standards.
So, what can companies do to achieve these goals?
Six building blocks of data governance
Adopting a data governance framework that incorporates six key building blocks will help strengthen transparency, integrity, accuracy, and accountability of data:
- Data governance policy – Sets guidelines for the management of organizational data. It includes data protection policy, data-sharing policy, regulatory adherence policy, etc.
- Data governance rules – Ensures adherence to data policy. Data protection rules can pertain to a specific type of data asset, regulating how data can move within the organization.
- Data classification mechanism – Organizes data based on business terms or data classes. Business terms is the language through which data is interpreted in the organization. Take for instance, Internal, Confidential, and Restricted. Data classes are measured through metadata, which tells us what’s inside the data source.
- Metadata management – Assists in contextualizing and managing data assets, reducing the likelihood of inconsistent data.
- Data lineage traceability – Captures the end-to-end journey of data from its source. This is an important aspect of data governance since it helps keep track of day-to-day data usability, minimize errors, and reduce the time and resources necessary for data migration or upgrades.
- Data quality – Ensures that data assets are complete, uniform, consistent, and fit-to-use for the intended purpose. Data quality directly impacts the decision-making process for an organization. Good quality data ensures better analytics, improved business intelligence efforts, and seamless reporting.
Concluding thoughts
Robust data governance is an imperative in this digital world.
What’s clear is that companies need to protect their wealth of data. And leading cloud vendors are making it easy to do so. They offer tools for data discovery, data assessment, cataloging, data quality, and security. There are various data governance tools available in the market, such as Collibra, Informatica, Talend, and many others. Business leaders would benefit from weaving in a strong governance strategy into their digital transformation to ensure integrity and security of data, an imperative in today’s world.
However, before choosing any data governance options, organizations should clearly outline their overall governance framework objectives and components, set data priorities, and iron out any data complexities. There is no one-size-fits-all solution; the data governance strategy that’s tailored to meet an organization’s specific objectives and overcome their challenges will be a great asset—and accelerator—in an organization’s digital transformation journey.
