Learn what data governance is and how it can help improve data quality, security, and integrity within the modern enterprise.
Businesses are becoming inundated with data as analysts continue to integrate more and more datasets into enterprise analytics systems. However, doing so has caused strains on organizations by creating data silos, quality issues, and security concerns.
To solve these increasing pressures, businesses are increasingly relying on their data governance teams to implement consistent and scalable solutions to these problems so that every employee, whether on the frontlines or in the boardroom, can feel more confident in the quality, integrity, and security of the data they use to make decisions.
In this article, we’ll explore what data governance is, including the benefits and challenges, and how it can be easily implemented in your business.
Data governance is the set of systems and processes for managing the quality, security, and standards of data within an enterprise data management system. It ensures that the integrity of data is maintained throughout its lifecycle and that access controls and retention policies are consistent and comply with both business security policies and regulatory requirements.
More broadly, data governance ensures that data is utilized in a way that meets strategic business objectives to maximize efficiency and reduce risk. By applying consistent and scalable data standards, a business can ensure that analysts and other employees across the organization are working with secure and high-quality data.
Enterprise data management systems allow analysts to easily integrate internal and external data into centralized repositories so that they can build models, reports, and alerting systems that guide the operations of a business.
However, in practice, data can be messy and inconsistent. For example, different databases may have different protocols for identifying customers, or manually curated data may contain typos that make it difficult to reconcile between different datasets. These data inconsistencies reduce efficiency across the organization. Even worse, they can lead to inaccuracies that drive poor business decisions.
Increased data privacy regulation such as GDPR and CCPA mean that businesses must have policies and systems in place to maintain the privacy of personal information and be able to redact and delete subsets of data.
Data governance works to solve these challenges with consistent standards, policies, and systems to ensure better quality and more secure data.
Data governance provides standards and protocols for how data is stored and interpreted, data cleansing and harmonization pipelines to reduce data silos, monitoring and alerting systems to understand the state of the integrations and pipelines, and access control and retention policies to enforce strict security policies.
For instance, an external dataset may have a different standard for identifying customers than internal systems do. To ensure that this data is correctly reconciled with internal protocols, a data pipeline can be implemented to transform and aggregate this into a centralized repository so that analysts can query the data based on a predictable and consistent identifying pattern and be confident that the results will include all relevant data for the customer.
Data governance requires a solid team of individuals to set policies, implement them, and monitor ongoing operations and KPIs. For most organizations, this requires a few key roles:
The Chief Data Officer (CDO) of an organization is an executive tasked with overseeing the entire data management and data governance systems to ensure that data is being securely used in a way that most efficiently meets the overall business objectives.
The CDO will often be the person allocating budgets, structuring the teams, setting broad goals and objectives, and monitoring progress and key performance indicators.
The data governance steering committee is a group of individuals — often comprised of leadership — who decides on the strategies, protocols, and standards that define how data governance is actually implemented across an organization.
For instance, the steering committee will often determine standardized protocols for how data should be formatted and stored to ensure consistency across departments and minimize data silos. That way, even if analysts or other employees work in very different departments with little communication, they’ll easily understand how to interoperate across domains and datasets.
A data owner is an individual responsible for managing the governance of specific datasets or domains within an organization. They will take policies set by the CDO or data governance steering committee and ensure that they are properly implemented in the systems that they manage.
A data owner could also be a party within the data governance steering committee, either as a voting or non-voting member. This allows the committee to get diverse representation across the organization to ensure that the standards they set fit the wide range of needs across the organization.
A data steward is an individual responsible for actually implementing the data governance policies. They manage the day-to-day operations including maintaining data quality and accuracy, implementing changes to standards and protocols, removing redundancies and breaking data silos, and enforcing security requirements and access control policies.
While the data steward may not be a voting member of the data governance steering committee, it’s important that their voice is heard in the decision making process as they are the ones that are actually doing the ongoing work of implementing the policies so they’ll best understand the direct implications of them.
As data governance is a broad and diverse topic, it’s not always clear why it’s essential. Let’s take a look at a few specific benefits that it can afford:
Implementing consistent standards and protocols for data can significantly improve the quality and integrity of data. For instance, data dictionaries and useful metadata tagging can help ensure that those procuring and transforming data will understand the intent of different data structures, which will minimize errors and create clearer processes.
Ultimately data is meant to help drive strategic business decisions. Having a sound data governance program in place can help managers and executives feel more confident in their decisions, as data will be more accurate and up-to-date. This can furthermore afford faster decision making as confidence in the quality will allow for fewer manual sanity checks on analyses.
Businesses often hold very sensitive data or personally identifiable information (PII) such as customer names, email addresses, phone numbers, etc. By enforcing consistent security policies and access control lists, data owners can ensure that their systems are compliant with business privacy policies and regulations.
For instance, a proper data management system should allow data owners and data stewards to restrict access to different datasets to different departments or even individuals to ensure data is not accidentally accessed by people that are not permissioned to
As with most large data management processes, data governance certainly comes with some challenges. Some common challenges that many businesses will need to address:
Coming up with consistent and quality data governance policies requires leadership that can make decisions and monitor ongoing performance to ensure that objectives are being met.
Once data governance policies have been set, there must be specific KPIs that define its success and contribution to providing actionable and valuable insights. If the KPI objectives are not being met, data governance policies should be adjusted accordingly.
Data governance requires sufficient resources to be implemented, monitored, and maintained. Therefore, the business must prioritize data governance to ensure that the teams have what they need to succeed.
Proper data governance requires sophisticated data management and analytical tools to maintain quality and secure data. Legacy analytics systems often cannot satisfy these requirements and therefore it's important that businesses modernize their systems.
Large businesses can have hundreds or even thousands of disparate datasets that need to be integrated and aggregated. This requires a lot of time and expertise to bring them all into a centralized repository where data governance policies can be properly implemented.
Datasets can be messy. This is particularly the case in manually curated data where there can be typos and inconsistencies. Businesses will have to come up with both automated and manual processes for addressing and reconciling poor data quality.
Traditional enterprise analytics tools run on local desktops. This can create data silos and governance concerns when analysts download copies of data onto their local machines. These must be moved to centralized, cloud-based systems.
To properly govern and secure the usage of data, businesses need to be able to define exactly which individuals, roles, and departments can access different datasets. Ideally they should even be able to do so down to the row-level. Legacy systems often don’t support this level of granularity, or are unable to do so in a performant and scalable manner.
Data governance touches all aspects of the data lifecycle and therefore requires several different data tools.
First and foremost is a data management system. Data stewards must have data transformation tools to consistently clean, harmonize, and aggregate datasets. The data management system ideally will have out-of-the-box security and compliance tools so that these don’t need to be built from scratch. For example, ThoughtSpot’s row level security (RLS) allows granular permissioning of data down to the row level without sacrificing speed or scalability.
With a data management system in place, data governance processes will also need to communicate with existing internal IT systems to ensure efficient and secure integrations with internal and external systems. This can be particularly complex for organizations that run on-premise or hybrid cloud solutions, as communication flows will need to be defined through all of these various paths.
Finally, a consistent and scalable data governance system will require tools to manage metadata. For example, schemas, data dictionaries, and content navigations will need to be implemented on top of the datasets to ensure that data is properly interoperable and of high quality.
Fortunately, these are often generalizable concepts so there’s no need to reinvent the wheel and build them all from scratch!
Data governance is essential to ensure that businesses can leverage their data to make smarter business decisions in the most efficient manner without sacrificing security. However, doing so requires implementing complex data management systems to allow data stewards to define standard protocols, build cleansing and harmonization pipelines, enforce strict access control, and much more.
ThoughtSpot’s powerful Modern Analytics Cloud platform was built with data governance in mindd. Data can be quickly integrated, transformed, and analyzed from all of your disparate data sources into an easy-to-use and secure centralized repository in the cloud. Try a free trial of ThoughtSpot to see how easily you can implement a data governance program for your business.
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