As organizations increasingly rely on data to drive all aspects of their businesses, the need for good data governance practices becomes increasingly clear. By implementing good data governance practices, you can ensure that your entire organization can analyze and act on data, while meeting all regulatory requirements. However, devising, implementing, and enforcing good data governance can be challenging.
In this post, we will explore what data governance is, why it’s important, the benefits of data governance, and some of the challenges involved in setting up a successful data governance program.
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.
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.
By improving data quality, data governance can help reduce the need for data cleansing and data enrichment activities. In addition, data governance can help organizations avoid regulatory fines and penalties by ensuring compliance with data privacy and security regulations.
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.
Data governance can also help reduce risk by improving data quality and data accuracy. By ensuring that data is complete, accurate, and consistent, data governance can help organizations avoid potential legal liabilities and business disruptions.
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 do so.
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 a critical piece of any organization, yet it can be challenging to get right. ThoughtSpot’s data governance capabilities make it easy for you to control who has access to your data, set permissions and security protocols, and define workflows to ensure that everyone in your organization is working with the most up-to-date information. Sign up for a free trial today and see how to improve your data governance using ThoughtSpot.