What is data governance, and why is it important?
As organizations continue to drive digital transformation and innovation, they are likely to face frustrations with having visibility into and understanding their organizational data assets. While organizations may like the sound of data governance and may understand they have a growing need to implement it, they typically ask us, “what does data governance actually mean and how can it help my organization more fully leverage the value of the data we already have?”
This blog will explain what data governance means to Neal and why we think it is essential for every organization to implement it.
What is the definition of data governance?
When defining data governance, organizations typically broadly appeal to the fact data governance is intended to help manage data throughout its lifecycle, but this can lead to confusion or more questions – what is a data lifecycle? How do you manage it? Perhaps worst of all, these broad definitions do little to convey the actual benefits provided by data governance.
These definitions fall flat because they are attempting to condense a complex topic down to a single sentence.
The goal of data governance is to establish trust in the data
To really understand data governance, one needs to understand its underlying goal: establishing trust in data. Trusting data is one of the most important factors in ensuring data creates the value intended. To this point, data governance is intended to provide the consistency, transparency, traceability, and understanding required to enable data-based decision making and foster strategic changes to business practices and operations.
Data governance typically consists of a set of policies, procedures, and tools designed to manage data from the moment it is acquired. It is intended to ensure organizations can effectively modernize data and applications at scale. It also includes establishing user roles and responsibilities, helping to ensure data is only accessible to relevant parties.
The overarching goal of data governance is to ensure data delivers maximum value and remains easily useable without incurring tech debt that can complicate future development down the road.
What are the benefits of data governance?
The proper implementation of data governance has numerous benefits that help organizations maintain data that is trustworthy, valuable, and scalable. Having data that is trustworthy, valuable, and scalable in turn allows the organization to implement and leverage modern solutions that bolster capabilities in areas like discovering customer insights, more accurate forecasting, workflow automation, and much more.
The benefits stem directly from multiple elements that support data governance, including establishing a governance foundation and data stewardship, as well as implementing data standardization, master data management, data quality controls, and performance improvement policies.
Establishing a governance foundation
Common, control-based policies for data governance do not work well in modern organizations. They are frequently restrictive and are often believed to slow down implementation of new data sources and solutions rather than supporting rapid adoption.
By setting up modern strategies and policies for data governance, organizations can gain stakeholder buy-in and create company culture that views data as a company asset, not just an IT asset. Positioning data as a company asset allows the organization to agree on simple principles to guide data use, ensuring consistent usability and value across projects.
Setting up roles and responsibilities for data governance ownership creates a clear path for resolving data strategy and execution concerns, helping to expedite decision making processes. Additionally, by establishing ownership roles, organization can reduce the risk of incurring incremental tech debt by ensuring data implementors don’t work in siloes or embark on projects that don’t follow governance policies.
Data standardization is more than a few stakeholders setting a policy to which all data must follow. Establishing a policy this way may inadvertently leave out important concerns from an organization’s most vital data users and implementors. By leveraging a more thoughtful approach to data standardization, organizations can create data standards that help teams succeed rather than impair them.
To standardize data in a way that provides the greatest value to the entire organization, cohorts with stakeholders from multiple teams should be established, starting with teams that derive the most value from data. By creating cohorts in this fashion, organizations can implement data standards that are based on the input of all relevant teams. This benefits the organization by ensuring all voices are heard and considered, while also keeping teams participating in the cohort are all on the same page.
Master data management (MDM)
MDM helps organizations determine how best to approach data management by creating a forum to discuss data policy while codifying the purpose, mandate, and scope of the forum. By creating and codifying a forum for MDM, organizations create a platform to discuss new data challenges and needs while providing a clear set of criteria to evaluate the importance and impact of the challenge or needs. From there, the MDM forum can determine the best path forward to address the challenge or need.
Organizations can help ensure data quality remains consistent by leveraging several different data governance tools and procedures. By leveraging tools like a decision tree or data quality heat map, organizations can quickly identify and prioritize data quality issues for escalation. Additionally, by creating a decision tree to filter issues for escalation both business and technical stakeholders can understand the impacts and benefits of resolving different data quality challenges.
Driving continual data governance improvements requires more than a roadmap for future improvements. By working with a consulting firm with expertise in data governance, organizations can not only establish different levels of data governance maturity but identify where they fall in terms of their own maturity level. Creating definitions for different levels of data governance maturity and knowing where the organization falls in those levels allows the organization to gain a clear path forward to drive incremental improvements in maturity.
If you would like to learn more about data governance and how it can benefit your organization, complete our contact us form, and one of our data experts will quickly reach out to help answer your question.