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Data governance is the set of policies an organization puts in place to ensure appropriate access, integrity, standardization, and security of its underlying data. Data governance has become a hot topic in modern organizations, as data strategy, especially with the rise of Large Language Models, has become ever-more important.
A large focus of data governance is to avoid data siloing across an organization by creating standards for data accessibility across the organization. With this effort, teams can consolidate data into one place.
In today’s world of B2B software, there are many more compliance requirements than ever before (HIPPA, GDPR, SOC2, etc.). Sometimes, to operate in a market or industry, it is required by law or by vendors to maintain these compliance standards, which explicitly requires a data governance strategy.
Teams we work with, such as vendors and customers, expect data of their close collaborators to be safe-guarded to ensure a healthy long-term relationship. By signaling to the market that your organization is ahead of the curve on data governance, this builds trust with customers and vendors.
While we always trust the people that we work with, in a world with more hacking and phishing attempts than ever before, it makes a lot of sense to limit data access on an as-needed basis to reduce the surface area of a potential breach. Formal data governance can help with establishing what as-needed means.
By having a formal data access document within an organization, a team can be more deliberate in giving access to data and information to team members. With that, team members can be empowered with additional data access they didn’t have before to better accomplish their roles within an organization.
Overall, there are many aspects to a good data governance policy, but below are some of the components one might see in a policy.
To see the extent of a breach, companies should always have logging setup of the data accessed, by whom, and when. This helps in evaluating the scope of a potential issue and the areas of vulnerability. Additionally, unauthorized or suspicious access can be traced.
To ensure only appropriate access of a set of data, login security is incredibly important, particularly with the proliferation of password managers and 2 factor authentication. Every organization should have clear written policies around password and login management principles to reduce the chances of a hack or, in the case of a hack, containing the issue.
Most modern SaaS tools allow different levels of access and permissions within an application. Instead of making every user an administrator, it oftentimes makes sense to restrict access of each user to a more granular access control to ensure safe data handling.
Any organization that has data management and data governance should have an approval structure in place for when an individual needs access to additional data or software in their workflow. This process should be clear and straightforward, but robust enough to ensure efficient data access.
For a data governance strategy to work, there must be individuals within the organization responsible for data governance. For larger organizations, it may even make sense to have a data governance council to determine and enforce best practices within an organization. Regardless, the first step to a successful data governance implementation is to empower individuals to be in charge of the process.
Mistakes happen in data governance and data management. It’s important to plan ahead for these mistakes and mitigate the downsides of these unforeseen events. It’s good to understand who needs to be notified when events happen, resolution steps for common unwanted scenarios, and post-mortem processes on making improvements.
Teams within organizations tend to diverge in terms of their practices. One of the best aspects of data governance is the ability to standardize, adding agility to operations at an organization as a whole. Standardization can additionally help with data integrity, ensuring consistent data management, aggregations, and pipelines for full accuracy.
These two concepts are closely related, but are distinctly different. A close analogy would be data governance is like the Architect in a construction project, whereas data management is the contractor. The Architect sets the high-level guidelines and rules for constructing a building, whereas the contractor puts those procedures and rules into execution. Data governance is focused on setting up rules and procedures, whereas data management is about the practical implementation of databases, backups, security controls, and more.
All organizations that feel a need to standardize data practices. Typically, this begins right from the beginning of a company and scales with the requirements of the organization. Many seed stage companies are required to have an in-depth data governance strategy due to SOC 2 or other data standard requirements; regardless of requirements by customers, it is always best practice to maintain a centralized process for managing data.
Alation is a tool for documenting data governance processes and creating automations around stewardship, reducing the administrative overhead of data governance. Additionally, Alation focused on Key Performance Indicator tracking with data governance, helping with measuring the success of any initiatives.
Atlan is a data governance platform for searching data governance policies and automatically flagging personally identifiable information (PII), along with automating masking and data governance policy implementation.
While not explicitly a data governance solution, Explo can be immensely helpful in permissioning data for access for both internal and external BI use cases. Explo offers this granular control with permissioning and customer access tokens to ensure safe data access.
Data governance has become an incredibly important part of information security, information technology, and the growth of startups and large enterprises. Without data governance, companies would have siloed data and data risks that aren’t worth taking. With data governance, companies can feel more confident in how they handle their data, which will only benefit the software ecosystem, as we can trust the vendors we work with more deeply than ever before.
