Data. It can be one of the greatest assets at any business. It can also be a mess sometimes. Understanding it is critical to getting ahead in business, but it is also vital to marketing and providing a high quality customer experience. In that regard, data governance procedures serve vital functions. They define responsibility and creates an air of accountability at all organizations.
Looking at data can be cumbersome and tedious at times. Understanding who owns it, who is responsible for it, and how it is used is not always straightforward. Its governance works on a system of models that determine who is allowed to take action with certain information as well as the circumstances, methods, and time frame of taking that action. Implementing a strong governance framework at your business is crucial to handling the steady influx of data you will undoubtedly accrue as you run your organization.
Types of Data Governance Models
In data governance, there are three models primarily used. These are centralized, decentralized, and hybrid. Here is a quick breakdown of each:
Centralized – A single person manages the data, delegates responsibility, and takes a top down approach to data governance. This model is great for quality control and focusing on managing the data effectively.
Decentralized – A group or committee handles data management and governance strategy. Different areas of the organization will create and manage their own accordingly. This model has plenty of benefits like improving data management in general, but it can create duplicate information and other issues along the way.
Hybrid – In this model, the governance framework is provided in a centralized way, but unique departments handle individual components. It is a best of both worlds approach that works for many organizations depending on their needs.
Data Governance Policies and Compliance
In data governance, there are numerous industries where it is a crucial aspect of running the organization. Poor data integrity and governance in the financial sector, for instance, are bad news for everybody. The last thing anyone wants to hear is that his or her bank had a breach. The same goes for consumer reporting agencies, such as credit bureaus or retail establishments.
Any production plan or marketing firm also needs to have robust strategies in place and be compliant with all applicable data governance policies. It is a duty to not only the clients/customers, but to the continued operations of the organizations themselves! GDPR compliance is increasingly important in this day and age. Using data governance procedures and protocols helps organizations be in full compliance with GDPR standards. Document personal data thoroughly and capture privacy notices. Privacy notices are metadata governing privacy, so collecting and storing them appropriately are necessary to be GDPR-compliant.
Another part of data governance compliance is knowing where data about individuals is stored on your servers. For privacy, organizations should consistently review how they are collecting data and recording consent for that collection. Finally, your organization needs a plan in place for how to detect and mitigate potential breaches.
Data Integrity
When you think about data governance, remember that it is only part of a larger strategy for managing data. Data Integrity is also crucial to proper data governance tactics, which refers to the usability, organization, and consistency of your data. Data integrity and governance policies and procedures work in tandem with one another to create a secure data pipeline at the organization level. These two things work in conjunction with one another to create a powerful system for governing and assessing quality data. If the data is high quality enough and has integrity, then kicking into data governance procedures and policies is easier in the long haul.
Scalability
As businesses grow, the sheer amount of data they collect gets a bit out of hand. Scalability of data, then, becomes a primary concern for improving operations. Fortunately, properly implementing governance policies and integrity policies at an organization can help make your data scalable. Scalability covers both physical and digital concerns at the organization. On the physical side, upgrading hardware and using cloud services help you scale your data governance procedures easily. Scaling your data gathering procedures is another part of the process, but once the data is already in your data lake, you need to scale your governance as well. The best way to do this is to understand what kind of data you have and how to delegate it appropriately.
Managing multiple data sets as you scale the company up is not always easy or convenient. Using proper data governance protocols, you figure out ways to manage your data effectively and scale it to your company’s growth. Auditing, policy enforcement, and privacy protection all play significant roles as well to ensure you govern your data properly at scale.
Implementation
Implementing your governance strategies should be simple. Of course, that does not mean that it happens quickly. It requires some time and planning. You need to define roles, educate business partners and stakeholders about data governance, ensure that your procedures are being followed, and define some sort of way to measure progress/success. Obviously, this is a bit of an oversimplification, as the process is much more involved, requiring councils, setups, and well defined metrics, but the basic idea is the same.
Proper implementation of data governance procedures only sets up your organization for success and general compliance. Properly implementing a data governance strategy has numerous benefits for the organization. It produces costs across the board and simultaneously protects the quality of the day. Furthermore, it keeps you compliant with established policies and procedures in addition to reducing risks. It is more efficient, proves discussion, fosters collaboration, and ultimately makes every aspect of running your business better.
