Why Two-Way Tag Syncing ain’t no Slouch for Data Governance

Have you ever tried keeping multiple data systems in sync with each other, only to find that managing them is like juggling with one too many plates/balls/flower vases? That’s where two-way tag syncing steps in and saves the day.

Imagine you’re working with data spread across different platforms. Every time you make a change to your data’s tags in one system, you have to go and manually update it elsewhere. Not only is this time-consuming, but it also opens up a huge can of worms for errors, inconsistencies, and compliance headaches. Wouldn’t it be great if changes could just flow across systems automatically? That’s exactly what two-way tag syncing does.

So, What’s the Deal with Two-Way Tag Syncing?

In simple terms, two-way tag syncing means that when you make a change to a tag in one system, that change is instantly mirrored in the other connected systems. It’s like a magic trick where everything just stays in harmony. You add, remove, or modify a tag in one place, and voila—it’s instantly updated everywhere else. This kind of automation can be a game-changer, keeping your data organized, consistent, and up-to-date without any manual effort.

Why Is This So Important?

Well, think about how much time is spent manually managing tags across different data systems. Every time you adjust something, you risk making mistakes. Those mistakes can lead to inconsistencies, which, if you’re working in a regulated industry, could mean trouble. But when you automate this process, you get rid of the manual work, the potential errors, and the headaches that come with trying to keep everything in sync.

Let’s Put It into Perspective

Picture this: A Data Governance Officer at a healthcare organization needs to keep sensitive patient data properly tagged across all platforms. With two-way syncing, they can update tags related to patient confidentiality in one system, and the change will be reflected everywhere else without lifting a finger. No more double-checking, no more cross-referencing. It’s efficient, accurate, and ensures compliance.

Or consider a data engineer working at a financial services company. Every time a new regulation comes out, they must make sure that all relevant data is tagged correctly across different systems. Without two-way syncing, it’s a nightmare of manual updates, and one missed tag could mean non-compliance. But with syncing, any update they make to the tags in one place is instantly reflected in all the others. It’s that simple.

How Does It Work?

At its core, two-way tag syncing pulls in tags from your data platforms and allows you to manage them centrally. It’s like having a control center where you can see all your tags and tweak them as needed. The best part? Any changes made are automatically pushed back to the original systems, ensuring everything stays aligned.

Let’s say you have a data governance tool and you’re using a cloud service where tags are crucial for organizing data. You could update a tag in your governance tool, and that change will be sent back to your cloud service, keeping everything consistent. No more back-and-forth, no more manual syncing—just pure efficiency.

Example

Imagine a retail company that manages its inventory data across multiple systems, including their e-commerce platform, in-store sales systems, and a Snowflake data warehouse. The data governance team needs to ensure that sensitive product information, such as supplier details and cost prices, is consistently tagged and protected across these systems.

With two-way tag syncing, the team can set up object tagging directly in Snowflake. For example, they tag the “Cost Price” column in their “Products” table as “Confidential,” ensuring that this sensitive information is restricted to authorized users only. Similarly, they might tag the “Supplier Name” column as “Internal Use Only,” meaning that this information won’t be exposed to external partners. These tags are then synchronized back to their central data governance tool.

Additionally, they use Snowflake object tagging to label the “Customer Email” column in their “Orders” table as “PII” to maintain compliance with data privacy regulations like GDPR. This column-level tagging ensures that any system connected to Snowflake recognizes this data as sensitive, enforcing access controls and encryption policies as needed.

By integrating two-way tag syncing, any updates made in the governance tool—such as adding a new “Seasonal” tag to the “Category” column of the “Inventory” table—are instantly mirrored back in Snowflake and across other systems. This ensures that everyone working with the data has an up-to-date, consistent understanding of its sensitivity and classification, reducing the risk of exposure, improving compliance, and making data management more efficient across the entire organization.

Why Should You Care?

• It’s a Time Saver: No more wasting hours manually updating tags. Two-way syncing does it for you, freeing you up to focus on more important things.
• Fewer Mistakes: When you rely on manual updates, you’re bound to slip up now and then. Automating the process means fewer errors and more reliable data governance.
• Better Compliance: If you’re in an industry with strict data regulations, two-way syncing helps ensure that your data is always tagged correctly, reducing the risk of non-compliance.
• Keeps Things Simple: It takes the complexity out of managing multiple data systems, allowing you to handle everything in one place.

The Big Picture

In today’s data-driven world, staying on top of your data management game is more important than ever. Two-way tag syncing isn’t just a “nice-to-have” feature; it’s a necessity for ensuring that your data remains consistent, reliable, and compliant. It’s about taking control of your data ecosystem and making sure that you’re always a step ahead.

If you’re dealing with data tagging headaches or spending too much time managing tags across platforms, it might be time to explore how two-way syncing can make your life easier. It’s not just about keeping your data neat and tidy—it’s about building a data governance process that’s as smart and efficient as the rest of your organization.