Data is among your company’s most valuable commodities, but only if you know how to manage it.
For most companies, data governance is now a “need-to-have” for several reasons:
- The rise of GDPR and other compliance measures that put data security under more intense scrutiny;
- The increased sophistication and technological prowess of bad actors seeking access to proprietary data;
- The proliferation of data usage across the company, as more and more teams require data access in their work; and
- Increasingly varied, complex, and distributed data architecture and data management.
More data, more access to data, and more regulations mean data governance has become a higher-stakes game. As you prepare to roll out a successful data governance strategy at your organization, keep an eye on the following four trends that will shape the future of data governance.
Data Governance Trends
The biggest data governance trend isn’t really a trend at all—rather, it’s a state of mind. Data governance is fast becoming an “all-company” problem, no longer relegated to disparate silos of the business. At the same time, data governance technologies are growing more intelligent.
The result is a win-win for data teams, which increasingly have both the organizational buy-in and the tools they need to launch data governance strategies that effectively maintain the availability, usability, and security of data.
Today’s data engineering teams have multiple options when it comes to designing data governance strategies that best suit their respective businesses. Here are four data governance trends to watch.
1. Cloud-based data governance
In some ways, it would be a disservice to call cloud-based data governance a trend, but it’s undeniable that the rise of the cloud has had a marked impact on how we ensure data compliance and discovery.
Modern data warehouses, data lakes, and data lakehouses have empowered multiple constituencies across companies to access and use data without always needing to tap the data engineering team for help.
Organizations are fast finding that cloud-based data access requires an equally nimble approach to data governance. Too many organizations are still enforcing data governance with highly manual, inefficient tools and processes. As data input channels and tech stacks become more complex and prolific, this manual approach to data governance just isn’t scalable.
Plus, a growing number of companies are leveraging cloud-based, distributed data architectures like data mesh. Without a comprehensive and flexible data governance strategy, companies will not be able to manage their data effectively.
2. Data governance as a service
Data governance isn’t one standalone tool or workflow—it’s a complex combination of people, processes, and tools that unite to give an organization better control over its data. Thus, multiple tools and technologies can work in harmony in service of an overarching data governance strategy.
Keep an eye out for the rise of an emerging new trend to tackle this: data governance as a service: i.e., the proliferation of third-party providers offering data governance services to help organizations manage their data more effectively. As you launch your own data governance strategy, look partners that can help you:
- Classify and map your existing data, so you have a clear picture of your company’s complete data landscape
- Store your data with a cloud-based platform that syncs well with automated tools
- Conduct data observability, to ensure your data is accurate, reliable, and trustworthy
- Create a data governance model, complete with goals, mission statements, roles and responsibilities, and accountability structures
As data governance becomes front-of-mind for every company, leveraging a growing number of tools and services can help make instituting data governance strategies easier and more scalable.
3. Application of blockchain and homomorphic encryption
A third trend data governance teams can take advantage of to enhance data privacy and security are blockchain and homomorphic encryption.
Blockchain technology is intrinsically secure. “It’s based on principles of cryptography, decentralization and consensus, which ensure trust in transactions,” according to IBM. The eponymous “blocks” in blockchains represent a data transaction, and blocks connect to one another in an essentially unbreakable cryptographic chain. If your organization uses a private blockchain, your data engineering team can manage membership, which helps to streamline data governance.
Similarly, homomorphic encryption augments data privacy by converting data into ciphertext that “can be analyzed and worked with as if it were still in its original form.” This allows teams to analyze and use encrypted data without compromising its encryption. Now, organizations can ensure the privacy of their data, even when it’s being used by third parties.
This is particularly important for organizations that have adopted a cloud-based approach to data architecture and data governance. As this TechTarget article explains, “With homomorphic encryption, data processing or analytics can be outsourced to a third party without needing to trust that party’s data security. Without the correct decryption key, the original data can’t be accessed, which means sensitive data can be sent and analyzed while still remaining encrypted.”
In 2023 and beyond, I predict we’ll see a lot more cloud-based data governance, which shifts data management away from a centralized IT team in favor of delegating data management responsibilities throughout the enterprise. The end result is a collection of data users who can manage their own access and permissions and alleviate the IT bottleneck.
What’s more, I predict we’ll see data engineering teams ditch manual data governance processes in favor of automated cloud-based data governance tools, which teams can activate in mere minutes, and which yield better, more reliable results.
4. Automation of data governance
Say goodbye to manual data governance processes. The sheer volume of data today’s organizations manage makes it practically impossible for data engineering teams to govern data assets manually.
Thankfully, advances in AI and machine learning are making it possible to automate certain data governance tasks, like data observability.
Data observability is an organization’s ability to fully understand the health of the data in their systems. Built on five pillars—data freshness, quality, volume, schema, and lineage—data observability eliminates data downtime by applying best practices learned from DevOps to data pipeline observability.
Data observability platforms like Monte Carlo allow organizations to use automated monitoring, alerting, and triaging to evaluate data quality and discoverability issues. This makes it easier for data governance teams to monitor data changes, identify broken pipelines, and fix any problems more quickly. Ultimately, more reliable data is a critical part of any data governance strategy, which seeks to ensure data is not just accessible and secure, but also usable by various constituents across an organization.
It’s an exciting time to be in the data business—and data governance is only enhancing that excitement. Solid data governance strategies make it easier for organizations to use, discover, and apply their data—and the resulting possibilities are exciting and unlimited.
Technologies like data observability platforms are paving the way forward for more effective, automated, and scalable data governance strategies.
Interested in learning more about how data observability and data governance go hand-in-hand? Reach out to get started.