This blog was co-written with our partners at Anomalo.
Congratulations—at last, you’ve finished your cloud data migration and it feels as if you just bought a brand new car! You can go from 0 to 60 in how many seconds?
Hopefully, your cutting-edge cloud data warehouse comes with the latest and greatest safety features, too. Just like a car, the upgrade is great, but you risk accelerating too much and causing new problems you have to be ready to handle.
In this post, we’ll discuss what a cloud data migration is and why it’s so powerful for businesses as well as the importance of data quality during this process.
Modern data infrastructure enables you to significantly scale up the amount of data you work with, which while desirable, also introduces data quality risks. Left unchecked, your new data systems could actually create more problems by virtue of there just being more data.
A complete migration needs a state-of-the-art data quality monitoring tool as a seatbelt to keep the data safe.
Why Do Businesses Invest in Cloud Data Migrations?
Data modernization is an umbrella term for the many ways businesses upgrade their data infrastructure, typically with cloud-centric solutions like the Snowflake Data Cloud.
One of the most common elements of data modernization is a cloud data migration, referring to the process of transitioning data from legacy, on-premises databases to a modern, cloud-based data warehouse or data lake.
Traditional on-prem data infrastructure incurs significant maintenance costs for an organization and still tends to face reliability issues.
In contrast, a cloud provider specializes in data infrastructure, typically offers multiple back-ups to reduce the risk of outages, and abstracts away any thought you have to give to data management—it just works.
Cloud migrations usually come with a host of other benefits. Rather than requiring you to invest a large amount upfront to build infrastructure, cloud data services tend to be low-cost and scale as your organization’s data grows. The cloud also democratizes access to data, whereas on-premises databases tend to restrict access and create silos.
With easier access to data, your organization is more likely to perform analytics, use business intelligence tools, or run machine learning algorithms that would be harder to support with a conventional data model.
Cloud Migrations Fall Short When They Overlook Data Quality
Typically, the main purpose of a cloud migration is to transfer your data to a place where it’s more centralized and easier to access. The quality of that data is an afterthought (if it’s considered at all).
However, any cloud migration is bound to come up short if you don’t also modernize key support systems like your data quality monitoring tool.
Without instituting proper data quality monitoring, organizations end up creating more data problems than they had pre-migration. Modern data stacks are open systems by design, giving everyone in an organization access to data that was previously siloed.
With that openness comes far more opportunity for low-quality data to creep in. For example, people adding new sources or you might have different teams making conflicting assumptions about a table’s schema.
Cloud data architectures also enable data aggregation across more sources than ever before. Intermingling sources further exacerbates risks around misaligned data, say if two sources are keyed differently or use different methods for identifying users.
Poor data quality sows doubt in your company’s data, which in turn defeats your migration’s original purpose of making the business more data-driven. When team members experience unreliable data, it’s hard to know whether any data is trustworthy.
Debugging messy data becomes a huge time sink of its own, and the integrity of any decisions you do make with data gets called into question.
Cloud Migrations Need a New Approach to Ensure Data Quality
Data quality monitoring platforms instill the confidence that teams need to make decisions based on their data. But be careful about taking a traditional approach to data quality monitoring. That would be like trying to pump gas into your modern electric vehicle.
Historically, data quality was enforced by rules, where a few domain experts encoded the set of criteria that the data needed to meet. Validation rules remain an important part of a comprehensive data quality strategy—but they don’t cut it on their own for data at cloud scale.
With rules, teams need to anticipate every possible way data could be problematic. This approach becomes brittle as more sources are added and unified after migrating to the cloud. Not only do static rules fail to cover the “unknown unknowns,” but they also struggle to capture complex expectations about the data distribution.
When there’s a fluctuation in your average order value, is that because of seasonality or because of broken data? To unlock the power of your data, you need to feel confident answering these kinds of questions.
It’s important to look beyond hard-coded rules and empower everyone at your company to take responsibility for data stewardship. Salespeople are likely subject matter experts about sales data—shouldn’t they have a no-code tool that lets them view the quality of that data and add new kinds of monitoring?
By giving everyone in your company a stake in quality, you earn their trust in the data and can succeed in creating a data-driven culture.
Anomalo is a Data Quality Monitoring Platform Built for the Cloud
Anomalo is a modern data quality tool that directly integrates with all the components of your cloud data architecture. Within minutes, you can connect Anomalo to your modern data stack and start monitoring all your tables automatically.
Moreover, Anomalo’s data quality monitoring is designed for scale. It uses unsupervised machine learning to detect data anomalies automatically, whether a single field shows something unusual or the relationship between multiple parts of your data is off. While you can still write rules, this approach helps flag issues you may not have even thought to look for.
With Anomalo’s alerting and resolution features, you can quickly sort out data problems before they harm downstream systems. Anomalo intelligently alerts relevant stakeholders when there’s a concern. Data engineers can count on Anomalo’s root-cause analysis to identify an appropriate fix to the issue.
Should you want to periodically check in on your overall data health, even when everything is good, Anomalo offers a bird’s-eye dashboard and the ability to set and track key metrics you’ve prioritized.
Don’t let the possibility of data issues discourage you from modernizing your data strategy. It’s very much worthwhile to migrate to the cloud, so long as you are conscious about modernizing your data quality monitoring system as well. For expert help, get in touch with phData, whose specialists can assist your business in setting up a cloud-based data architecture.
With a modernization effort that includes a data quality solution, you’ll wield more data than ever before, be able to take richer actions based on that data, and have the highest confidence in your data’s integrity. To see Anomalo for yourself, request a demo today.
