Contact Us
Call us
Offices
Email
United States
+1 646 893 3042
Accounts receivable department
+1 646 974 0772
All other
+32 2 793 02 19
North America: USA and Canada
Collibra Inc.
61 Broadway, 31st Floor
New York, NY 10006 - USA
EMEA: Belgium
Collibra NV
Picardstraat 11 B 205,
1000 Brussels - BELGIUM
View all
Register for access
Register for access
  • Dashboard
  • University
  • Data Citizens
  • Marketplace
  • Product Resources
  • Support
  • Developer Portal
By signing up you agree to Collibra's Privacy Policy.
My Profile
John Smith
name@company.com
Data Scientist, USA
Interests
Cloud-Ready Data
Digital Transformation
Data Governance

Assuring high data quality during cloud data migrations

Geese flying in v-formation representing a cloud data migration

Data is a precious thing that will last longer than the systems themselves, Tim Berners-Lee once famously said. Cloud storage makes this happen, by enabling universal data access that is independent of the systems. 

As a result, many organizations are flocking to the cloud for their data storage. Gartner research predicts that by 2022, 75% of all databases will be deployed or migrated to the cloud

But like any migration, cloud data migration is also fraught with problems, the top challenge being data quality. Unlocking the potential value of accessible, secure data in the cloud needs a dedicated focus on data quality.

Achieving data quality during a cloud data migration

Organizations shifting from on-premises data storage to the cloud want to harness the efficiency and scalability of the cloud to deliver accessible data. Both data producers and consumers benefit from the convenience and performance of the cloud. 

But simply moving to the cloud is not going to help if the data is not trustworthy. What if the data quality is lost in migration? What happens if the data quality is poor in the first place and you bring over poor quality data? How will this impact the business? 

Data quality is the fitness of data to drive trust in business decisions. As data keeps pouring in from multiple diverse sources, assuring continuous data quality gets challenging. Temporary solutions or hasty afterthoughts cannot provide the kind of quality needed for trusted insights. Even when you use sophisticated analytical tools. 

The definition of quality is also evolving beyond accuracy. As Tom Redman says, to be fit for use, data must be “right” (free from defects) and be the “right” data (possess desired features). It takes a multifaceted strategy to achieve that high level of quality during cloud data migration.

Understanding and assessing data quality challenges in cloud data migrations

Migrating your data to the cloud is like moving houses. It needs preparation “before” migrating, monitoring “during” the migration, and verifying the quality and integrity “after” the migration. While most cloud services simplify the actual process of data migration, the real preparation starts much earlier.

The typical challenges in data quality in cloud migration begin with understanding data.   

  • Understanding data: You can leverage cloud platforms to unify data access across diverse sources and systems. Yet just accessing data without sufficient business context makes it difficult to understand and use that data effectively. Without data intelligence, migrating large volumes of data to the cloud is a waste of resources.  
  • Migrating from old data models: Some legacy systems need detailed planning to prevent loss of quality while moving to a newer data model.
  • Managing duplicate records: Data duplication is a common challenge in migration, making it hard to assess which data to retain and its impact. These types of issues need a full understanding of data, including how the data transforms as it flows across systems.
  • Resolving data ownership: While migration sounds like a technical process, people involvement has a large share in migration challenges. When you don’t know who owns what, getting anything done is a mammoth task. Without well-defined roles and accountability, data quality issues can become a burden difficult to shake off.  
  • Prioritizing quality issues: When you are tackling multiple data issues, the smart way to manage them is by focusing on those with high business impact. Now, how would you know which are the priority issues? How do you decide which issues need immediate attention? A quick and reliable impact analysis is the only way to prioritize data quality issues efficiently. Once prioritized, clear data ownership is essential to assign and escalate those issues to the right persons.    

These challenges demand a comprehensive strategy of a strong data governance foundation for your data quality solution.

Migrating data with predictive data quality

Cloud data migration need not be a one-time activity. In fact, you can grab this opportunity to build a quality-first data culture in your organization.

Data quality thrives on enterprise-wide security and privacy implementation with a deeply rooted collaborative framework. Applying predictive data quality, you can automate quality workflows to get a centralized view and better control over data. You can also efficiently audit data with adaptive rules to minimize business disruptions. 

Add data catalog for registering data with relevant business context of definitions, ownership, policies and usage. Complete with data lineage to enable a granular-level impact analysis, and you will find where and how quality issues developed. Getting complete visibility into how data sets are sourced, aggregated and used simplifies reporting for privacy regulations, too. You can classify sensitive data and assign responsible data owners to ensure policy-driven, compliant access.

Building data quality on top of a data governance foundation promotes a shared understanding of data, clearly defined roles and responsibilities, and standardized policies and procedures. A comprehensive enterprise platform with integrated data governance, data catalog and data quality gives better visibility into data for deciding which data to move to the cloud. 

Migrating with the enterprise platform ensures that you identify and migrate critical data, address the quality issues early on, take measures to improve data quality and establish committed participation from all. Automated, proactive insights into lineage and centralized data quality speeds up compliance reporting, auditing and risk management.

Validating data quality after a cloud data migration

Sometimes, data migration can be the epitome of Murphy’s law, where anything that can go wrong will go wrong. Older driver versions, parsing errors, memory issues, connection limits, or even noisy networks can corrupt data. That prompts for post-migration data validation to ensure that data is not lost or altered during migration. 

Yet validating data consistency in two distinct locations is challenging. Typical low-level integrity checks of row or column counts do not confirm that the data is the same. Nor do they not account for schema or value differences. The discrepancy in data types of source and target systems also proves hard to reconcile. 

If testing large data volumes with manual quality rules is constraining your cloud data migration, predictive data quality offers you the best solution. Using automated, adaptive rules, you can quickly perform end-to-end data quality validation after the migration.

With a single click, predictive data quality carries out row, column, conformity, and value checks between your source data storage and target data lake. It can also run checks against high-dimensional datasets to make sure that you don’t struggle with any of your complex data. 

After a successful cloud data migration with the enterprise platform, you will have a catalog of trusted data with policy-driven sensitive data access. Data producers and consumers can then choose the “right” data with the confidence of the data being “right.” 

“A top healthcare organization saved 2,000 hours during their cloud migration with predictive data quality to de-risk their move and pave the way for future data quality initiatives.”

Making your data lakes better with continuous data quality

Data lakes support sophisticated AI-driven analytics to drive growth strategy, maintain compliance, and optimize business operations.  The analytics can be trusted only when the data lakes are trusted. Predictive data quality provides hundreds of quality checks and can keep learning to develop a unique set of checks for you. These checks can also be performed on streaming data, assuring that only high-quality data pipelines power your trusted analytics.

Its unique Spark-based architecture supports multi-cloud, on-prem, or hybrid storage, alerting issues at the source. The ability of self-service push-down fix at source ensures that quality is addressed early on, without any struggle to fix at downstream applications. With streamlined dashboards, you can quickly focus on critical issues supported with an overall quality score.

As more companies move to the cloud for data and analytics, establishing a quality-first data culture is a top priority. Cloud data migration proves to be the right opportunity to get started on it. Enabling continuous data quality maximizes your migration efforts and offers business-ready data pipelines for powering analytics.

Organizations united by data recognize that data access is critical not just for people but for systems and tools, too. Data producers and consumers increasingly include AI and ML tools that work with trusted, compliant, and relevant data. A continuous data quality approach ensures that your BI and other tools are united by trusted data, unlocking your full business potential.

Gartner predicts that spending on cloud system infrastructure services is expected to grow from $63 billion in 2020 to $81 billion by 2022. If you want to leverage the cloud infrastructure, the key is in successfully governing and accessing the trusted data you migrate. A robust enterprise data strategy leverages the synergy of predictive data quality, data lineage, and data governance,  assuring continuous data quality. Not just during migration, but always. 

Related resources

Blog

The 6 dimensions of data quality

Blog

Data quality and data governance: where to begin?

E-book

Predictive data quality and observability

View all resources

More stories like this one

Oct 14, 2021 - 5 min read

How to achieve enterprise-scale data reliability

Read more
Arrow
Aug 26, 2021 - 6 min read

What is anomaly detection?

Read more
Arrow
Aug 20, 2021 - 4 min read

Defining data observability

Read more
Arrow