4 Steps for a Successful Cloud Migration

Data Intelligence

The journey to the cloud is in motion – even for highly regulated and risk-averse enterprises. Cloud migrations require shifting data and infrastructure to new environments like Office 365, Azure, Google Cloud Platform, or AWS – and organizations face a myriad of challenges that can be not only expensive, but introduce unnecessary data challenges and risk.

It’s a complex endeavor – and there’s a variety of data privacy, security, and data management considerations that need to be addressed with the right strategic planning and approach.

How to Create a Successful Cloud Migration Strategy

Moving data to the cloud can bring many operational benefits if the right approach is in place to manage risk. A successful cloud migration starts with the data: organizations need to understand that they are moving the right data to the right place – all while ensuring that the right controls are in place when that data lands in the cloud.

In order to balance compliance, privacy, security, risk, and cost and efficiency concerns, a cloud migration strategy should be based on key initial steps to ensure that organizations inventory, assess, stage, and secure their data.

Privacy regulations add another dimension of complexity: regulated data requires specific protection like access controls, encryption, and data residency stipulations. Organizations need to determine what data they have (along with where it is), what data should be migrated, how to shift environments without disrupting business, and how to do it in a way that not only minimizes risk, but establishes consistent data privacy, protection, and perspective practices moving forward.

Step 1: Map and inventory your data

In order to migrate your data, you need to know what you have first. Map and inventory your entire data environment to plan what data to migrate – and how to do it safely.

BigID automatically discovers, inventories, catalogs, and classifies your sensitive and personal data wherever it lives. Organizations can leverage BigID’s identity intelligence and machine learning to establish an accurate and scalable inventory of all data, everywhere.

Step 2: Clean up data

Cloud migration plans can uncover long standing data quality issues – and these initiatives are an opportunity to clean up and delete redundant or duplicate data (which add unnecessary risk in first place).

With patented ML-driven discovery-in-depth technology, BigID automatically identifies duplicate, similar or redundant data within large volumes of data to minimize and reduce risk on sensitive data before migrating to cloud environments. BigID also uncovers dark data, identifies data linked to an individual (personal information), and can even determine if Social Security numbers have inadvertently been stored in a database column named ‘email’, for example.

The outcome? A clearer picture of whose data you have, what attributes are associated with individuals across data sources (both structured and unstructured), and full visibility and inventory of all personal and sensitive data across the enterprise.

Step 3: Determine what stays and what goes

Data privacy and protection regulations require that specific types of data remain on-prem or have specific controls around it if it’s going to be migrated to the cloud. Identify what data should be migrated – and what should stay – based on the relative value and risk.

Before migrating data to the cloud, classify and tag sensitive and personal data wherever it lives, and identify data that falls under specific regulations by policy. Determine what should be migrated to the cloud in the first place, and apply labels based on classification output for automated enforcement in the cloud.

Step 4: Align policies and safeguards

Regardless of where data lives (in the cloud, on prem, or a hybrid environment) – or what type it is, organizations need a consistent way to classify, tag, and enforce policies on data based on sensitivity, location, and more.

BigID gives organizations visibility into sensitive data and personal data across the data center and cloud, enabling them to apply consistent policy controls and protections. Organizations can then orchestrate protection policies for access controls and encryption before they move data to cloud services.

By integrating with tools provided by AWS, Microsoft Azure and Google Cloud Platform, enterprises can limit the risk that data is exposed in the migration process and ensure configurations are consistent with the relative sensitivity or residency of the data. When organizations are pursuing multi-cloud strategies or maintaining hybrid architectures, the ongoing visibility into the data provided by BigID can ensure that policies are driven by discovery and classification insights.

BigID also works with partners like Ionic, Privitar, Immuta, and SecuPI to allow for orchestration of policy enforcement and controls based on data insights across cloud platforms.

In order to manage security & privacy risk, organizations should independently monitor data in the cloud to enforce policy (and flag violations), extend cloud monitoring tools – such as AWS Security Hub – with privacy policy insights, and leverage access intelligence to identify overexposed sensitive, personal, and regulated data.

How BigID Helps with Cloud Migrations

BigID helps organizations with cloud initiatives by enabling organizations to strategically manage, monitor, and analyze their data wherever it lives, all while preparing to move their data to the cloud.

Organizations can make sure they’re moving the right data to the right place – and ensure that the right controls are in place when the data lands in the cloud. With a privacy-centric approach, BigID empowers organizations to execute a successful cloud migration strategy: from a unified data inventory to data minimization to policy enforcement.