Organizations across industries — from financial services to healthcare to retail to government and beyond — sit on mountains of data, much of which is sensitive, personal, and highly regulated. Sensitive data may be siloed, come from unstructured sources, live in legacy databases, or present other challenges that stand in the way of effective data identification, mapping, and management.
What Is Data Mapping?
For any data migration, data integration, or data management process, it is crucial that organizations efficiently map their data — or extract data from source files and transfer it to destination files — across various data systems while ensuring accuracy, completeness, and quality.
Data that has been mapped from one system to another must be accessible and usable for data professionals in governance, privacy, and security. These teams need to be able to rely on accurate data mapping at scale to:
- identify and protect sensitive, regulated, and high-risk data — wherever it exists
- generate a single, accurate, reliable data inventory that covers all types of data — on-prem and in the cloud
- agree upon and collaboratively utilize an accessible, single source of truth across the organization
- manage data processes like file access permissions, data lineage flows, data retention, data remediation, etc.
- identify risk in how sensitive data is handled
- produce relevant insights that drive business value, and more.
You can’t protect what you don’t know you have — and can’t find.
Why Data Mapping Matters
While the importance of data mapping is hardly a new concept for businesses, the complexity and volume of the data that organizations collect, process, and maintain is growing faster than the adoption of best practices. Even as data mapping becomes increasingly critical to drive business processes and insights, enterprises still struggle to efficiently, accurately, and scalably generate data maps that can also be easily managed.
Organizations face the absolute necessity of shifting manual mapping processes to automated solutions. Automated data mapping software is required for companies to maintain the increasingly large, labyrinthian data sets they manage.
With this enormous influx of personal and sensitive information — which increasingly grows year over year — enterprises can no longer support the time and resources necessary for manual mapping strategies, nor can they afford the risk associated with the outdated data these processes produce.
Data Mapping for Privacy and Compliance
A growing list of other U.S. and global laws require that organizations safeguard their sensitive data — whether they define that data as “personal information,” “private information,” “protected health information,” “nonpublic personal information,” or any other type.
These regulations include, but are not limited to:
- the EU’s General Data Protection Regulation (GDPR)
- Brazil’s General Data Protection Law (LGPD)
- the California Privacy Rights Act (CPRA)
- the Virginia Consumer Data Privacy Act (CDPA)
- the Colorado Privacy Act (CPA)
- the Illinois Biometric Information Protection Act (BIPA)
- the New York SHIELD Act
- the Health Insurance Portability and Accountability Act (HIPAA)
- the Children’s Online Privacy Protection Act (COPPA)
- the Gramm–Leach–Bliley Act (GLBA)
In the U.S. alone, there are also 50 state-specific data breach notification laws that have specific definitions for what would be considered “trigger data.”
What all these regulations have in common is that they care about data that can be tied directly to an individual — and potentially cause harm if it falls into the wrong hands.
Effective data mapping helps organizations accurately connect personal, sensitive, confidential, and all regulated data to individuals — so they can know not only what data they have, but who that data belongs to. This visibility allows organizations to enact measures to protect that information while deriving insights from it and meeting regulatory compliance.
Comprehensive consumer privacy regulations like GDPR and CPRA also give data subjects rights over their data by supporting data subject access requests (DSARs), deletion requests, and other requests for information that individuals have the right to obtain from an organization that handles their data.
Companies that implement effective data mapping tools can identify data subject records faster — and better respond to DSAR requirements in a timely fashion that meets compliance, maintains customer trust, and supports data privacy best practices across the organization.
Data Mapping Best Practices
Discover Your Data
Start with the data first, implementing a deep data discovery foundation for all your enterprise data — personal, sensitive, regulated, and high-risk data that your organization collects, retains, shares, and processes. BigID automatically finds, maps, classifies, and cleans up all of that data — constantly finding relationships, adding context, and uncovering dark data.
Blend Surveys with Scans
Manual mapping requires that organizations rely solely on survey-based questionnaires for data discovery. This technique is not only very expensive to maintain, but also depends on the individual recollections of multiple data owners. Even those of us with elephant-like memories are prone to error on occasion. Manual mapping doesn’t account for these inevitable gaps and inaccuracies. To combat this, BigID combines the traditional survey based experience with automated, scan-based discovery and classification techniques that can be used to power privacy reporting functions including PIA. This allows BigID to provide a unique data mapping experience supplemented with scans that dynamically update the data map and resulting inventory, validate any existing surveys you may have already done; and support adaptability, modification, and DSARs.
Classify Data with ML-Driven Technology
To implement efficient data flow mapping, organizations need to go beyond regular expressions, which classify data according to patterns. BigID reimagines data classification with purpose-built technology that leverages not only pattern-based discovery, but ML classification based on (neural language processing) NLP and named-entity recognition (NER); AI insight that is based on deep learning; and patented file analysis classification.
Cover All Your Data — Everywhere
BigID empowers organizations to know their data — all of it, everywhere, across all types, in any language, in the data center or the cloud, at rest or in motion — at petabyte-scale. This includes structured and unstructured data, files and documents, images and mail, Big Data, and more — no matter how siloed, hidden, legacy, or hard-to-find that data might be.
Scale and Extend Across the Enterprise
Data grows exponentially, and organizations need a data mapping solution that can scan, discover, define, classify, and monitor data at scale over time. Data maps are living entities that must dynamically add new data sources, account for new standards, and adapt to changes. BigID scales to your business needs, accommodating changes in requirements, structures, and regulations — and gives intelligent insight into the data at scale.
Automated Data Mapping
Automated data mapping technology is a critical part of any organization’s privacy, security, and governance program. In an era of exponentially increasing, sprawling, complex data, companies can no longer rely on manual processes to achieve the level of accuracy that privacy regulations require.
BigID’s flexible, scalable solution gives organizations full data coverage on all of their sensitive and regulated information — everywhere — and leverages a deep discovery, ML-based foundation so companies of any size can efficiently generate accurate data maps. These maps scale to the organization’s needs, create a unified view for data professionals, help achieve regulatory compliance, and more. Schedule a demo to learn more about BigID’s data mapping capabilities — and extensible app framework that enables you to take action on your data.