How to Build an Accurate Data Inventory
Organizations sit on stacks of personal and sensitive data — different types, structured and unstructured, scattered in various locations across the organization and in the cloud, ranging from customer data to intellectual property to third parties data and more.
Knowing your data is the foundation for effectively utilizing and protecting it. But how can enterprises gain this knowledge in a fragmented data landscape of diverse data formats, schemas, and metadata types?
The answer is by building and maintaining a data inventory that meets the demands of the modern, data-driven enterprise — a data inventory that helps inform everything from digital transformation initiatives to aligning with privacy compliance regulations.
Why You Need a Comprehensive Data Inventory
Data is both an asset and a liability for your organization.
As an asset, well-managed sensitive and personal data can inform strategy, justify the direction of resources, enable insights to determine the right business decisions, and build customer trust.
As a liability, data that is inaccurate, incomplete, or inadequate increases risk, makes it difficult to comply with privacy regulations, jeopardizes data and cloud migrations, and can be detrimental to customer trust.
An accurate data inventory, on the other hand, is the first step to turn your data from a liability into an asset. It’s a critical component for compliance, risk reduction, digital transformation initiatives, cloud migrations, and more — and is essential to maintain accurate data quality for ML and analytics.
A successful data inventory requires:
- Inclusion of all relevant data — across structured and unstructured; cloud and on-prem
- Accuracy across the data inventory
- Bringing together a single source of truth for privacy, security, and governance
Data Inventory Challenges
These days, data is distributed across data types, data sources, on-prem, and in the cloud. It’s difficult to get complete visibility into your data across an organization’s entire data landscape.
Having full visibility into your data helps you deal with any underlying risks it might contain or pose to your organization’s security or regulatory compliance. These risks commonly include:
- Poor accuracy
- Unstable integrity
- Incomplete information
- Duplicate and similar data
- Ungoverned data
- Dark data
… and more
How to Build an Accurate Data Inventory
Start with deep data discovery of all your enterprise data — across structured and unstructured sources, on-premises and in the cloud — in order to know your data: the personal, sensitive, and regulated information that you collect, share, process, and store as an organization.
By leading with discovery-in-depth, organizations can create an accurate inventory of all their data that scales across the enterprise. BigID automatically finds, classifies, and catalogs sensitive and personal data — along with data relationships, identities, inferred data, and associated data — while surfacing relationships between data and uncovering dark data.
The resulting inventory gives you a clear view of not just what data you have, but who it belongs to, as well as what attributes are associated with individuals across data sources.
Best Practices for Building an Accurate Data Inventory
Step 1: Inventory all data, across your data landscape
Traditional inventories may not cover all types of data across data centers and the cloud. BigID’s unmatched data coverage provides organizations the ability to inventory all of their data, in any language, across structured, unstructured, Big Data, cloud, and apps — all within a unified solution.
Step 2: Scan vs. survey
Survey-based inventories require that stakeholders have a perfect knowledge of their data in order to manually inventory it — and is deeply inefficient, time-consuming, and inaccurate. Surveys don’t account for adaptability, modification, or DSARs. BigID’s scan-based discovery and classification accounts for all data and generates accurate data maps using auditable data scans — plus validates any existing survey-based inventories.
Step 3: Scale
Data grows at an exponential rate: a successful, strategic data asset inventory requires a solution that can scan, catalog, map, and monitor data at scale. BigID is built to handle large volumes of sensitive, regulated, and personal data — both structured and unstructured — at petabyte scale, giving intelligent insight into the data at scale.
Step 4: Discovery-in-depth for 360° data visibility and coverage
BigID provides deep data intelligence and context around sensitive and regulated data, wherever it’s stored. Organizations can accurately identify personal and sensitive data, understand that data in context and relationships, and incorporate personal information (PI), personally identifiable information (PII), metadata, business terms, and more — all in a single pane of glass.
By gaining full visibility into all of their sensitive and personal data, organizations empower their teams to understand data in context — providing clear, consistent ways to tag and enforce policies based on sensitivity, confidentiality, location, and type.
Step 5: Extend the value of a data inventory
Building an accurate data asset inventory is just the first step: once you’ve established the inventory, take action. BigID’s robust app framework future-proofs your organization’s capabilities and enables privacy, governance, and security teams to take action on data as needed. Reduce risk by applying data retention and remediation processes, ensure and evaluate data quality, automate access intelligence across your organization, and more. Get more from existing investments by extending BigID’s deep data intelligence for enrichment and integration.
A comprehensive data inventory is the first step in operationalizing privacy compliance, automating data governance, and reducing risk. See BigID in action to learn more about how to accurately inventory sensitive and personal data assets with deep data insight — for privacy, security, and data management.
