Data Discovery Software

Automatically Discover the Personal and Sensitive Data Hidden in Your Enterprise Data Stores

Because you can’t protect or use data you can’t see

You may think you know, or someone knows, well enough, where all personal information and other sensitive data exists in your enterprise. But 100% of organizations that use Dataguise discovery software find sensitive data they did not know or expect to exist in their data repositories. Some find entire data repositories. That’s data left unprotected and unused, increasing risks and costs without delivering any business value—the reason for storing data in the first place.

It doesn’t matter if your organization has a handful, hundreds, or thousands of data stores. Personal data is everywhere, from file servers and databases to data warehouses and data lakes, both on-prem and cloud-based. Data is shared among employees and partners. A single individual’s personal information may be stored in multiple repositories. Finding personal and sensitive data quickly, accurately, and completely is more difficult than most people think.

When compared to other data discovery solutions, Dataguise has been around longer, supports a broader range of data types and repositories, delivers lower false-positive results, and more reliably scans data at scale. Dataguise gives organizations the confidence to act on data in the best interests of the business and the people who trust them with their data.

Key Capabilities & Advantages

Fast and simple to set up and use

Unlike other solutions that require extensive integration or professional services work just to get started, Dataguise does not require a single line of code to install and deploy on prem—SaaS is even easier. Within minutes it can start delivering fine-grained and aggregated optics into exactly what and where sensitive data resides in your enterprise data stores. It offers more than 80 pre-built policy templates for PII, PCI, HIPAA, CCPA and GDPR elements, or you can define unlimited types and numbers of custom policies with just a few clicks. How easy is that?

Highly accurate to minimize false-positive results

Unlike other solutions that use metadata analysis or probabilistic heuristics to infer or guess where sensitive data might be located, Dataguise scans the data itself, at the element level. It performs deep content inspection using special techniques that incorporate dictionary-based and weighted keyword matches, patent-pending neural-like network (NLN) technology, intelligent contextual analysis, and advanced machine learning to discover sensitive elements more accurately. Fewer false-positive results mean fewer dollars and hours spent protecting the wrong data.

Supports a broad range of data types and platforms

Dataguise can discover any data defined as personal, confidential, or otherwise sensitive by your organization, in multiple languages. That includes structured, semi-structured, and unstructured data, both known and unknown, in relational databases (RDBMS) and structured data stores, NoSQL databases, data warehouses, big data Hadoop platforms, cloud object stores, in-flight data transfers, and on-premises file servers. If you’re storing sensitive data, we’ve got you covered.

Proven scalability to grow with your business and data needs

The scale at which Dataguise can discover sensitive data is unparalleled, whether scanning an entire repository or partial/sample data sets. It is optimized to leverage multithreading and concurrent processing, whether deployed in a centralized or highly distributed infrastructure. Dataguise has been proven in some of the world’s largest and most dynamic IT environments. A typical Dataguise scan is done in a matter of minutes or hours, not months.

Further reading:

  • Handles high volumes of disparate, constantly moving, and changing data with time stamping to support incremental change and life cycle management.
  • Supports a fluid or flexible information governance model that has a mix of highly “invested” (curated) data as well as raw, unexplored (gray) data such as IoT (Internet of Things) data, clickstreams, feeds, and logs.
  • Handles a variety of data stores such as traditional relational databases and enterprise data warehouses as well as non-relational big data sources (Hadoop) and file repositories (SharePoint and file shares).
  • Processes structured, semi-structured, and unstructured or free-form data formats.
  • Provides automated detection and processing of a variety of file formats and file/directory structures, leveraging meta-data and schema-on-read where applicable.
  • Provides deep content inspection using techniques such as patent-pending neural-like network (NLN) technology, and dictionary-based and weighted keyword matches to detect sensitive data more accurately.