Humans generate 2.5 quintillion bytes of data every day. And that number is growing at a staggering rate. For businesses, this a double-edged sword. On the one hand, this rich data provides 360-degree views of the customer experience, fuels R&D, and creates comprehensive data ecosystems that help you predict, react, and evolve in unprecedented ways. On the other hand, this is a massive amount of data that needs to be governed, and discovering, managing, and classifying data is becoming an increasingly severe headache. The need for companies to have an automated data discovery solution in place is more present than ever as data proves itself to be one of the most valuable, but also most risky, business assets.

Watch the webinar: Automate your privacy program with data discovery

Adding to the complexity is that most companies now face the challenge of having data across legacy on-prem systems, modern SAAS tools, and data lakes. Many organizations also have business operations that rely on third-parties for the transfer of data. Combine these elements with continuously evolving privacy regulations like GDPR, CCPA, and LGPD, along with security frameworks such as ISO 27001 and NIST CSF. Ultimately creating a perfect storm showcasing the need for effective data governance. Other regulations and sectoral specific governance laws for the finance, healthcare, and other heavily regulated industries add additional layers of complexity to the data challenges many companies face.

Challenges from Increasing Data Usage

The data landscape is evolving with new technology such as IoT devices, artificial intelligence, AR/VR, and use cases like Industry 4.0. Plus, the desire to access data more readily combined with events like the COVID pandemic are causing a massive shift to the cloud for organizations of all shapes and sizes. The result is that many organizations end up with a combination of legacy, on-premise, and modern, cloud-based IT systems. And across these systems, organizations are typically faced with the challenge of managing two core data types:

  • Structured data –data that is highly organized in fields and tables, e.g., databases and SaaS systems
  • Unstructured data –free form data that isn’t structured into fields and tables, e.g., files, emails, comments within free text fields

While both create challenges for business, unstructured data can be uniquely problematic by containing hidden and potentially “toxic” data. Consider the use case of numerous sales reps putting open text remarks in a notes field in a CRM tool. These notes might include personal details about the customer and even sensitive information (religion, health, etc.) that can be inferred from the data captured in that field. A traditional approach to data discovery may be to look at the name of that field – the metadata – see that it is titled “Notes,” and assume the data there is relatively harmless. To truly understand the risk of and apply the appropriate governance policies to the data, the challenge becomes not only looking at the name of that field but going deeper than the metadata to parse through and understand the data within.

In addition to different types of data, as more regulations and frameworks are put into place, another challenge businesses are faced with is evolving definitions of personal data that are not always standardized across global regulations. Add to that the many different categories of the data itself, such as personal health information, personally identifiable information, and sensitive personal data, and it becomes critical that organizations have a way to understand which regulations and frameworks apply to which data sets. This often drives the downstream privacy, security, and broader data governance policies that need to be put into place.

How Automated Data Discovery Helps

While it may not be a “silver bullet” that solves all your data problems, a data discovery tool can significantly help organizations know their data, how it needs to be governed, and how it can provide value to the business. However, not all data discovery tools are created equal. It is critical to understand the common mistakes that organizations make when approaching data discovery and the types of solutions to look for to avoid them. We will discuss this and more in part two of this blog series: Data Discovery -Avoiding Common Pitfalls (coming soon).

Watch the webinar: Automate your privacy program with data discovery

OneTrust DataDiscovery provides organizations with data discovery solutions to address today’s privacy, security, and data governance challenges. Contact us to learn more about OneTrust DataGovernance and OneTrust DataDiscovery.

Read more in this series:

  • Data Discovery -Avoiding Common Pitfalls (Coming Soon)
  • Data Discovery for Privacy Teams (Coming Soon)
  • Data Discovery for Security Teams (Coming Soon)
  • Data Discovery for Data Governance Teams (Coming Soon)

Further Data Discovery reading:

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