How Automated Data Discovery Transforms Data Management and Compliance

Published On: April 9, 2024Categories: Blog

In the digital transformation era, data isn’t just an asset; it’s the heartbeat of business innovation. With data volumes projected to reach 180 zettabytes by 2025, the need for efficient data discovery and classification has never been more pronounced. Legacy, manual data discovery methods are increasingly inadequate for today’s dynamic data environments. A Forrester survey reveals that 95% of IT professionals struggle with visibility across their data estate, with 77% concerned about hidden PII.

Automated data discovery platforms have become indispensable, simplifying the process of identifying, classifying, and governing data at scale. Today’s post explores the essential role of automation in data discovery and its business-wide benefits.

Automated data discovery process

The Essential Role of Automated Data Discovery in Data Governance

Data discovery, the process of identifying, classifying, and analyzing digital assets, is foundational for effective data governance. It enables a comprehensive understanding of data’s location, nature, and usage, facilitating robust data protection measures, compliance with regulations like GDPR and HIPAA, and strategic decision-making. With breach costs averaging $4.45 million per incident in 2023, accurate data discovery mitigates risks and avoids hefty fines.

Automated data discovery is the process of employing advanced technologies, including AI and machine learning, to automatically locate, categorize, and analyze digital assets across an organization’s data ecosystem. Automation boosts efficiency, reduces errors, and significantly enhances data quality and visibility, driving informed decision-making and robust governance.

This is essential for:

  • Increasing Efficiency: Organizations that implement automation in IT report saving 10-50% of time previously dedicated to manual tasks.
  • Reducing Breach Risk: Accurate discovery enables security teams to identify and remediate risks from sensitive data that may be exposed.
  • Ensuring Compliance: Regulations like GDPR, HIPAA, and CCPA require enterprises to maintain visibility into their data estate and protect personal information. Effective discovery helps avoid costly penalties by finding regulated data across structured and unstructured sources.
  • Aiding Decision-Making: Data-driven insights can only be as good as the underlying data. Effective data discovery ensures that decision-makers have access to reliable, high-quality data.
  • Enabling Analytics & AI: Accurate discovery improves data quality and governance, which is foundational for driving accurate insights from predictive analytics and AI initiatives. By 2025, IDC projects that 30% of revenue will be generated by data-driven insights.

Unlocking your data’s full potential begins with an efficient and accurate automated data discovery platform, which simplifies the process of finding, classifying, and storing data, increasing accuracy and consistent data quality. Otherwise, you may struggle to keep up if relying on manual or legacy workflows. Having the right tools and processes for data mining and knowledge discovery is mission-critical.

Overcoming Legacy Data Discovery Challenges

Organizations face myriad challenges in implementing effective data discovery solutions—especially when using legacy tools like Dataguise, BigID, and Varonis. These first and second-generation discovery tools are now outpaced by the volume, velocity and variety of data. They fall short in real-time insight, scalability, and coverage, especially with unstructured data and in-motion data. Their manual dependency, outdated methods, and lack of contextual understanding render them inadequate for the scale and diversity of today’s dynamic data landscapes.

Legacy discovery tools face limitations such as:

  • Manual-Intensive Processes: Legacy tools require significant manual intervention, including data source identification, classification, and validation table construction, increasing operational overhead and the risk of human error.
  • Outdated Discovery Methods: Relying on outdated methods like Regex pattern matching, legacy tools offer a less efficient, reactive approach that increases the risk of inaccuracies and fails to meet the nuanced needs of modern data types and sources.
  • Incomplete Coverage: Legacy tools usually prioritize structured data while neglecting unstructured data like emails, documents, and multimedia files. This limited scope can create blind spots in your data map, diminishing the effectiveness of your data governance and security efforts.
  • False Positives/Negatives: Their constraints in algorithmic sophistication and search criteria often generate a high number of false positives and negatives. Manually sifting through these inaccuracies to identify genuine risks can consume valuable time and resources that could be better utilized elsewhere.
  • Data in Motion: Most discovery and classification tools can handle data at rest but fail to effectively manage data in motion—the countless interactions and movements that occur throughout the data lifecycle.
  • Lack of Real-Time Insights: Legacy discovery tools, designed for periodic scans of static data, struggle to provide the real-time visibility needed to address the dynamism of modern data environments.
  • Limited Scalability: As the volume, variety, and velocity of enterprise data continue to expand, legacy systems often lag behind, requiring disproportionate resources to manage incremental increases in data. This inefficiency can strain your organization’s operations and budget.
  • Lack of Contextual Understanding: While legacy methods may successfully identify data, they often fall short in providing contextual information about how that data is being used, stored, or accessed. This context is essential for risk officers and Chief Data Officers when formulating targeted governance policies and controls.

Difficulty Working with Unstructured Data

Unstructured data, encompassing formats like emails, documents, and multimedia files, presents unique challenges in data governance and security. Unlike structured data, which resides in databases or spreadsheets, unstructured data’s diverse formats and storage locations complicate traditional data management and security practices.

The increasing prevalence of unstructured data across industries necessitates a strategic approach to ensure this data is not only accessible but also secure and compliant with regulatory standards. The complexity of unstructured data, which may contain sensitive or critical business information, heightens the risk of exposure and non-compliance if not managed effectively.

To tackle this, organizations require sophisticated data discovery solutions that go beyond traditional parameters. These solutions must be adept at scanning, classifying, and securing unstructured data, providing granular visibility into where sensitive data resides and how it’s being utilized. By implementing automated data discovery tools, organizations can enhance their data protection capabilities, ensure compliance with stringent data protection regulations, and leverage unstructured data as a strategic asset.

Ineffective Processes for Data Capture and Storage

Legacy data discovery tools often require users to manually select data sources for scanning, a process that presumes users know where all relevant data resides. This method is impractical in the complex and vast data environments of large enterprises and can result in overlooked sensitive or crucial data, leading to gaps in data protection and governance.

The challenge is compounded by the presence of dark data—unutilized information hidden within an organization’s repositories, which can harbor both risks and untapped value. Legacy manual scanning methods are not designed to uncover or manage this type of data, potentially exposing organizations to security risks and compliance issues.

Automated data discovery tools address these challenges by intelligently identifying and categorizing all data, including dark data, across an organization’s entire landscape. This comprehensive approach ensures all data is subject to governance and protection, mitigating privacy and security risks, ensuring compliance, and enabling strategic data utilization.

Compliance and Regulatory Concerns

Ensuring compliance with evolving data protection laws like GDPR, CCPA, and other regional regulations is increasingly complex when your data discovery tool doesn’t offer real-time or comprehensive insights. The inability to keep pace with these changing requirements can expose your organization to legal risks and financial penalties.

Maintaining compliance involves more than just superficial data handling; it requires thorough data discovery and classification to identify and secure sensitive information effectively. Inadequate data management can lead to non-compliance, attracting audits, substantial fines, and reputational damage.

Advanced data management tools are essential for organizations to maintain compliance, streamline audit processes, and manage reporting requirements efficiently. By implementing automated data discovery and classification solutions, businesses can ensure they meet regulatory demands, safeguarding their data and fortifying their compliance posture.

Data Quality and Consistency

Data quality, encompassing accuracy, reliability, and relevance, is crucial for the integrity and usefulness of organizational data. Poor data quality can adversely impact various facets of an organization, complicating compliance with stringent regulatory standards and undermining decision-making processes.

Automated tools for data discovery and classification can significantly elevate data quality. These tools minimize the risk of human error, reduce labor-intensive manual tasks, and eliminate subjective biases that can compromise data integrity. By ensuring a consistent and objective approach to data handling, automated discovery provides a foundation for accurate and reliable data insights.

Maintaining impeccable data quality is not just about operational efficiency; it’s about securing the trustworthiness of data analytics, enhancing compliance posture, and enabling advanced applications like generative AI. Automation in data discovery is essential to uphold the standards of data quality and consistency required for these sophisticated uses, ensuring that data serves as a reliable asset for strategic decision-making and innovation.

The Business Case for Automated Data Discovery

Automation addresses the volume, velocity, and variety of data, overcoming the limitations of legacy tools. Automated data discovery facilitates optimized data management, strengthening security, and paving the way for advanced analytics and AI applications. It transforms data governance, enabling organizations to harness their data’s full potential while ensuring security and compliance.

Business Benefits of Automated Data Discovery

Adopting automated data discovery can help organizations realize significant benefits:

Optimizing Data Management Through Automation

Effective data management is underpinned by comprehensive data discovery. Automated data discovery streamlines the complex task of mapping and understanding the full spectrum of an organization’s data assets, significantly enhancing efficiency and accuracy.

An adept automated discovery platform transcends the limitations of manual methodologies, quickly identifying, categorizing, and tagging data irrespective of its format or location. This automation not only accelerates the data discovery process but also ensures that all teams and systems within the organization have timely access to the necessary data, thereby optimizing operational workflows and data utilization.

Enhancing Security Through Automated Data Discovery

Data breaches not only incur substantial costs but also severely damage an organization’s reputation, with potential regulatory fines adding to the impact. Effective data discovery is crucial for identifying and securing sensitive information across an organization’s IT environment. Automated data discovery goes beyond mere identification; it ensures sensitive data is accurately classified and securely managed, aligning with stringent security and compliance standards. This proactive approach mitigates the risk of sensitive data being mistakenly stored in vulnerable, public-facing, or low-security areas, safeguarding against potential breaches.

By leveraging continuous and comprehensive scanning capabilities, automated data discovery fortifies an organization’s defense mechanisms, maintaining the integrity and confidentiality of critical data assets and ensuring compliance with relevant regulatory mandates.

Optimizing Data for Advanced Analytical and AI Applications

Industries across the board are harnessing data to enhance services, inform decision-making, and deepen analytical insights. Key to unlocking the potential of generative AI and predictive analytics is the foundation of accurate, high-quality data.

Automated data discovery is essential in this context, ensuring comprehensive identification and classification of data, preparing it for effective utilization. Without this foundational accuracy and data governance, advanced analytical and AI-driven applications cannot perform to their full potential, potentially leading to suboptimal outcomes. By ensuring data is accurately discovered and prepared, organizations can maximize the value extracted from their data assets, driving innovation and strategic advantage.

Real-World Impact of Automated Data Discovery

From healthcare to finance and cybersecurity, automated data discovery has proven invaluable across sectors, enhancing data security, compliance, and operational efficiency. It safeguards patient data, enhances fraud detection, and fortifies data breach defenses, demonstrating its utility across sectors.

  • Protect patient data (healthcare): Sensitive data discovery is necessary for healthcare organizations that must meet strict regulations for how data is captured and managed. Failing to properly protect patient privacy can create significant issues for compliance, inviting fines and penalties. Adopting an automated data discovery platform makes sure nothing slips through the cracks.
  • Advanced fraud detection (financial services): Identifying and preventing fraud is a foundational process for any financial services industry. Allowing fraud to go unnoticed can create a host of issues, from harming the bottom line to putting customers at risk. Data discovery combined with advanced AI fraud detection tools helps prevent fraud by better identifying when it might occur.
  • Data breach mitigation (cybersecurity): 2023 saw the highest amount of data compromises on record, with 3,205 incidents compared to 1,802 in 2022. Lacking effective data discovery can allow malicious actors to gain access to sensitive data that are unidentified or incorrectly classified. The 2023 IBM Cost of Data Breach report shows that AI and automation can reduce the data breach lifecycle by one-third, decreasing breach costs from $5.36 million to $3.6 million.

Automated, Accurate Data Discovery with Inventa Inventa automates the complex challenges of data discovery. The platform excels at identifying, classifying, and mapping sensitive data across an extensive range of data sources, both structured and unstructured, handling petabytes of information with ease. By integrating AI and ML, Inventa transcends traditional data discovery methods, offering real-time insights and detailed contextual understanding of data across various environments, from on-premises systems to multi-cloud and mainframe infrastructures. This capability ensures not only the detection but also the granular understanding of sensitive data, enabling sophisticated data governance.

In contrast to legacy systems that are limited to pre-defined data repositories and require manual intervention, Inventa autonomously scans the entire data ecosystem. It proactively identifies and classifies data, providing a holistic view that includes previously unseen or unstructured data, in motion, and at rest.

Inventa’s continuous monitoring and alerting dynamically tracks changes within the data environment, offering a proactive stance on data protection. Its supervised AI feature not only simplifies compliance but also adapts to the evolving scale and complexity of enterprise data landscapes. With Inventa, organizations can advance their data governance, enhancing security, compliance, and operational efficiency.

Key benefits of the Inventa platform include:

  • Network-Centric Approach: Inventa continuously analyzes network traffic to dynamically discover and classify sensitive data, eliminating the need to predefine data locations.
  • Autonomous Operation: Inventa automatically detects and scans new data sources, ensuring constant visibility as your data landscape evolves.
  • Total Visibility: Inventa automatically discovers all data, both known and unknown, structured and unstructured, at rest and in motion, across on-premises, cloud, and mainframe systems.
  • Industry-Leading Accuracy: With an industry-leading 98.6% accuracy rate, verified by The Tolly Group, Inventa reduces false positives and negatives, significantly improving compliance and data quality.
  • Scalability: Built on a modern Kubernetes architecture, Inventa effortlessly handles petabytes of data across on-premises, cloud, and mainframe environments.
  • Contextual Intelligence: Advanced analytics provide deep understanding of data usage, access patterns, and business context, enabling nuanced governance policies.
  • Proactive Compliance and Governance: Inventa’s AI-driven, contextual approach ensures dynamic governance and compliance, adapting to regulatory changes and offering evolving insights.

Download Our Data Discovery Comparison Guide

Automated data discovery is a strategic choice for any enterprise aiming to harness data’s full value while ensuring security and compliance. Step beyond outdated tools with Inventa.

Explore our Technology Comparison Guide to understand how Inventa surpasses legacy systems, enabling secure, comprehensible, and effective data utilization.