However, are CDOs data-ready to make the most of unstructured data to fuel their AI and data-related transformational initiatives? The answer is seemingly unexpected. A 2023 survey of 334 CDOs and data leaders reveals that organizations, although enthusiastic about the transformative impact of GenAI, have yet to develop new data strategies that focus on leveraging the technology effectively.
Read on to learn more about the challenges CDOs face in managing unstructured data and the best practices for governing it.
What is Unstructured Data?
Before moving to the challenges and best practices, let’s take a quick look at what unstructured data is.
Unlike structured data, which has a purposeful format, unstructured data lacks a pre-defined data model. As the name implies, it is available in a free-form format, ranging from media files to text documents and markup texts to database files.
As such data lacks a pre-defined format, it is commonly managed in non-relational (NoSQL) databases or data lakes, where it is stored in its native or raw format.
Since unstructured data is available in diverse and most commonly used formats, it is no wonder that, as estimated by IDC, it makes up 90% of an organization’s data. Astonishingly, less than a fraction of this data is used and analyzed.
Learn More About Unstructured Data Here
Top Challenges of Managing Unstructured Data
Traditional discovery and cataloging tools were built primarily for managing structured data. Hence, they fail to provide detailed insights into unstructured data, hindering organizations from leveraging it for analytics, machine learning, or other strategic purposes.
Following are the top challenges that organizations face with managing unstructured data.
Volume and Variety
Unstructured data exists everywhere across an organization’s data landscape, including shadow data assets. Moreover, it speaks in different tongues in that the data is available in varying formats, such as video and audio files, markup texts, source codes, text and image files, emails, etc. The sheer volume and variety of the data make it significantly challenging for organizations to discover and classify the data via conventional discovery and automated classification tools.
Data Quality Issues
To make the most of unstructured data, it is critical that the data is meticulously compiled for accuracy and quality. However, it is easier said than done. To put things into perspective, the same survey reveals that 46% of CDOs and data leaders believe data quality is the biggest challenge that hinders their GenAI initiatives. Data quality is impaired when unstructured data is stockpiled over time with outdated, duplicated, and trivial data. Moreover, it is yet another challenge for organizations to reduce redundant or outdated data, as it requires complex tools to identify such data across hundreds of data lakes and other repositories.
Lack of Data Lineage
The dynamic nature of unstructured data allows it to be swiftly moved across different repositories and cloud environments. As it moves through systems, applications, and departments, it undergoes various transformations. Without clear insights into data sources, it is difficult to track the lineage or verify its integrity and authenticity. Due to cloudy lineage and transparency, organizations face compliance, governance, and security risks.
Compliance & Security Problems
Unstructured data is a privacy and security minefield if it is not managed appropriately. Unstructured data contains high volumes of personally identifiable information (PII), including sensitive information. GenAI applications use this data for training the LLM or fine-tuning its performance. Without proper controls and policies in place to accurately identify sensitive information and redact or encrypt this information can lead to compliance and security threats. Similarly, there are now various data and AI laws that may have overlapping regulations regarding the collection, use, and selling of personal information and the development of AI systems. Without clear visibility of sensitive data and AI models across the environment, organizations fail to implement appropriate security, governance, and compliance controls.
Access Governance Challenges
Governing access control of unstructured data is a significant challenge for mid to large-scale organizations as they have it in their environment in petabyte volumes. Lack of or inefficient access controls could mean risks of sensitive data exposure. Unfortunately, organizations do not have a unified approach to govern access. After all, traditional tools do not have the capabilities to address unstructured data access in silos.
10 Best Practices to Manage Unstructured Data
A piecemeal approach to managing unstructured data can result in more silos, lack of data context across teams, and increased challenges and costs. Organizations must strive for a unified framework to govern unstructured data that includes key capabilities like unstructured data discovery and classification, access entitlements, lifecycle management, data sanitization and validation, and robust security controls.
To begin with, CDOs must implement the following best practices to manage data effectively.
1. Discover Unstructured Data
Effective governance of unstructured data begins with having complete visibility of all your data across all your repositories and environments. Hence, discover unstructured data in all your repositories, including data lakes, enterprise applications, cloud storage, emails, and content management systems. Gain insights into the metadata of your unstructured data assets, such as encryption status, location of the data, owner, size of the data, etc. These insights help security, governance, and compliance teams to drive and implement better data strategies.
2. Catalog Unstructured Data
Organizations must build a comprehensive catalog of their data to gain complete visibility. Data cataloging further allows teams to have a single source of truth. Consequently, every team and department across the business knows the same definition of specific datasets. Cataloging also enables seamless searchability and accessibility of data based on different categories. For instance, legal teams may easily search datasets based on their regulatory labels, or a marketing team may look for the required data based on marketing tags. Therefore, build the inventory by adding tags and metadata to files according to their content and context for relevancy. Or group the files according to departments, formats, or functions.
3. Classify Unstructured Data
Classification enables the discovery and identification of personally identifiable information (PII), including sensitive data, in unstructured datasets. Leverage out-of-the-box classifiers and automate the classification of data based on sensitivity and other important attributes. To go beyond the conventional keyword and pattern-matching approach, governance teams may capitalize on AI/ML techniques and algorithms. For instance, Natural Language Processing (NLP) techniques like text classification, entity recognition, topic modeling, and text mining can transform unstructured data into valuable insights for seamless classification and searchability.
4. Ensure Access Entitlements
Knowing and preserving data entitlements is critical for preventing unauthorized access and sensitive data leakage. Access governance teams must start by identifying users and roles with access to sensitive data, files, and folders in unstructured repositories. Secondly, they must map the relationship of those entitlements between users, roles, and permissions. For GenAI systems, teams must ensure that they preserve the entitlements from source systems while extracting the data and enforce those entitlements within GenAI pipelines or at the prompt level.
5. Track Data Lineage
Monitor the flow and transformation of data across its lifecycle to ensure its integrity, reliability, and transparency. Start by evaluating and documenting the source and usage of data in GenAI and other projects for compliance and risk assessments. Create a visual map that illustrates where the unstructured data originated, how it was processed, such as during LLM training or fine-tuning, and how the end user consumed it. Verify the source and integrity of each response of the GenAI output to ensure transparency and compliance.
6. Curate Unstructured Data
Successful GenAI transformational initiatives also depend on data precision and usefulness. For that purpose, it is important to ensure that the data is high-quality in terms of its accuracy and reliability (precision) and relevancy and applicability (utility) to specific data or GenAI initiatives. To achieve that objective, data teams must curate unstructured data and automate labeling based on its content, sensitivity, and use cases.
There are a number of benefits associated with data extraction. Enhancing data utilization and analysis top the lists. Extracting data from multiple sources allows teams to create a unified view of all their data and make it more accessible for analysis. To ensure efficient extraction, unstructured data must be extracted from every available format, and there are a number of ways to do that. For instance, with high-fidelity parsing, teams can capture a document or file’s visual layout that improves chunking for vectorization and enhances an LLM’s ability to understand the data better. Similarly, Optical Character Recognition (OCR) can be utilized to extract data from images.
8. Run Data Sanitization
Data must go through a careful sanitization process before it is made available to be used in GenAI projects. After all, once an LLM is trained on a specific set of data, it cannot untrain itself. Therefore, when unstructured data is extracted, especially when it contains sensitive data, it should be sanitized using automated masking, anonymization, redaction, and tokenization. It is further critical that the data goes through internal compliance controls to make sure that it doesn’t violate any data or AI regulations before it is used for LLM training.
9. Ensure Data Quality
As discussed earlier, data quality is one of the biggest concerns of CDOs and data leaders that hinder their GenAI projects. To drive meaningful analysis or results out of data or develop ethically sound and reliable GenAI applications, the data should be fresh, unique, complete, accurate, and relevant. Measure data quality by inferring metadata, such as its recency and topic, and evaluating files in-line for freshness and reliability of source.
10. Establish Data+AI Security Controls
Build in-line privacy and security controls around data and LLM interactions. Make sure that the data systems and AI models are properly configured and appropriate permissions are assigned to authorized users only to prevent sensitive data exposure. Formulate and implement policies that cover sensitive data tone, topics, phishing, and attacks.
Manage & Safeguard Your Unstructured Data with Securiti
Conventional data governance tools are not equipped with the necessary capabilities required to govern unstructured data, such as inline data discovery and classification, data quality insights, lineage tracking, or data extraction and sanitization controls.
Securiti Data Command Graph, a key capability of our Data+AI Command Center, helps organizations capture all the important metadata and the relationships between them, providing contextual insights into unstructured data for all key perspectives, such as:
- Data Systems.
- Buckets / Folders.
- Files / Objects / Documents.
- Data Sensitivity.
- Access & Entitlements.
- Internal Policies & Controls.
- Applicable Regulations.
- GenAI Models / Pipelines.
This is the baseline intelligence that organizations need for effective data utilization and enable the safe use of GenAI. Together with the Data Command Graph, the Data+AI Command Center helps organizations:
- Discover files of all types (docs, audio, video, images, etc.). CLOBs.
- Identify file categories (legal, finance, HR, etc.) based on content.
- Gain insights into and automate access and user entitlements.
- Find sensitive objects within a file.
- Map regulations applicable to file content.
- Ensure data quality (freshness, relevance, uniqueness, etc.)
- Track the lineage of files & embeddings used in GenAI pipes.
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