When to Tackle Unstructured Data within Your Organization?

Your organization sits on possibly petabytes of data, which is mostly unused because it is Redundant, Obsolete, or Trivial—i.e., Data ROT. The scale of the problem means that it is impossible for humans to fully understand the scope of what they have in their organization without a machine to assist them. These machines can leverage artificial intelligence (AI) to discover and manage unstructured data and can help improve decision-making, optimize operations, and enhance data storage efficiency.

AI-powered tools can assist in locating, classifying, monitoring, and protecting data sources that hold significant business value. Before you begin to tackle the unstructured data problem, let us look at the big picture - why, when and how to tackle unstructured data.

The Why!

Why Tackle Unstructured Data?

Enrich Existing Data

Unstructured data can be valuable for your organization’s data analytics and increase operational efficiency. Gaining unstructured data context from emails, social media posts, documents, images, and videos can help better understand customers, suppliers, and competitors.

Improve Data Compliance

Through data discovery and classification, unstructured data management can accurately identify and classify files that may contain sensitive information, such as Personal Information (PI) and Personally Identifiable Information (PII) or even regulated financial data. By implementing such unstructured data management practices, you can avoid the risk of costly legal and reputational damage. by accurately identifying and classifying files that may contain sensitive data such as Personal Information (PI), Personally Identifiable Information (PII), regulated financial data, etc. This will enable your organization to maintain compliance, protect sensitive data, and uphold the privacy expectations of your customers, ultimately preserving your reputation and avoiding potential financial penalties.

Optimize Storage

By analyzing and effectively managing unstructured data, you can streamline user management processes, facilitating the monitoring of user access, permissions, and storage of business records.

This knowledge allows you to reduce the storage of unnecessary or redundant data. Furthermore, it can help identify opportunities for data compression, deduplication, or tiered storage strategies, leading to further cost savings in the long run.

The When! 

When to Tackle Unstructured Data?

Here are a few scenarios wherein unstructured data management can deliver significant value:

Data Migration: When moving data from one system or platform to another, there is often a mixture of structured and unstructured data involved. While schema-based analysis of structured data can be important for structured data migrations, building a governed unstructured data migration process will enable effective handling and analysis of unstructured data as it moves from one location to another.

By implementing an AI tool using the latest in machine learning (ML) and natural language processing techniques (NLP), you can pinpoint sensitive files, understand their context, and extract insights during migration. This enables a more comprehensive understanding of the data being migrated, improves data quality, and facilitates a smooth transition of information while minimizing potential data loss or errors.

Data Governance: Knowing your organization's unstructured data estate will help in controlling it. An automated unstructured data discovery tool will give you insight into what kind of files your data lakes and repositories hold, where they are stored, and who has access to them.

Additionally, unstructured data governance can enhance data security processes such as file access management, enabling you to safeguard files that may contain sensitive or personal information. Thereby, ensuring that your organization adheres to relevant laws, regulations, and industry standards.

Data Retention: Sitting on large volumes of data may seem advantageous, but without proper management, it can lead to increased storage costs, compromised data security, and hindered data analysis capabilities. To avoid such negative consequences, deploying a NLP-powered records and retention tool can indeed help manage data throughout its lifecycle, enabling organizations to efficiently organize, categorize, and enforce retention policies.

Data Minimization: By assessing the data being stored, you can determine its necessity and minimize the amount of personally identifiable information (PII) stored or processed. This step involves adopting techniques to anonymize sensitive information, thereby safeguarding privacy. Minimizing data not only reduces potential liabilities but also demonstrates a responsible and ethical approach to data handling.

Pre-Processing of Data During an M&A: Merger and acquisition deals involve the integration of diverse data sources, such as documents, contracts, emails, and financial records, from multiple entities. The initial step in this process is to transform unstructured data into a structured format, facilitating easier analysis and interpretation via dashboards.

By applying advanced AI techniques, organizations can gain a deeper understanding of the files’ content and context. This allows decision-makers to make informed strategic decisions, identify synergies, and mitigate risks associated with the merger or acquisition.

The How! 

A Dedicated Tool For Unstructured Data Management

Data X-Ray can help you harness the power of your organization's unstructured data trapped within on-premise, hybrid, and multi-cloud environments. Powered by purpose-built AI, Data X-Ray can discover and comprehend your enterprise-wide data, including emails, documents, social media posts, images, and videos.

Data X-Ray enables you to locate, classify, and monitor files that exist on-premises and in the cloud. It scans at a rate of 100,000 words per second across various unstructured data formats. The best part? The processed or classified information can be fed into third party tools like data catalogs and data security tools, providing a centralized repository for easy data discovery, exploration, and utilization.

This article has answered three commonly asked questions about unstructured data management — why tackle unstructured data, when to tackle unstructured data, and how to tackle unstructured data. The next article will delve into unstructured data discovery, its importance, best practices, and more. Watch this space.

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