Information is the lifeblood of organizations, providing valuable insights and competitive advantages. Yet, the sheer volume of data generated every day can be paralyzing.
It’s estimated that around 80-90% of all digital data produced today falls into the category of unstructured data. This vast reservoir of unmanaged, unorganized information holds immense potential – and challenges – for companies in every industry.
What is Unstructured Data?
Unstructured data refers to data that does not have a predefined data model or is not organized in a specific manner. Unlike structured data, which is in searchable format and neatly organized into databases or tables with a clear format, unstructured data does not follow a rigid structure or schema.
Instead, it can take various forms, including text, images, videos, audio recordings, social media posts, emails, and more. Most of the world’s data, including most real-time data, is unstructured, and the ability to properly manage it and act on it presents a big opportunity for companies.
Key characteristics of unstructured data include:
- Lack of Structure: Unstructured data lacks a fixed format, making it challenging to organize or query using traditional databases.
- Varied Sources: Unstructured data can come from diverse sources, such as social media, web content, sensor data, and documents.
- Natural Language: Much of unstructured data is in human-readable natural language, which requires advanced techniques like natural language processing (NLP) for analysis.
- Rich Content: Unstructured data often contains valuable information, insights, and context, but discovering, extracting and analyzing this information can be complex.
- Large Volumes: Unstructured data is generated at an enormous scale, making it a significant challenge for organizations to manage and extract meaningful insights.
Examples of unstructured data include:
- Text documents: Word documents, PDFs, emails, and web pages.
- Multimedia: Images, videos, and audio recordings.
- Social Media: Posts, comments, tweets, and other user-generated content.
- Customer Feedback: Comments and reviews from customers.
- Free-Form Surveys: Responses to open-ended survey questions.
Discovering Unstructured Data in Your Systems
Ensuring data quality, security, and privacy while considering scalability, cost management, and compliance with regulations all add to the complexity of managing data across your systems.
In order to achieve those goals and manage both structured and unstructured personal data, the first thing you need to do is to discover it.
The discovery of personal data in unstructured data sources is a complex task – primarily due to the sheer volume of the data that enterprises have and the extreme heterogeneity of the data itself.
What are the challenges?
With DPM Data Discovery, we focus on textual data since the majority of all data in the company are textual data.
However, identifying sensitive data in textual formats is challenging as there are different formats (.docx, .pdf), languages, alphabets, and conventions (i.e., the use of diacritics) in different sources.
Many companies try to optically locate the information on the page to extract possible personal information, i.e., from headers, using the knowledge of where the information should be.
This method works well for certain types of documents, such as contracts, where we can reasonably assume where the personal data will be found.
However, dealing with emails or social network posts is problematic as they do not necessarily follow a predictable format (besides the obvious sender-receiver structures).
Furthermore, even in documents that are in nature formulaic, such as CVs, there are many different styles and formats that need to be addressed.
How does DPM Data Discovery Tackle Unstructured Data
DPM Data Discovery does not rely on the format of the document to locate personal data.
Instead, in the initial steps, we identify and cluster the documents based on their extensions, as different file types require different processing throughout.
We extract the sensitive information using state-of-the-art machine learning approaches.
In order to accomplish this task, we created our corpora with a specific focus on sensitive data, eliminating the clutter of information that some of the modern Named Entity Recognition (NER) solutions have.
Furthermore, our data discovery is language and script-agnostic, allowing us to extract personal information even from such languages for which NER off-the-shelf solutions do not exist.
By doing so, we again eliminate the need to send any personal data to third parties and allow the process to be carried out in-house.
Maintain an Up-to-Date Personal Data Inventory
Keeping your data inventory up-to-date is crucial to protect your data and have access to the latest changes, updates, and new data.
One of the main goals of DPM data discovery is to automatically label data sources with data domains. Once this is done, a searchable data inventory is established and continually updated.
DPM Data Discovery automates data inventory, ensuring it remains up to date, streamlining the process of identifying new data and providing organizations with a real-time view of their complete data landscape in the cloud or on-premises.
Data discovery results also contain technical information about the scanned data object, the configuration of the data discovery used for scanning, the time of the discovery, and a sample of data.
Automatically Uncover Dark Data & Shadow Processing
Dark data comprises more than half of the data collected by companies. It is estimated that out of all data created daily, as much as 4.12 sextillion GB of data will go dark every single day.
Dark data and shadow processing can introduce risks to an organization, including data security vulnerabilities, compliance issues, and inefficiencies in resource allocation.
Dark data is the information organizations collect, process, and store as a part of their regular business activities. However, it is not used for business purposes like analytics or marketing. Maybe that data is irrelevant, outdated, or incomplete, and often, organizations do not even know it exists.
On the other hand, Shadow processing refers to unauthorized or unmonitored data-related activities conducted outside an organization’s official systems. It often involves employees or departments using unofficial workarounds or external tools to process data.
Tackling Dark Data and Shadow Processing
You are obligated to protect not only the data you know you have but also the data you don’t know you have.
You are exposing your organization to data security and privacy risks if you don’t know what is happening in your systems and what kind of personal data you collect and process.
By combining Data Discovery results with the information from the Data Privacy Manager platform, DPM Data Discovery can automatically find dark data you collect and identify shadow processing.
What makes DPM Data Discovery unique?
- Connects to all standard databases, file share locations, SaaS applications, and other types of data sources
- Works with all file types like text, Excel sheets, PDF, CVS, e-mails, log files, social network interactions, and others
- Labels personal data in any language and any script
- Works with structured and unstructured data