Unstructured data management

Unstructured data management

Written by Ana Canteli on 4 March 2024

In today's business environment, the ability to analyze large volumes of data has become a fundamental pillar to enable data-driven decision-making. The need to work with management systems that ensure information security, as well as the quality and confidentiality of data, is more pressing than ever. Today, companies need to be versatile enough to be able to work with both structured data sources and manage unstructured data.

 OpenKM: An All-in-One Solution for Unstructured Data Management

OpenKM document management software offers a wide range of features and functionalities that make it a valuable tool for managing unstructured data. With its KEA (Keyphrase Extraction Algorithm), multitude of text extractors, zonal OCR engine, and AI integration (including ChatGPT and Amazon), OpenKM provides advanced capabilities to organize, analyze, and leverage unstructured data efficiently and effectively. In this post, we will explain all these concepts by showing the scope of OpenKM as a structured and unstructured data management tool.

What is Unstructured Data Management?

 Unstructured data management refers to the process of organizing, storing, and analyzing information that does not conform to a predefined format or schema. This includes a variety of data types, such as plain text, images, audio, video, social media posts, and more. Unlike  structured data that is stored in relational databases or data warehouses, unstructured data does not follow a uniform format and can be more difficult to analyze and process.

Problems and Challenges

Managing unstructured data poses a unique set of challenges, especially in terms of sorting and searching for information. While in structured data classification can be relatively straightforward due to the predefined organization of the data into tables and fields - as in spreadsheets - in unstructured data classification can be a challenge. Unstructured text, for example, can contain a wide variety of subjects and topics, making automated sorting difficult.

In addition, when searching for information, unstructured data can pose problems due to the lack of clear tags and metadata. This can make it difficult to accurately retrieve relevant information and make it harder to find what's needed in large volumes of unstructured data.

Quality and Integrity of Unstructured Data

The quality of unstructured data refers to the accuracy, consistency, and reliability of the information contained in it. This can vary widely depending on the source and process of capturing the data. For example, unstructured text may contain spelling errors, grammatical errors, or inaccuracies in information.

Unstructured data integrity refers to ensuring that data is complete, accurate, and consistent over time and across different sources. This is crucial to ensure the reliability of information and data-driven decision-making.

Privacy & Compliance

The privacy of unstructured data is a major concern, especially in the context of regulations such as the European Union's General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) in the United States. These regulations set strict standards to protect the privacy and security of personal information, including information contained in unstructured data.

The GDPR, for example, requires organizations to comply with certain requirements regarding data management and data protection, including unstructured data. This includes ensuring data security, obtaining proper consent from affected individuals, and complying with notification regulations in the event of a security breach.

Natural Language Processing (NLP) and the Role of Artificial Intelligence

Natural language processing (NLP) and artificial intelligence (AI) play a critical role in managing unstructured data. These technologies allow the extraction of information, the classification of documents, the analysis of sentiment in social media posts, machine translation, among other functionalities.

For example, NLP algorithms can be used to analyze unstructured text and extract relevant information, such as people's names, dates, locations, etc. AI can also be used to automate processes of sorting and searching for information in large unstructured data sets.

Examples of Machine Learning in Unstructured Data Management

Machine learning is used in a variety of applications for managing unstructured data. For example, machine learning algorithms can be trained to automatically classify documents, analyze sentiment in social media posts, recognize objects in images, translate text from one language to another, among other tasks.

In conclusion, OpenKM can be a useful tool for unstructured data management, as it has advanced data processing, text analytics, and cloud storage capabilities. Its integration with AI and machine learning technologies further expands its usefulness and makes it a comprehensive solution for organizations' unstructured data management needs. Request a free trial.