As the volume of enterprise data has grown exponentially, the concept of structured and unstructured data is now at the center of IT departments’ concerns. Over time, it became apparent that enterprise data could be divided into two subsets based on data type, defined primarily by example rather than formal, standard definition.
Structured data was typically found inside databases, ERP, CRM, PLM, directory systems, and other content management tools consisting of people data, financial transactions, clinical trial datasets, etc. On the other hand, the amount of text found in patents, scientific papers, websites, project deliverables, and contracts has led knowledge managers to label them as unstructured data.
And what about the two gray areas that lie in between:
Documents consisting of large, unstructured content are sometimes managed in content management systems to better organize them using categories, metadata, and properties. These types of documents have given rise to the term “semi-structured” data.
Short content includes several pieces of text hosted in social networks, instant messaging systems, or even several columns in database tables. Should they be considered structured data? Unstructured data? Semi-structured? None of these categories? Both ?
So let’s see why this classification attempt took place and why a new approach should be taken to manage them all.
What are these two broad categories for and how are they used?
The main reason there are two categories of data is to better specify the software systems that will handle them best. Starting with Excel and databases in general, many products have been developed to properly handle structured data. At the same time, content management systems (starting with shared drives) have been developed to better accommodate Word documents, PDFs and other textual documents (aka unstructured documents). The list of structured/unstructured document management systems is extremely long, depending on the purpose and expectations of the business. All feature a wide variety of features, abilities, strengths, and weaknesses.
The main difficulty lies in unstructured data
While the content of a database is simply formatted inside the cells of a table, according to a more or less strict scheme, unstructured documents can include hundreds of binary formats written in many native languages.
Database content management is simple once the information contained in the database is identified. Dates are correctly stored in date formats, people’s names are clearly written in the appropriate fields, and money amounts, category names, quantity values, etc. are all stored in the appropriate formats.
If we now consider a plain text document written, for example, in German, Russian or Japanese, how can we identify the same types of named entities (for example, dates, people’s names, quantitative values, etc.) Most of the time, basic search engines allow you to perform a full-text search, but you have to know what you are looking for.
More importantly, it is necessary to read the result carefully to retrieve the precise information that is inside a sentence on a given page of the document, even when the most relevant document is found. This complex challenge is the main reason why unstructured content is very often underutilized in many companies and why many of them claim that their “data driven” strategy is still far from becoming “information driven”.
The interest of using an advanced search engine
With extensive connectivity, search engines can index both structured and unstructured documents to provide access to truly unified information based on all of the organization’s data, regardless of information management system. documents.
Since it is possible to work with any document, the text becomes easily accessible and any user can carry out extensive searches on any piece of information, regardless of its binary format. Built-in natural language understanding technologies eliminate the fear of documents and data written in multiple languages.
Built-in text mining capabilities help identify named entities, so data such as people’s names, amounts, locations, and company names can be easily identified and highlighted for any post- qualitative and quantitative processing. Using machine learning, documents can be automatically organized into categories and user intent can be detected and correlated at the time of search to maximize user satisfaction.
The ability to process both structured and unstructured data goes beyond simple federated search across multiple data sources. A company with an employee directory, a customer relationship management (CRM) system to manage its customers’ data, an integrated management software package (ERP) and several business applications to accurately describe the products, suppliers, manufacturing plants, etc. is probably the most common and significant example.
So the point of a platform is to refine and enrich business vocabularies to enhance text mining capabilities and search functionality to deliver best-in-class enterprise search.
By using proprietary structured data to better leverage unstructured data, the enterprise search platform approach not only enables all employees to search across all enterprise data. ‘business. Additionally, it will improve its ability to structure unstructured data, helping users surface all relevant facts, entities, and relationships previously hidden in the millions of unstructured documents. And that’s what it takes to become truly data-driven.