Home > Resources > Whitepapers > The hidden value of unstructured data

The hidden value of unstructured data

May 2, 2019

It’s important to understand that not all data is the same. Structured data is most often located in cells in a database, and usually deals with a clear, predetermined business purpose. Instead, unstructured data is most everything else. The most complex portion of unstructured information is textual social media data, news feeds, transcripts, documents, etc. It can’t be easily organized into a database, and it can be ambiguous and difficult to manage because it is characterized by an important trait: human language. Unstructured data is the opposite of structured data: everyday language can contain endless amounts and types of information, is expressed in many different ways, and meaning depends significantly on context. For example, consider that the 500 most common words in everyday language have an average of 23 different meanings. This means that even a simple sentence of just 10 words could have a huge number of different meanings.

Spotlight

Cyber Test Systems

Cyber Test Systems is headquartered in France was founded in 2014 by Gregory FRESNAIS and Laurent CHABENET, two experts of the performance and network security testing industry provides a range of 10 network traffic generators and designs Cyber Test Range / Cyber Range - Cyber Defense Training Center.

OTHER WHITEPAPERS

SQL to NoSQL: Architecture Differences and Considerations for Migration

whitePaper | May 26, 2022

Since their invention in 1970 by Edgar Codd, relational databases have served as the default data store for almost every IT organization, large or small. Today, the most iconic and familiar relational databases include IBM DB2, Oracle Database, Microsoft SQL Server, PostgreSQL, and MySQL.

Accelerating Data Modernization with Azure

whitePaper | May 18, 2021

Next-gen data operations require next-gen data technologies and strategies and Azure is an ideal platform for this modernization to take place. Download this white paper to better understand the strategies, philosophies, and technologies your organization needs to expand analytics abilities, reduce technical debt, embrace AI, and more.

Building a Data Management Strategy for Your Nonprofit

whitePaper | June 14, 2022

In a digital-first world, every organization needs a data management plan. Yet, fewer than 25% of nonprofits report having a plan in place. This means data is infrequently shared across departments and rarely used to make decisions or predict future stakeholder behaviors. A data management plan is fundamental to make an impact and an attainable goal regardless of organization sizeor technological capacity. An effective data management plan takes into account your mission, staffing, time, budget, goals, existing technology, and more.

Data & AI Survey Report 2021/2022

whitePaper | September 2, 2022

This year, unlike previous years, we asked professionals the most difficult elements of a successful data strategy instead of the most important.

2022 EDUCAUSE Horizon Report® Data and Analytics Edition

whitePaper | September 16, 2022

With the 2022 data and analytics edition, we further expand our series of Horizon Reports to focus on an emerging area of practice that is driving institutional decision-making and strategic planning for the future—the trends, technologies, and practices that are shaping the world of postsecondary data and analytics. Based on a methodology that grounds the findings in the perspectives and expertise of a panel of leaders in higher education data and analytics, in this report we summarize the panel’s input on the major trends shaping higher education, including panelists’ reflections on the implications of this research for the future of higher education for particular institutional roles.

Introducing Apache Druid

whitePaper | January 31, 2020

Many companies have invested heavily in specialized enterprise data warehouses (EDW) and Extract, Transform and Load (ETL) technologies to analyze their operational data. But these technologies were never designed to be truly real-time.They were originally built for batch, and that original design limits how real-timeEDWs and ETL can become. They were also designed to support a focused group ofanalysts, not a larger group of employees spanning operational functions, or even partner and end customers.

The hidden value of unstructured data