Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Concept
Unstructured data
Summary
Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents.
In 1998, Merrill Lynch said "unstructured data comprises the vast majority of data found in an organization, some estimates run as high as 80%." It's unclear what the source of this number is, but nonetheless it is accepted by some. Other sources have reported similar or higher percentages of unstructured data.
IDC and Dell EMC project that data will grow to 40 zettabytes by 2020, resulting in a 50-fold growth from the beginning of 2010. More recently, IDC and Seagate predict that the global datasphere will grow to 163 zettabytes by 2025 and majority of that will be unstructured. The Computer World magazine states that unstructured information might account for more than 70–80% of all data in organizations.
The earliest research into business intelligence focused in on unstructured textual data, rather than numerical data. As early as 1958, computer science researchers like H.P. Luhn were particularly concerned with the extraction and classification of unstructured text. However, only since the turn of the century has the technology caught up with the research interest. In 2004, the SAS Institute developed the SAS Text Miner, which uses Singular Value Decomposition (SVD) to reduce a hyper-dimensional textual space into smaller dimensions for significantly more efficient machine-analysis. The mathematical and technological advances sparked by machine textual analysis prompted a number of businesses to research applications, leading to the development of fields like sentiment analysis, voice of the customer mining, and call center optimization.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.