Whenever I meet people at data industry events and we discuss their environments and the challenges they face, it seems there are two related problems regarding data governance that always show up: managing and governing their structured data, and managing and governing their unstructured data.
Understanding the different types of data your company is storing is essential to developing an effective data management strategy. However, many people I encounter do not understand the difference between structured semi-structured and unstructured data, even with examples, and why they require different approaches for data governance. In this post, we’ll dive into the question of what is unstructured data vs. structured data and semi-structured data.
Structured data is the easiest to explain but the most challenging to search through. Structured data is data that would be inside a database or some sort of data management application. These applications can track the usage and activity and provide versioning back to the beginning of the file’s existence if managed from the start.
Database type applications such as SQL, Mongo, and Caché, to name a few of the popular ones, use an application to collect the data through various data entry points like a GUI or web‐based portal. Data is added to the fields on the user interface and then inserted into various columns and rows in the database. Most websites or data entry applications will collect data into these various database formats.
Now let’s look at unstructured data. Unstructured data makes up the majority of enterprise data–well over 80%, in fact. (You may also be interested in our post on fascinating data growth statistics.) This data is not usable in a traditional database application since single field entry is normally the mechanism to add data to the rows and columns. Unstructured data types are vast; there are applications that can process over 1000 types of unstructured data formats.
Examples of unstructured data types include office documents, text files, image files, PDFs, log files, and application data files like .ini or .dll. A typical user will create and process primarily unstructured data. This is the data that Aparavi is going after.
Now that we understand structured vs. unstructured data, note that some data is considered semi-structured. Semi‐structured data is, as its name suggests, a mix of structured and unstructured data. An example would be an on‐prem Exchange Server. Exchange stores all the email and attachments data within its database. However, an email file can be easily moved or duplicated from your email client by simply dragging the email to the desktop. This creates an .msg file and includes all attachment data. Attachments can be opened within this client and saved to your local file share or desktop. Aparavi can also process this type of data, provided the data has been exported from the structured environment.
The Aparavi Platform processes unstructured data types like office files, text files, PDFs, etc. We can also index any type of file that has selectable text and make it easy to search through and classify those files for purposes of compliance, cost savings, storage consolidation, and more. Selectable text is any text for which you can open a file and drag your mouse cursor over the text to highlight or select. Files that do not have selectable text but have images of text (such as a scanned document) would require an OCR (optical character recognition) application to process the image text data.
As unstructured data makes up the majority of most companies’ data sets and is growing an uncontrollable rates, Aparavi focuses on helping you take control of your unstructured data. Our Platform helps you classify, protect, and optimize your data, regardless of its location.
Data intelligence takes your data and provides the information you need to truly leverage your data’s value and make intelligent decisions on your unstructured data sets. Understanding what you have is the key to getting the most out of your data. Our mission is to provide you with the tools you need to protect, analyze, and process data effectively. This enables you to adhere to data privacy regulations, defensibly delete ROT data, make informed decisions, simplify operations, and save money on your data management. To learn more, contact Aparavi or get started today.