What is unstructured data?

Today, the amount of data generated by large business organizations is estimated to grow rapidly, at a rate of 40 to 60 percent per year.

Where does unstructured data come from?

Some sources of unstructured data include:

• Web pages
• Videos
• User comments on blogs and social media sites
• Memos
• Reports
• Survey responses
• Documents (Word, PPT, PDF. Text)
• Unstructured texts
• Transcripts of customer service calls
• Images on the internet (JPEG, PNG, GIFs, etc)
• Media logs

This data is stored in databases, transaction logs, e-mails, voice logs, and so on. It is typically too unstructured, fragmented, and scattered to derive insights from at a glance. Simply storing it as-is does not serve any purpose.

If this data were to be made cohesive across silos and easily accessible across an organization, its patterns decoded, and insights extracted through data analysis, it could provide stakeholders a great deal of valuable information.

An emerging new form of unstructured data is machine data. This includes log files from websites, servers, networks, and mobile applications that record a vast amount of activity and performance data. Companies are increasingly capturing and analyzing data from the Internet of Things and connected devices, even smart sensors on manufacturing equipment.

Storing unstructured data: The challenges

Though stockpiling unstructured data without using it for analysis does not serve any practical purpose, storing it is not that simple either. There can be several problems:

• Unstructured data is literally all over the place, and it uses up a large amount of storage space. As significant chunks of it are in the form of large files like video, audio, and images, they take up large slices of the storage pie chart.
• Compared to structured data, with its compact and neat architecture, unstructured data costs a lot more to keep around or maintain.
• Due to its lack of structure and architecture, running searches, deleting portions, or launching updates in the system is often difficult.
• The larger the amount of unstructured data, the more difficult it becomes to index it.

How can unstructured data be stored?

There are a few possible methods for storing unstructured data:

• It should first be converted to a more easily manageable format. eXtensible Markup Language (XML) is often the format of choice.
• A Content Addressable Storage System (CAS) is used to store unstructured data. This system stores data by accessing its metadata and assigning a unique name to every item or object stored within the data. The object is retrievable based on its content, not its location.
• Unstructured data can be stored in a software system and then used to maintain relational databases. Some relational database systems give the choice of using Structured Query Language (SQL) for submitting queries and maintenance of the database.
• A Binary Large Object (also called BLOB) is a workable system for storing unstructured data. A binary large object is a collection of binary data stored as a single entity in a database management system. Binary large objects are typically images, audio, or other multimedia objects. Sometimes even binary executable code is stored as a binary large object.

Disadvantages of unstructured data

The disadvantages of unstructured data are clear:

• The absence of schema and structure makes unstructured data difficult to manage, in addition to being cumbersome to store.
• Indexing unstructured data is not only difficult, it leaves the door wide open for error due to a fuzzy structure and lack of predefined attributes. Running searches is quite a painful activity, as search results are not accurate enough to be helpful.
• It is also extremely difficult to keep unstructured data secure.

Extracting information from unstructured data

As mentioned earlier, unstructured data is notoriously difficult to tag, index, and read. It cannot easily be interpreted by conventional algorithms. The chances of errors are high. Below are a few strategies that are helpful in mining unstructured data to extract usable information:

• Storing data in a virtual repository such as Documentum allows it to be automatically tagged.
• Running various data mining tools.
• Taxonomy or classification of data gives it structure and hierarchy. This simplifies the search process with its inherent logic.
• Through the use of application platforms like extended online analytical processing ( XOLAP), which is useful in extracting information from emails and XML based documents.
• Tools and techniques used on unstructured data in big data environments include text analytics tools. These search for patterns, keywords, and sentiment in textual data at a highly advanced level. Another is natural language processing (NLP) technology, a kind of artificial intelligence that assesses context and derives meaning in text and human speech. This is accomplished by means of deep learning algorithms that use neural networks to analyze data.

Other techniques used in unstructured data analytics can include data mining, or using machine learning and predictive analytics.

Advantages of unstructured data

Unstructured data is not without its advantages, though. Some of its disadvantages can become more favorable.

Lack of schema allows flexibility

Unstructured data’s lack of schema and architecture makes it less rigid. In fact, it can be highly flexible. This flexibility makes it scalable and unconstrained. Unstructured data is portable.

Richer source of information

Heterogeneity of sources ensures that richer data is captured when in its unstructured format. When analyzed right, unstructured data can have a variety of applications and offer valuable business intelligence insights.

Unstructured data comes in many formats

Datasets can be maintained in a variety of formats. The lack of a uniform storage structure frees analytics teams to analyze and work with all of the available data without having to focus on consolidating and standardizing it first. This lays the groundwork for wider, more comprehensive analyses than might be possible in a more rigid data format.

How unstructured data is different from other data types

Big data contains other kinds of data in addition to unstructured data, namely, structured and semi-structured data.

Structured data

This is the opposite of unstructured data in every way. Structured data presents itself for effective analysis at any time, being organized within a database or similar formatted repository.

The term structured data technically applies to all data that can be stored in a database. It concerns all data which can be stored through structured query language (SQL) in a table with rows and columns. Such structures are characterized by their relational keys and can easily be mapped into pre-designed fields. Structured data is the most processed kind. It is the most uncomplicated and organized way to manage information. Relational data is one example of structured data.

The rigid format of structured data makes it very difficult to scale up. An example would be the transaction data in financial systems and other business applications. In most cases, it usually has to conform to a given structure to ensure consistency in processes and analyses.

Semi-structured data

Semi-structured data is information that does not belong to a relational database. It, however, still has some organizational properties that make it easier to mine and analyze than purely unstructured data. For instance, if metadata tags are added, there is more information and context about what the data contains. XML data is an example.

According to some data management experts, all data, even unstructured, has some level of structure. They contend that the line between unstructured and semi-structured data is a blurry one. Given that unstructured data tends to hold a rich set of insights that data scientists can use to better structure their models, the importance of unstructured data simply cannot be emphasized enough.