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The amount of data generated in the digital world is increasing by the minute! This massive amount of data is termed “big data.” We may classify the data as structured, unstructured, or semi-structured. Data that is structured or semi-structured is relatively easy to store, process, and analyze. However, this is not the case for unstructured data. By definition, it has no pre-defined structure to it — for example, images, audio files, and video recordings.

This article discusses the challenges faced while handling unstructured data in various dimensions. As examples, you’ll find a list of tools to help you get started in this domain: database tools, automation testing tools, data analytical tools, and more. Being aware of these tools will help you explore the different dimensions in which entities directly or indirectly work with technologies that support unstructured data.

Data Analytics on Unstructured Data — Challenges Faced

There are multiple challenges faced while working with unstructured data, namely:

•    This type of data is raw and unorganized.
•    It is difficult to find out if the data is relevant.
•    Finding high-quality data is tricky.
•    Searching for info and indexing is a challenge.
•    More processing is required.

It was tricky handling data analytics on unstructured data until modern-era technologies involving artificial intelligence, machine intelligence, etc., came into emergence. Big data tools are now available to support, extract, process, store, and derive business value from the data. 

Additionally, this article will offer examples of tools that projects can use.

Examples of Unstructured Data

Here is a fact that will boggle your mind: Did you know that 80 percent of the data generated on the internet is unstructured data?

The unfortunate and not-so-wonderful fact is that there is still a massive chunk of this data not yet tapped for business value! It is a matter of concern because instead of deriving benefit for the business, the business is failing to extract solutions from the data.

However, the good news is that technology is now developing fast enough to help decode unstructured data into a reality!

So, what does unstructured data look like? Here are some examples:

• Media That Is Rich: For example, weather data, spatial analysis data, and more, comprised of images, audio, and video data formats
• IoT Data: For example, sensor data, ticker info, and more
• Social Media Generated Data: For example, data involving user activity, sentiment analysis, and more

It is indeed a challenge to make associations, comparisons, correlations, and analyze this type of data.

Let us take the example of social media posts. How do you think we can derive value from the generated data? Here are some questions we can ask:

• How many posts are trending on a particular topic?
• How many posts are being liked/disliked?

This type of analysis is straightforward. However, if we were to analyze aspects such as:

• For Facebook — in the comments section, how many people are showing positive emotions about a trending topic?
• For Twitter — an organization may analyze the tweets to understand customer satisfaction regarding a product.

In a use case, such as those mentioned above, sentiment analysis comes into the picture. Sentiment analysis works based on natural language processing (NLP) and machine learning algorithms. They help determine what the emotion is behind the social media posts, based on which, organizations can derive marketing strategies, determine customer satisfaction, and more.

Hence, though handling this type of data is not so easy, we have advanced technologies to help you navigate and support your business decisions. According to the retrieved data, organizations can now make suggestions, correlations, find similarities, and more.

Storing, Processing, and Utilizing Unstructured Data in Multiple Dimensions — Tool Walkthrough

Unstructured data cannot be stored in traditional relational databases and data warehouses, as they do not associate with the row-column type of data. Also, they occupy a high amount of storage space. However, there are tools like those mentioned below which support unstructured data in several dimensions:

• Big Data Tools: For example, Hadoop can store and process ever-changing, complex unstructured data.
• NoSQL Databases: For example, MongoDB is a document-based type of NoSQL database, Redis is a key-value-based NoSQL database, and Neo4j is graph-based.
• Data Lakes: Unstructured data is stored in data lakes as well. Here, data is integrated into its raw format. Companies such as Google, Oracle, and Teradata offer data lake storage solutions.

Here are some popular tools that enable making operations on the data:

• Apache Flume helps to import, aggregate, and move unstructured data into Hadoop HDFS. One can, for example, retrieve a stream of live running data using it.
•  StormStorm also enables the ingesting of unstructured data into Hadoop. This event-based system is based on the concept of bolts and spouts.
• Spark is another alternative to ingest unstructured data into Hadoop.

All these tools offer high availability, scalability, and security, which is vital to organizations.

Also, external platforms such as the following work seamlessly with unstructured data:

• Business Intelligence Software: These tools are capable of analyzing, mining, and reporting to help organizations derive business decisions out of unstructured data. For example, Zoho Analytics and YellowFin are popular tools to help do this.
• Data Integration Tools: These tools go a step further by combining unstructured data from several sources to be later analyzed for business use cases. SAP data integrator, Hevo Data, and Microsoft Azure are some of the popular tools.
• DataOps Tools: When people, processes, and technology work together to deliver useful data to organizations and operations throughout the cycle of data utilization, DataOps comes into emergence. For example, IBM CloudPak for data help supports these capabilities.
• Test Automation Tools: Many new-age tools that support automating test activities also have integration capabilities with databases that support unstructured data. For example, the TestProject tool integrates with the Teradata database, Oracle database,       PostgreSQL database, and more.

Conclusion

Today, big data is growing at a super-fast rate. Among the types of data, tapping the value of unstructured data is one of the greatest challenges due to the complexity involved. Generated unstructured data makes up the vast majority of data and, hence, cannot be ignored as well. Unstructured data is vital for all organizations and businesses — to help make informed decisions and analyze data.

However, we are now lucky enough to have technologies evolving to help analyze and leverage unstructured data to its maximum potential, helping businesses move towards a data-driven ideal. For example, advanced analytics and deep learning can help to recognize content, emotions, and more. Hence, businesses have started using their analyzed data to help them thrive and grow with the mindset of exploring, processing, and utilizing this valuable asset. Thus, we need to continue decoding the unstructured data in all the dimensions we can! Enhancing the data lifecycle is of utmost importance to any organization, and hence, we need to embrace making the best use of this valuable data.