Unless you have been hiding under a rock, you will be familiar with the term Big Data which basically refers to a huge amount of data. Several businesses are using big data to their advantage and adapting to the new data analytics software, tools, and technology to make sense from that data. Due to this, the entire big data industry is going to be worth about $77 billion by 2023. Add to this the fact that more than 150 zettabytes (150 trillion gigabytes) of data will need analysis by 2025.

Even with all the glory, there lies a catch. The data collected for big data analysis is not just limited to a single type but can be broadly classified as structured and unstructured data. In order to make huge chunks of data work, it is a must to understand the key differences between these.

What is Structured Data?

Structured data is pre-defined, quantitative data, which is machine-readable. It is mostly about real-world objects and is difficult for people to comprehend at a glance. Basic algorithms can be used to search this type of data, which includes spreadsheets as well as data from various machine sensors. Needless to say, such data is extremely organized. Think of fixed fields, which are easy to detect using various search operations and algorithms, provided that the field name and type are defined appropriately. This also makes the data easy to store, enter, query and analyze.

For instance, analysts use SQL (Structured Query Language) to perform the queries on structured data in the relational databases.

Tools like pivot tables and regression analysis are some common ways to decipher this type of data. This is by far, the topmost advantage of structured data. Some of the common instances of structured data are:

• Meta-data
• Library Catalogues
• Historical Records
• Economic Data
• Legal Data
• Databases

Structured data may bring several benefits to the table, but companies are also looking forward to exploring future opportunities by deconstructing unstructured data. Let’s understand what makes it different from structured data. 

What is Unstructured Data?

Categorized as qualitative data, unstructured data can’t be processed or analyzed using conventional methods or tools. Did you know? Over 80% of all the data that’s generated at present is considered unstructured? And this calls for immediate action by firms. This is a number that is going to be on the rise, especially with the rise of IoT (Internet of Things). Sample this, 95% of businesses need to manage unstructured data, while 40 percent of businesses say they need to manage unstructured data on a frequent basis.

This type of data doesn’t conform to a structured format like a spreadsheet but is immensely valuable in nature. It is closer to human language, something a layperson can understand.

Some of the common examples of unstructured data include but are not limited to:

• Text
• Audio
• Video
• Images
• Sales Interaction
• Customer Interaction
• Social Media Activity
• Satellite Imagery
• Surveillance imagery

Because it doesn’t have a pre-defined model, unstructured data can also be more difficult to deconstruct. This implies that it can’t be organized in relational databases. On the contrary, NoSQL databases or non-relational databases can be the best choice when it comes to managing this particular type of data.

The second method to manage unstructured data is to make it flow into a data lake. This step lets the data be in a raw, unstructured format. To decipher the insights within this form of data, companies need to invest in advanced analytics and expansive technical expertise.

While this can be expensive, the results are worth it. It can also be used in predictive analysis. For instance, instead of getting a bird’s eye view of the customers, mining of the unstructured data can unearth an in-depth understanding of customer behavior. This includes their purchase patterns, buying habits, product preferences, social media posts, call recordings, and so on. Therefore, unstructured data is crucial and can change the entire ball game especially in industries such as digital marketing and sales.

Where the Two Meet

While the volume of big data continues to rise, accurate data is the need of the hours. This is regardless of whether the data is structured and unstructured. Both these sets of data need to be collected by firms, processed and analyzed if they need to gain traction. The benefits can be in terms of reducing operational costs, understanding and observing key-metrics, getting thorough know-how of customer behavior patterns, enhancing products and services, promoting targeted campaigns, etc.

For more insightful write-ups like this, keep a tab on this space as we will be back with more.