Structured Unstructured Data
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Structured data vs. unstructured data: The two success pillars of big data analysis

Author at TechGenyz Big Data

If you are not living under the rock, you have definitely heard about the data organization with big data that makes the data easily searchable by the human-generated queries or machine algorithms. The analysis of structured data is effortless, but not of unstructured data if we look back a few years ago. This is due to the limited processing capability, high storage cost, and inadequate memory of the systems, which makes managing unstructured data difficult to handle.

This is why the stored unstructured data was least considered, but overlooking the unstructured data means the businesses cannot avail the insights locked up in the unstructured data. The insights are valuable to gain business intelligence, market intelligence, operational intelligence, and pretty more.

In the last couple of years, with the increasing unstructured data sources, and the skyrocketing availability of the tools to organize and process the unstructured data, the businesses have started realizing the benefits from unstructured data analysis. To look at the bigger picture and get better insights, the companies are investing in both structured data analytics and unstructured data analytics.

Before looking at the importance of the two data formats in the big data analysis, let’s comprehend how different the two data structures are:

Structured data

As the name suggests, it’s an organized data that’s uploaded in the relational database (RDBMS) in the defined fields, rows, and columns. It makes easier to enter, store, search, process, and analyze the information using simple data mining tools or the algorithms. Any operation on the data can be done only by specifying the data type and field names. The rigid nature of the structured data format limits its usage to the financial details, inventory control, sales transactions, or web server logs.

Unstructured data

The unstructured data is more like human interactions which have no identifiable structure. It’s a sign that the unstructured data format has structure, but it’s complex to understand, and never fit into the business spreadsheets. It includes text files, emails, social media content, audio, video, images, PDF files, business applications, and so on, which can be human-generated or machine-generated. The massive unorganized data is assembled and processed in a non-relational database like NoSQL.

The difference between the two:

  1. Structured data is organized through pre-defined models or schema, while unstructured data not.
  2. Structured data is usually text only, but unstructured data comprises of text, audio, video, images, and other formats.
  3. Structured data is easy to search, but unstructured data not.
  4. Structured data conforms neatly with the relational database, but unstructured data resides in the non-relational database.
  5. Structured data analytics are matured, while unstructured data analytics tools are in the rudimentary stage and maturing.
  6. The businesses are spilling unstructured data in high amount and at 15 times rate as opposed to structured data, which makes it important to analyze the valuable data.
  7. Structured data is a logical extension of the current analytics done on the internal data of the enterprise, and internal unstructured data is a fundamental learning ground to gain customer insights.

How is both data types an unmatched solution for the businesses?

The counterpart of structured data that’s unstructured data holds everything that even businesses don’t know they wanted. Although the modern tools to measure and quantify the unstructured data are in the nascent stage, they are unboxing the game-changing insights that are allowing the businesses to make great strides in their niche market.

The SQL and NoSQL technologies are leveraged by the businesses to convert the data in RDBMS into NoSQL data format where it’s analyzed through elastic search that provides easy access to all the data. The tools built on the top of machine learning sense the pattern, classify the data and mine the data from the unstructured database to gain the business intelligence.

In this manner, combining, consulting, and querying both structured data and unstructured data enable the businesses to get a unified analytics platform were accessing any type of data and making better decisions becomes painless.

For instance, Intuit website use the next-gen database MongoDB and real-time analytics tool together that makes integrating structured data and unstructured data easier; enables high-performance unstructured data analysis; helps in getting rich customer insights and actionable patterns from more than 500,000 traffic at lower cost; MongoDB can be deployed at speed as opposed to relational databases.

Well, the benefits of structured data are already realized by the businesses, and now the unstructured data analytics are scoring high popularity due to the several benefits they offer. Take a quick look at how leveraging unstructured data enhance business efficiency:

Marketing endeavors can be improved

Machine learning tools have the capabilities to quickly analyze the millions of documents to find out customer behavior. Customer support services are largely implementing machine learning technology to unfold the hidden insights from customer interactions.

The machine learning tools are trained and get mature as they analyze more and more customer interactions. Based on the chat, the tools can identify the customer’s reaction to the products or services, and provide the insights into the products they likely to purchase. The insights from interactions form a ground to send them personalized marketing messages to the customers that uplift the probability of getting more sales and thus, improved ROI.

Analyzing social interactions becomes viable

Many businesses are leveraging social power to improve brand awareness and sales. In social marketing campaigns, analyzing the context of the content is vital for improving sales through social channels. Here, text analytics along with sentiment analytics are leveraged to tap the potential of unstructured data and find out the positive and negative results of the campaign.

For instance, the online shoppers landing on the retailer’s websites from the social channels, mainly prefer to review the other customer’s feedback before making any purchase. This is why marketing campaigns are always tailored to the positive and negative feedback of the customers in order to bring positive results.

Check the Digital communications are in compliance or not

Many times, in the emails or chat data, the companies failed to comply with the regulations, which costs them thousands of dollars as punishment fees, litigation, and reputation hit. With pattern recognition and email threading analysis software, the businesses can monitor and analyze the massive communications for the potential non-compliance.

Volkswagen is the best example of the companies that have implemented the unstructured data analytics to monitor and measure the chats and email threads for the suspecting messages, which has recently saved the company from the huge fines.

Conclusion

The shift towards bringing structured data analytics and unstructured data analytics together is gaining momentum in the big data ecosystem. The businesses are favoring the dramatic shift of considering both data type analytics as opposed to one data format as they are providing returns exponentially due to the powerful insights. The future of data lies in the power of incredible insights that enables businesses to make smarter decisions, and stay ahead of the curve.

This is why accelerated big data universe is now considered as blessings rather than a curse. Bring unstructured data into the big data equation to enable better decision-making and make the business future-proof.

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