What is Unstructured Data? (And How Does It Impact Business Intelligence)

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If you work in the business intelligence or data analytics space, you’ve probably heard the term “unstructured data” at one point or another. Unstructured data analysis used to be far more difficult since it took many hours of labor to manually sort through it.

The good news is that improvements in AI technologies now enable computers to automatically filter unstructured data, saving you a ton of time and enabling teams to make data-driven choices based on potent consumer insights.

But how exactly does it work? In this guide, we’ll break down what unstructured data is, how it differs from structured data, and everything else you need to know about it.

What is Unstructured Data?

Unstructured data can be defined as information or data that is not organized using a premade data model. It cannot be stored in a traditional relational database system, often known as an RDBMS.

Unstructured material frequently comes in the form of text and multimedia. Emails, movies, images, websites, audio files, and other digital media are all examples of unstructured corporate documentation.

It makes up between 80% and 90% of the data created and gathered by businesses, and its quantities are expanding at a rate that is several times faster than that of structured databases.

A lot of data that may be utilized to influence business choices can be found in unstructured data storage. This one, however, has historically been particularly challenging to evaluate. New software tools are developing that can search through enormous amounts of data to find useful and useful business intelligence with the use of AI and machine learning.

Structured data is crucial, but if this kind of data is properly evaluated, it may be much more beneficial to organizations. It may offer a variety of insights that figures and statistics just cannot convey.

Is Unstructured Data Important?

Today, the vast majority of data is unstructured, and this untapped resource is called unstructured data. Unstructured data, when managed properly, may provide a wealth of insights that support data-driven decision-making.

You can automatically handle and analyze unstructured data using machine learning technologies and do it rapidly and correctly. Natural language processing (NLP), a technical innovation, has enabled robots to interpret text as a person would. This enables you to do away with tedious operations like manually routing and labeling issues or browsing social media postings.

Instead, artificial intelligence technology may learn how to automatically extract keywords, names, phone numbers, and locations as well as how to understand viewpoints and purposes and spot themes that are important to your business. Once all of your unstructured data has been classified, you will have access to detailed insights that will help you make informed business decisions.

Use Cases for Unstructured Data

Transaction processing software, which usually deals with structured data, is intrinsically incompatible with unstructured data. Instead, its primary purposes are BI and analytics. One typical use is customer analytics.

For the purpose of enhancing the consumer experience and enabling targeted marketing, retailers, producers, and other businesses examine unstructured data. In order to understand clients better and pinpoint opinions about goods, services, and business branding, they also do sentiment analysis.

An emerging analytics use case for unstructured data is predictive maintenance. For instance, manufacturers can use sensor data analysis to identify potential equipment failures in plant floor systems or field-ready goods before they happen. Using unstructured data gathered from IoT sensors, energy pipelines may also be monitored and evaluated for any issues.

IT system log data analysis reveals consumption patterns, detects resource constraints, and determines the root cause of application faults, system crashes, performance bottlenecks, and other problems. Unstructured data analytics also helps firms comprehend what corporate papers and records include, which is helpful for regulatory compliance initiatives.

Examples of Unstructured Data

Business reports, legal papers, and presentations are frequently printed on paper, submitted as PDFs, or are even sometimes written by hand. Some of these documents may also include spreadsheets, photos, or XML files.

Despite the fact that text files often follow a similar pattern, data is not arranged in a way that allows for analysis without the use of cutting-edge AI technology. Large volumes of unstructured data are present in these papers, but they are frequently ignored since the analysis of them is thought to take too much time.

Another type of unstructured data is emails. Every day, we send thousands of emails, generating enormous volumes of unstructured data. The data contained in each email is unstructured, despite the fact that emails are semi-structured by categories, as in the example below.

In a similar vein, social media may be seen as a source of unstructured data. Emails and social media data are similar in that some of each is structured. For instance, hashtags make it easier for users to look for topics they’re interested in. However, the messages that contain these hashtags are not organized.

How It Differs From Structured Data

Big data includes both structured and unstructured data, as well as semi-structured data. While all three forms of data may provide amazing insights, it’s critical to know which to gather and when, as well as which to evaluate for the information you seek.

Starting with unstructured data, please. Unstructured data, although including numbers, statistics, and facts, is frequently text-heavy or set up in a way that makes it challenging to evaluate.

For instance, posts on social media may include opinions, hot subjects, and feature suggestions. But processing this data in mass is challenging. To obtain useful insights, certain information must first be retrieved, classified, and then examined.

Contrarily, structured data is frequently numerical and simple to interpret. Data is contributed to standardized columns and rows according to pre-set parameters in a pre-defined structured format, such as Excel. Structured data models’ framework is made for simple data entry, search, comparison, and extraction.

Semi-structured data is text-rich and also informally categorized or “meta-tagged,” yet it is also text-rich. Although it is simple to separate this information into its many groupings, the data inside these groups is unstructured.

An excellent example of this is email, where you may search by categories like Inbox or Spam, but the email content inside each category lacks any predetermined organization.

Simply put: Data that isn’t actively handled in a transactional system, such as data that doesn’t reside in a relational database management system, is considered unstructured data. In a database setting, structured data may be compared to records or transactions, such as the rows in a table of a SQL database.

How Can Unstructured Data Become Structured?

It’s possible for unstructured data formats to include intrinsic structural components. Since their data doesn’t lend itself to the type of table structuring needed by a relational database, they are regarded as “unstructured.” Unstructured data can be produced by people or robots, and it can be written or non-textual (such as audio, video, and photographs). Many different types of unstructured data are best stored in non-relational databases.

How Businesses Approach Data Mining and BI Reporting with Structured Data

Although Excel modeling has historically been a popular method for many businesses to use structured data, structured data only provides a tiny portion of information about a company’s consumers and their behaviors. Businesses may now analyze both structured and unstructured data together using BI solutions like Qlik, Microsoft Power BI, and Tableau to quickly provide even more insightful data.

Business executives face several decisions every day. Many leaders in the past greatly relied on their experience when making decisions. They might not be able to determine if they made a wise business move, however, until the sales figures dropped or increased, if they are not monitoring their performance using data.

Leaders no longer have to rely on chance to determine how well their businesses function by using structured and unstructured data. Professionals throughout the firm may acquire deeper understanding of what is and isn’t working in their business by utilizing the most recent business intelligence technologies. Additionally, these leaders may get tailored advice for how to proceed and get the greatest results thanks to AI and ML.

Data provides executives with an additional source of support for their business knowledge and skills. Businesses may engage with consumers more effectively and develop goods and services that better fit their requirements by leveraging data to understand their customers.

Workers may obtain a new perspective on their business processes, seize new possibilities, and change course more rapidly when a decision goes wrong by merging structured data with unstructured data.

The correct tools are the first step in making use of your company’s data. It’s now simpler than ever to standardize, analyze, and display the vital data that your business produces thanks to business intelligence solutions like DashboardFox.

How to Leverage for Business Intelligence

So, how can one deal with the challenges that come with it? Better yet, how do enterprises that use traditional methods for managing such data hit the mark?

Scale should be taken into account first. Unstructured datasets of tens or hundreds of billions of elements are typical in many organizations. These things, things, or files might be anything from a few bytes to terabytes in size (for instance, a temperature readout from a production-line device) (for example, a full-length 8K resolution motion picture).

As more and more resources are needed only to keep a “balance” of servers, file systems, arrays, and other components, managing this size using standard file techniques quickly becomes untenable.

Collaboration is something else to think about. As these enormous unstructured databases are exchanged, their value grows. For instance, scientists from several hospitals have access to the same sizable collection of genetic sequences.

The capacity to communicate enormous amounts of unstructured data across locations, business entities and other entities has traditionally required incredibly expensive replication and governance.

Yurbi as a Solution

To be clear, Yurbi is not a solution to query and bringing structure to unstructured data.

Yurbi, like most BI tools, thrives on structure. Yurbi can query and leverage data in traditional databases, API endpoints, and spreadsheets, but in all cases, structure is a must. It’s key to our approach of building a semantic layer (think of that as a data model) so that Yurbi can automatically create the query code to fetch data, regardless of the source. But without structure, there’s no way to do a data model.

Hopefully some of the ideas in this article can help organizations bring structure to unstructured data, which is the goal. Once that step is complete, Yurbi enters the picture as a great option for your BI and presentation layer, the ability for your end users to leverage this powerful data in a secure and simple method.

Why Yurbi as opposed to all the other BI tools out there?

Yurbi is designed to address this and other challenges that come with it, thanks to its wide range of functions that come with it. From data visualization to embedded analytics, Yurbi is the ideal business intelligence tool for your company’s daily operations.

Data security is at the top of their priorities, and with their constantly updating and innovating security measures and systems in place, you can be assured that your important data will be taken care of professionally for you.

Yurbi is also affordable – with a reasonable pricing scheme aimed directly at small and medium-sized enterprises, you can have the VIP service without breaking the bank. We have a free trial that you can take advantage of to see if we are the right fit for you.

So what are you waiting for? Set up a meeting with us to discuss more on what Yurbi can bring to the table, or we can always offer a live demo session for free. Either way, you are choosing the best for your business.

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