Analyzing unstructured data has the potential to transform business operations with optimized performance, better insights, and higher profits. However, despite its immense potential, unstructured data comes with its share of challenges, which makes it very difficult for organizations to properly analyze data and get valuable information.
It is everywhere that doesn’t fit into the neatly arranged rows and columns of a database. It can be a file server that has become a dumping ground for all of the work documents, Excel spreadsheets, and PowerPoint presentations one’s organization uses.
So, in this blog post, one is going to learn the challenges of breaking down and analyzing unstructured data.
What is unstructured data?
Before addressing the challenges of unstructured data, it is important to first explain what unstructured data is. In data analytics, there are two types of data: Structured and Unstructured. While structured data refers to organized information in a database, unstructured data is the opposite, it is the raw data that is not easily categorized in the existing database and comes in different formats.
It is often known as freeform information because it comes in a variety of formats. A common example of unstructured data includes emails and text messages.
Challenges in analyzing unstructured data:
Unstructured data generates immense business value, but most organizations have not been to yield insights because there are simply so many challenges involves in analyzing unstructured data.
- The data cannot be analyzed with conventional systems
Unstructured data cannot be analyzed with the current database because most data analytics databases are designed for structured data and are not equal for unstructured data.
Unstructured data comes from different formats of a database that need to reflect the freedom state of the data.
- Unstructured data keeps expanding
Unstructured data continues to grow at an exponential rate and experts believe that it will make up over a good percentage this year. The large volume is going to be a huge challenge in analyzing this type of data because the larger the data set, the harder it is to store and analyze data.
- Is it relevant?
Making sure data is one of the biggest challenges when it comes to analyzing unstructured data.
Data analyzing cannot make a distinction between causation and correlation.
If the analyzing models see a frequent connection between two variables, it will give significant weight to the connection, even if there is nothing of value in that connection.
- Not all unstructured data is high quality
Unstructured data can be very uneven when it comes to quality. The lack of consistency in quality occurs because data is difficult to verify and, is not always accurate.
Furthermore, much of the data may not be reliable because people tend to exaggerate, distort, or be dishonest about their information.
Are there any solutions to solve these challenges?
While there is no one-stop solution to solve the challenges of analyzing unstructured data, there are some measures organizations can use to tackle these problems.
A major step is to invest in the latest technology: Machine Learning and natural language processing.
Summing it Up!
Despite the immense benefits of unstructured data, organizations can’t just dive into data analysis with their current infrastructure. Furthermore, unstructured data also make up the vast majority of data, so it makes sense for organizations to build systems that can analyze unstructured data.