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The challenges of analysing unstructured data

Analysing unstructured data can be quite a hassle, learn the best practices here.

Analysing unstructured data has the potential to transform business operations with optimised 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 organisations to properly analyse data and get the most value out of their information. So, in this blog post, I am going to explain what the challenges of breaking down and analysing unstructured data are, along with some potential solutions.

What is unstructured data?

Before addressing the challenges of analysing unstructured data, it is important to first explain what unstructured data is. In data analytics, there are two types of data: Structured and unstructured data. While structured data refers to organised information in a database, unstructured data is the opposite, it is the raw data that is not easily categorised in existing databases, and comes in different formats. It is often known as freeform information because it comes in a variety of formats. Common examples of unstructured data include emails and text messages.

Challenges in analysing unstructured data

Unstructured data generates immense business value, but most organisations have not been able to yield insights because there are simply so many challenges involved in analysing unstructured data.

This data cannot be analysed with conventional systems

Unstructured data cannot be analysed with current databases because most data analytics databases are designed for structured data, and are not equipped for unstructured data. Therefore, data analytics experts need to find new methods to locate, extract, organise and store data. Unstructured data comes in different formats and databases that need to reflect the freeform 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 93% of data by 2022. This large volume is going to be a huge challenge in analysing this type of data because the larger the data set, the harder it is to store and analyse data in a way that is timely and efficient. To combat this problem, organisations need systems that can process large data volumes efficiently.

Is it relevant?

Making sure data is relevant is one of the biggest challenges when it comes to analysing unstructured data. Data analytics models cannot make a distinction between causation and correlation. If data analytics models see a frequent connection between two different variables, it will give significant weight to that connection, even if there is nothing of value in that connection. This has a huge impact on the reliability and accuracy of findings.

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, therefore, is not always accurate. For example, Facebook status updates, images and videos all qualify as unstructured data, but that does not make it useful for organisations who are looking for ways to improve sales. Furthermore, much of the data may not be reliable because people have a tendency to exaggerate, distort or be dishonest about their information. If organisations feed this information into their analytics systems, then they will not get accurate findings, which will hurt the company’s fortunes down the line.

Are there any solutions to solve these challenges?

While there is no one-stop solution to solve the challenges of analysing unstructured data, there are some measures organisations can use to tackle these problems. The first step is to invest in the latest technology: Machine learning and natural language processing. These technologies pave the way for a more sophisticated-level of analysis, like sentiment analysis, prescriptive analytics, pattern recognition and cognitive analytics, all of which are better suited for analysing unstructured data.

Analysing unstructured data yields several benefits

Despite the immense benefits of unstructured data, organisations can’t just dive into data analysis with their current infrastructure. First, they need to build systems that tackle these challenges. It might seem like a hazard to invest in technology that can analyse someone’s Facebook images, but the results will be worth the effort. This is because analysing unstructured data will reveal newer and deeper findings that cannot be found analysing structured data alone, as it allows organisations to not only understand what is happening in the organisation but also why it is happening.

Furthermore, unstructured data also makes up the vast majority of data, so it makes sense for organisations to build systems that can analyse unstructured data.

Unstructured data has the potential to optimise your organisation’s performance but it’s difficult to read this data using conventional methods. SAS is fully capable of reading unstructured data and providing valuable insights; with the Selerity analytics desktop you can have access to data analytics features like this and more. Get in touch with us for more information.