How to Analyze and Process Unstructured Data in 5 Simple Steps

How do you analyze unstructured data?

Enterprises constantly deal with large volumes of data coming in from many sources, such as business documents, emails, and web meetings, usually in the forma of images, videos, and large text files.

Since most of this data is unstructured, it doesn’t fit neatly into relational databases, unlike structured data, therefore making it challenging to manage and analyze.

In fact, according to statistics by Aparavi, 71% of enterprises reported that unstructured data is growing much faster than other business data.

However, unstructured data provides valuable insights to businesses, such as complete explanations, descriptions, and predictions of customer behavior and demand in the market for enhanced decision-making.

So you should know how to analyze unstructured data to help you achieve your enterprise goals and objectives in an optimal manner, while also keeping your data analysis costs down.

In this article, we’ll look at how to analyze and process unstructured data in 5 simple steps.

Let’s get started.

The first step in processing unstructured data is collecting it.

However, the data you collect depends on what you’re looking to achieve. 

Remember, although you may have a lot of unstructured data coming in from internal and external sources like social media and news reports, not all of it will be useful.

For example, if you want to gain insights on your social media marketing campaign, you may want to analyze the hashtags related to the campaign to determine if they are negative or positive instead of analyzing all the generated social media content.

Often referred to as qualitative data, unstructured data mainly contains subjective judgments, opinions, and sentiments regarding your business in the form of text, images, and audio files that most analytics software tools can’t directly collect.

For this reason, the first step on how to analyze unstructured data is to collect it using tools such as questionnaires, surveys, and emails or in real-time through AI technologies. 

Once you administer them to your target audience, for example through your website or on social media, the responses you get are a source of unstructured data. This way, you get better insights on your customer needs and determine the endeavors that may require extra effort for more optimal business performance.

If most of your unstructured data comes from files and documents locked in legacy systems, you can also employ the use of intelligence document processing software to fetch all this information and avail it for your data analysis purposes.

Afterward, secure your data against loss by storing it in data lake repositories in its native format to preserve metadata for future analysis.

This essentially means cleaning unstructured data to make it usable.

Unstructured data often comes with irrelevant and repetitive texts and symbols, like emojis, email signatures, and banner ads that may not necessarily add any value in your unstructured data analysis venture.

How to handle unstructured data best at this stage is by filtering and deleting the unnecessary data because it ends up skewing your data analysis results. If you are still not experienced at this then these data analytics project ideas will get you some hands-on practice on preprocessing and cleaning unstructured data.

Start by creating a copy of the original file. Expand informal or handwritten text to make it more legible so that the valuable information is better captured. 

Next, run simple word processing tasks, such as removing repeated words, URL links, and special characters.

Read through the unstructured text to make sure words are used appropriately then run spell check to eliminate errors you may have missed.

To make your work easier, you can employ the use of AI technologies for processing unstructured data. For example, an email cleaner that automatically removes legal clauses, signatures, and previous replies within a thread so that you only remain with the actual response. 

In addition to that, you can leverage intelligent document processing that uses natural language processing and image recognition to extract only relevant text from large text files of unstructured data and separate facts from opinions.

Although unstructured data is critical for decision-making in your enterprise, its lack of organization makes processing a tad bit challenging, if not overwhelming. 

When manpower is relied upon to extract useful business insights from customer calls, survey responses, and news reports, it takes long hours that could be directed towards core business processes like resource management. 

Structuring unstructured data, in this case, is therefore key to organizing it neatly into columns and rows in predefined relational databases, which makes it easy to access and synchronize across the business.

It is, therefore, critical that you learn how to analyze unstructured data by breaking it down using text analysis machine learning programs that leverage natural language processing algorithms. 

The process of making data clean involves, among other things, prioritizing parts of it, for example tagging parts of speech to classify entities, such as “location”, “gender” or “age”. 

You can also use other unstructured data analysis techniques, such as tokenization, lemmatization, and stemming through content intelligence tools to transform unstructured text into structured data that is easily understood by machines.

By comparing your now structured data to similarly prepared data, you can easily search for patterns and deviations to make interpretations for example on customer demand shifts and new marketing campaign targets..

Once you have all your unstructured data structured, the next step is learning how to analyze the data to derive actionable insights that are beneficial to your enterprise decision-making. 

Depending on your enterprise goals or what you’re trying to achieve with unstructured data analysis, you can at this point calculate the metrics you need, for example how visits to your website and social media, as well as clicks on your ads are translating into sales for a higher ROI.

However, even after processing unstructured data and structuring it, depending on manual effort to analyze it will not give you the best results in a timely manner. Some of the insights may not be visible to the naked human eye.

It is, therefore, essential to leverage content intelligence technologies, such as pattern recognition tools that can determine patterns within the data regarding customer behavior. 

In fact, I’d say that incorporating an operational analytics engine is the best way you can turn big data into big success because contrary to offline analytics, these power real-time decision making.

This way, you can determine if you are targeting the right audience for your products using your content strategies and marketing campaigns. 

You can also perform sentiment analysis on your data to determine customer pain points. Accordingly, use predictive analytics tools to determine the right direction to take regarding the products you put out into the market and get predictions on how they will perform in the future. 

Can you draw actionable conclusions from unstructured data analysis?

YES.

You definitely can, and this is by visualizing the results using data analytics tools to simplify the decision-making process.

Creating visuals, such as charts and graphs makes it easier to compare and comprehend data unlike going through data that is still in the form of text and symbols while trying to make sense of it. 

Instead of creating visuals manually, which is time-intensive and error-prone, leverage an all-in-one business intelligence platform that uses machine learning algorithms to run your analysis, perform automatic calculations and create visuals for you.

This is usually based on topics frequently spoken about by customers, their sentiments, and frequently used keywords.  

As a result, you can easily consume and present the information to staff in your enterprise, draw actionable conclusions on overall trends, and make suitable recommendations based on the data for improved business performance.

One of Phronesis Partners' clients wanted to improve their sales performance, but for that they would have to use traditional tools like Excel to analyze and visualize data to compare their performance with other product categories. 

However, this was not an optimal option for preparing visually compelling presentations. But after implementing Tableau, they were able to visualize the complete data set and present the required insights.

In fact, amazing visualization tools like Tableau enable you to make real time changes by editing and appending additional back-end data.

Unstructured data analysis is essential for decision-making in your business, for example when it comes to audiences to target and directions to take with product marketing campaigns.

However, in its raw format, such as audio transcripts and social media mentions, you can hardly derive any actionable insights to improve your business performance.

It is, therefore, necessary to learn and understand how to handle unstructured data so that you can structure, analyze, and draw conclusions from it.

Having gone through the 5 simple steps on how to analyze unstructured data, I hope you can incorporate them and use business intelligence tools to simplify the entire data analysis process.

Have you tried unstructured data analysis and processing before?

Please share your thoughts in the comments below.