Unstructured Data Analysis With Machine Learning


Data scientists divide data into three major groups: structuredsemi-structured, and unstructured. Let’s have a closer look and find out how this data is different.

As the name suggests, unstructured data is information that is not organized into a uniform format, and thus, it is hard to operate. Unstructured data can include text, images, video, and audio material. It is widely used for day-to-day business and marketing analytics.

Most often, data has some semantic tags, but it lacks consistency or standardization, it is referred to as semi-structured.

Structured data is well-organized and can be easily processed. It can be accessed in various combinations and examined with maximum efficiency.

Structured data is information that has been organized into a formatted repository or a database: its elements were made addressable – every entity has a unique ID and a set of characteristics – for more effective processing and analysis. Structured data refers to information with a high degree of organization, while unstructured represent data as is.

But even though structured data seems the only sufficient resource, unstructured data is no less relevant and useful. Even more, data science community prophets unstructured data to be the most significant source of insights in the nearest future. And to effective processing of such data we need advanced technologies as machine learning.


Notable fact: almost all information we used to operate with is unstructured: emails, articles, or business-related data like customer interactions. Unstructured data can be extremely different: extracted from a human language with NLP (Natural Language Processing), gained thru various sensors, scrapped from the Internet, acquired from NoSQL databases, etc.

As the majority of information we can access is unstructured, the benefits of unstructured data analysis are obvious. It can bring many useful insights and ideas on how to improve the performance of the company or a specific service.

If we want a machine to process the data, so the first step is to make it “understandable” for computers. We should build a bridge between human understanding and computer processing. It means that most often human operator processes required data manually and translates it to the format suitable for machine processing.

One of the main problems with qualitative data analysis, however, is that standard databases like Excel or SQL require a certain structure. Unfortunately, unstructured data lacks this structure and traditional ORM (object-relational mapping) software can’t process it properly to fill the database.

But it doesn’t mean that we should forget about this kind of information and lose valuable insights. When you sift the unstructured data, you get details that allow seeing the full picture of what’s going on.

The information you receive after the analysis can become the cornerstone of a successful business strategy since it usually contains essential nuances about customer behavior or current trends. Let’s take a look at an example.


One of the possible scenarios of using unstructured data is an online store. The customers could be divided into three groups: those who left positive reviews on the products they’ve recently bought, people who left negative reviews, and those who didn’t leave any comment.

Quite an undeniable fact: the first group of users has a higher lifetime value as they are satisfied by service and tend to buy more during future sessions.

But the second group is critical too. While analyzing user reviews business owner can gain valuable information about how the service is made (customer communication, shipping, packing, dispatching) and how good are the products they are selling.

This process can be done both manually and automatically. It is a common situation when a small marketplace relies on a data entry vendor, that processes these reviews by hand somewhere in India or the Philippines. But sometimes huge players develop a particular software that not only extracts insights automatically but also tags reviews to be positive or negative.

The extracted insights can be used in different ways:

  1. You can easily plan the demand and order the right quantity of the products according to the season, global trends, and supply chain.
  2. The quality department can analyze if current shipping company delivers in time or not and how this impacts customer satisfaction.
  3. Find “rising stars” across vast catalogs and provide in-time feedback to manufacturers helping them to develop better products.
  4. Develop relevant and personalized loyalty programs or bring new ideas to the existing ones.
  5. Build advanced recommendation system that can recommend related goods to the users according to their previous reviews.

The good idea is to reward active customers for their reviews since they provide a business with one of the best marketing tools – personal opinions. By rewarding such customers and encouraging others to write reviews, you can significantly increase the retention rate and, as a result, improve sales.

To encourage clients to write reviews, you can study the behavior of those customers who leave testimonials and work out an appropriate strategy. User behavior is not only about CTR (click-thru-rate), but also about what pages the user visited, decision-making chain, on-page behavior, etc. And that’s another benefit of unstructured data analysis.

Yet another great examples of how unstructured data can be used in e-commerce (Source)

As you understand, an online store is not the only example. We hope you now know how essential it is to collect and examine unstructured data. You might be wondering which analysis tools can help your business interact with this kind of data? When it comes to dealing with big unstructured data, machine learning is a go-to technology for many data scientists.


The more of qualitative data you gather and don’t process, the less useful it gets and the harder it will be to maintain it. So, it will be smarter to take advantage of it and effectively process the unstructured data as it accumulates.

Step 1: Choose the most valuable sources of information. You should define your goals. If you want to apply the sifted unstructured data to the existing structured repository, it won’t be an easy job to do, but it is possible.

Step 2: Create a robust database you can use to establish new business approaches, as well as advanced and predictive models. But working with the wrong source of information, you can get inaccurate data and thus ineffective patterns.

But let’s make a small step back and bring some form of consistency to the unstructured data. You need to organize it into tables and attributes, as well as add filters because the main difference between structured and unstructured data analysis is that having a structure always makes processing and analysis more natural and more efficient. This step is called data cleansing.

Unfortunately, there are no all-in-one software instruments, that can handle all types of unstructured data. There is no option to buy a software application that covers all your data processing and analysis needs.

Step 3: Find software that suits your needs. Unstructured data processing is not cheap and almost always requires custom software engineering. To facilitate the whole process, scientists use machine learning algorithms for unstructured data that performs a contextual analysis for it.

The ML-powered tool looks for similarities and improves the organization of information. Also, the ontology evaluation helps in detecting the patterns and trends. So, you might get valuable insights at this step, too.


1. Initial data analysis

During this step, our data scientists usually analyze the initial data and its formats to find proper instruments for data extraction. There are a lot of different paid software products, open-source tools, and frameworks that easily handle the specific data.

If consider an example (reviews analysis for an online store) mentioned earlier, we would probably use NLTK (Natural Language Toolkit) library written in Python, and it is used for natural language analysis.

2. Data gathering and sample preparation

It is cool when all the reviews are located in a single database (or any other individual data source), but most often, we first need to collect all the required data from various data sources. Like there are many websites where users leave reviews, and we need to unite these reviews into a database.

When the information is collected, our in-house specialists manually map several samples, that are later used for machine learning model.

3. Data processing and cleansing

NLTK helps our specialists understand what stands behind words. It (with some minor improvements) catches the main points of a review and determines if it is positive or negative.

Quite often, our data scientists manually perform group checks of processed data or train additional machine learning model, that analyses the processed data in search of anomalies and collisions.

After the data is processed, it is time to cleanse the results and built a structured or semi-structured data source. We often use MongoDB for it. The type of outcome data may differ from project to project, as some data types cannot be easily converted to structured format (images, video, audio) and it is cheaper to translate it to semi-structured data that can be analyzed with ease too.

4. Data export

Once the information has some structure and is represented as a database, you can index it to get some insights. Again, there is even free software for this, so the task is preferably executable.

But sometimes our clients want us to build custom interfaces to interact with collected data, and we create custom GUI (Graphical User Interfaces), dashboarding software, and even search engines that operate with MongoDB directly.

This was a brief theoretical review of how unstructured data analysis is performed. As a practical part, we suggest that you check our case study below on how we are processing unstructured data with machine learning. It describes our platform based on Artificial Intelligence that allows extracting data from images, scanned documents, complicated technical schemes, as well as convert it to JSON for easy post-processing.

Case Study: Cloud System for Document Digitization


Utilization of unstructured data is crucial for every company that wants to improve its business processes and get the most out of its own experience. The analysis of qualitative data should take place at the early stages and as regularly as possible. In this case, business owners and marketing specialists will get the required information in time and will be able to respond quickly to specific trends and changes in consumer preferences. It will help to drastically improve customer experience and the overall interaction between the company and its clients.

Of course, the best way to use unstructured data is to coordinate it with traditional structured information. By effectively integrating both data types into business processes, you can take full advantage of them making every customer as valuable as possible and, consequently, increasing the performance and revenue of the company.

Therefore, it’s just the right time to apply machine learning tools to process and analyze all this data in the most accurate way. Of course, it will require time and effort, but not as much as you might imagine. Various ready-to-use solutions can accelerate and facilitate the process due to their simple implementation. And with artificial intelligence on board, you will get streamlined analysis for both structured and unstructured datasets.

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