Structured and unstructured data: help in processing from AI

Today we live in the era of digitalization, daily observing extremely active development in this direction. Information, data are generated constantly, and in very large volumes. And here there is a serious need for collecting and recording information, its processing, analysis. All this has become especially important for modern business, various enterprises. If you delve into this issue in more detail, you can see that in the course of collecting information, most often we deal with unstructured data, those that have different sources, forms. This is what significantly complicates not only their search, but also their classification. And this causes a number of problems and difficulties in the work of organizations.

At first glance, an effective way to solve such a problem would be to transform all this disparate, diverse information into structured data. They will already have a clear structure, contain specific information. They are easy to find, analyze, and use in subsequent work. And it would be great if, in order to increase the value of data, many enterprises and business representatives could transform unstructured data into structured data. But, alas, it will not be possible to implement this with all information. That is, we need to look for other solutions that will provide a quick and easy way to search, collect, and analyze disparate data.

In today's review, we will get acquainted in detail with what structured and unstructured data are, and highlight the main differences between them. We will give examples of such information so that you learn how to correctly classify it. We will describe the techniques that are used today when processing such information. We will pay special attention to the opportunities that modern artificial intelligence technologies open up for users in the field of processing unstructured data. We will describe the technologies that are used to recognize such information. We will highlight the key advantages of using AI in processing unstructured data. The information provided will allow you to understand this issue in detail and understand how you can solve the problem with disparate information within your business. So, first things first.

What is structured data

By structured data we mean data that initially fits into a predetermined scheme or information model. They are most useful in numerical discrete information, which includes financial transactions, information directly related to sales, marketing activities. Scientific modeling also belongs here.

Most structured data is quite voluminous. They are organized in such a way that users can find them as quickly as possible. This will be facilitated by the presence of corresponding names, addresses, phone numbers, ratings displayed in stars or numbers, bank card numbers or accounts, etc. That is, this will be what can be most easily found in relational databases or queried using SQL.

The most common examples in the category of structured data that are actively used by certain applications will be data on flights, ticket purchases, and bookings. This also includes behavioral factors and customer preferences, which have become the basis for the work of modern CRM systems. In principle, they can be used anywhere that provides related collections of continuous, short, discrete values, both text and numeric. They also provide automated control of product stocks in warehouses, and the operation of ERP systems.

Storage of structured data is implemented in special graph, relational or spatial databases, OLAP cubes, etc. But, regardless of their type, all such information will be similar to each other in such aspects as ease of organization of search, cleaning, analysis. The difficulty here is that you can only structure the data that can be ideally entered into a certain model. If there are even minor differences, you will not be able to implement something like this.

What is unstructured data

The category of unstructured data includes absolutely all data that does not fit into a certain model, those to which standard attributes cannot be applied, be it a flight number, product code, bank account number, etc. If we talk directly about a particular business or enterprise, then this can include everything that will relate to corporate documents, correspondence via e-mail, social networks, data from a video surveillance system, etc. That is, these can be any formats, including images, video recordings, audio files, PDF documents, text correspondence.

Statistics show that in the modern business environment, over 80% of all information will be unstructured. And this means that there is a huge potential for creating significant competitive advantages, however, if used correctly. If we talk directly about the applications that businesses use in their work, this includes chatbots that analyze text user requests and provide relevant answers to them, the information that is used in predicting changes in stock markets and, accordingly, for making the most informed investment decisions.

In practice, unstructured data is actively used when working with related collections of information, files or objects where the indicators can change, or even be completely unknown. They are used in conjunction with software for running presentations, processing text content, programs designed for editing or viewing media files. Very often, structured data is supplemented with unstructured data in order to reveal its essence in more detail. In particular, such additional data as customer reviews, messages on social networks can provide quite valuable insights during transformation into structured formats.

Modern data lakes, data warehouses, various applications, NoSQL databases are provided for storing such information. Unlike structured data, this data provides much more useful information, allows you to comprehensively assess the situation, but at the same time it is much more difficult to analyze. And for many business representatives, enterprises, this becomes a serious problem. In particular, depending on the context, the available tools will have to choose the most appropriate technique for analysis each time. This is the only way to get truly voluminous and useful information.

The main differences between structured and unstructured data

Now that you have a general understanding of what structured and unstructured data are, let's look at their key differences:

1. A large number of practical advantages of structured data over unstructured data. The most significant advantages of the first option include ease of searching for information, the ability to use it for any machine learning algorithms. This is what significantly simplifies the work of various organizations, enterprises, business representatives in the process of interpreting information. In addition, the modern information technology market offers a huge number of automated tools designed to process and analyze such information. At the same time, unstructured data is much more difficult to process. In particular, it requires deeper, expert knowledge from specialists. In addition, it will be necessary to use more complex additional tools, which also require appropriate training and knowledge. This is what reduces the availability of unstructured data processing.
2. Features of structured and unstructured data management. Here, as in the previous version, high simplicity and convenience in managing structured data is ensured by their predictable and organized structure. All hardware and software, including programming languages, data structure and directly computer equipment will be able to understand and perceive structured information much easier. This means that when working with it, you will encounter a minimum number of errors, complexities, inaccuracies, and also guarantee yourself an excellent result at the output. Again, working with unstructured data is always a huge amount of work, selection of a set of tools and methods to obtain a good result in a particular case. Storage of unstructured data is also an additional complexity. Here, as well as during processing, the transformation of huge amounts of information is required. To start such processing, it is initially necessary to provide for a breakdown into simpler and more understandable components, which also requires additional time and effort.
3. The difference in analytics of structured and unstructured data. Since structured information is always strictly formatted, it is much easier to process. If desired, you can easily apply program logic to it, thereby finding certain records or information. Moreover, it will be possible to easily create, edit records or delete them if they are no longer needed. All this allows us to confidently state that the entire process of automating data management and their subsequent analysis will be as simple and efficient as possible. Its efficiency will also be at a high level. The situation with the analysis of unstructured data is completely different. The main difficulty here is that there are no predefined attributes that the system could focus on. In addition, analytics in this case often involves the need to use complex, multi-level algorithms for preliminary processing and subsequent analysis. As a result, solving such problems requires the involvement of specialists who are well versed in the issue of data parsing, can use advanced tools, methods not only for collecting information, but also for its subsequent analysis, structuring, extracting those data that will be truly useful and valuable for business. That is, analytics is possible in both cases, but when working with unstructured information, it will be more complex and demanding of resources.

That is, structured data wins on each of these points. They are endowed with a large number of advantages, they are easier to manage, they are easy to analyze. And here everything is basically clear and reasonable, because structured information will be elementary in organization, analysis. And everything would be fine if its volume in the general flow of information was large. But, alas, this is not the case. Yes, this is important information, such as phone numbers, product codes, dates. But still, the bulk of such information on a global scale is unstructured. Therefore, it is in your interests to find the most effective ways to work with it. And one of the secrets here will be to ensure that it is organized as neatly as possible and quickly accessible.

Some examples of structured and unstructured data

In the total amount of information presented on the modern market, it is difficult for a person who is not very familiar with the specifics of this issue to figure out which of the data he sees in front of him, which he encounters in his daily work, will be structured, and which will not. To simplify and speed up the process of getting to know both types of data, we will give a number of illustrative examples.

Several examples of structured data

Despite the fact that, in principle, the amount of structured data is quite small in the total amount of information, they are very important. In addition, they are found almost everywhere. Here are just a few of the most common categories:

• Time and date. To ensure work with such data, you will only need to select the optimal structure for yourself. This is what will simplify their reading and analysis by software tools and corresponding machines. Alternatively, the date can be set in the “day-month-year” format or vice versa "year-month-date", which is more common in European countries and the USA. But for setting time, the universal structure will be "hours-minutes-seconds".
• Contact information, buyers' names. Providing such information is a mandatory step in subscribing to a particular service, placing an order for a product. That is, you will fill out a standard form, indicating your name, email address, phone number or a number of related data. All this will be collected by the system and stored in a structured form convenient for subsequent use. This will make it as easy as possible for managers to navigate a large amount of data, automatically extract information about a specific client from the system with literally one click when such a need arises.
• Information about promotions. Most of this information concerns such information as the promotional price, the volume of goods that fall under this offer, market capitalization. Such information is necessarily systematized by online stores or enterprises, and it is updated in real time, that is, simultaneously with how certain goods are sold or, conversely, are added to catalogs.
• Various types of financial transactions. This includes the transfer of funds between credit cards, money transfers, bank deposits, and much more. Each of these transactions will contain the most accurate information, which is a set of important data on each movement of money. That is, the date of the transfer, the exact amount, the serial number, as well as the data of the parties involved will always be indicated here.
• Location. Any geolocation data, whether it is IP-address of the user's device, GPS coordinates, and other similar data is widely used in most applications available to modern businesses. In particular, they are used in various navigation systems and even in marketing campaigns aimed at promoting local businesses.

Of course, along with these, there are also more narrowly focused structured data, but there is no point in listing them here, since they will be relevant for a fairly limited market segment and business processes. We have provided examples of the information that is necessarily structured and used everywhere.

Several examples of unstructured data

Here we will talk not so much about individual areas, but about groups of unstructured data, because this category is extremely broad. Thus, the following types of information deserve special attention:

• Text files. This includes word processing files, PDF files, spreadsheets, presentations, reports, advertisements, etc. That is, it can be anything that provides text content.
• Emails. A fairly popular and in-demand format of structured data, which is widely used by modern businesses. Electronic mailings are also quite actively used by individuals for personal communication.
• Internet platforms. This includes absolutely all website content, including video hosting and other platforms. That is, everything you will see when you visit them is unstructured data.
• Mass media. Here, we include audio recordings, videos, digital images and many other non-textual information in this category of information. They are also mostly presented in an unstructured form.
• Social networks. In this case, all the information that is created on these platforms will be unstructured.

Again, these are only the most common directions and varieties, although in reality their volume is much more significant. But is it possible to analyze all this somehow? What tools and solutions can be used in practice?

Techniques and tools for analyzing structured and unstructured data

Now we will separately consider those techniques that modern business can use in practice when processing both structured and unstructured information.

Methods for analyzing structured data

Due to the increased simplicity of the upcoming work, a fairly wide range of tools and techniques can be used to analyze structured data:

• Data warehouses. Such warehouses are capable of collecting data from various sources. Their main advantages include the ability to support even fairly complex analysis and unusual queries.
• Structured Query Language, that is, SQL queries. With the help of such a tool, you can quickly extract data and manipulate it. But it is important to understand that all this will only be relevant for the information that is stored in relational databases.
• Machine learning algorithms. You can use this technology to process structured data as quickly as possible, identifying patterns and similarities in it. Thanks to this, you can make forecasts with high accuracy, increasing the effectiveness of certain strategies.

Structured data is inherently accessible to a wide range of users. It is quite simple to understand, it can be easily manipulated, stored, extracted as needed, and analyzed. As a result, the decision-making process is significantly accelerated. Moreover, the structured data system can be scaled to your own business processes, thereby adapting it to the processing of impressive volumes of information, which will be the key to excellent performance and stability in operation.

Methods for analyzing unstructured data

In modern practice, the following technologies are used to process and analyze unstructured data:

• NLP technology, that is, natural language processing. With its help, you can extract quite important information and insights even from impressive volumes of unstructured information.
• Data lakes. They are used to store unstructured data. They are great for placing information in its original format until it needs to be extracted and launched for analysis.
• Machine learning. Today, you can use special algorithms, setting them up to recognize certain patterns in an unstructured data stream, such as audio and video files, images.

But in any case, the analysis of unstructured data will require certain knowledge and skills from the performer, additional tools, techniques, more powerful computing resources, and capacious storage. It will be more difficult to implement. In addition, it is possible that unstructured information may contain inconsistencies, inappropriate information, and obvious errors. This is what negatively affects its final quality. If you manage to optimize data entry, you can significantly improve the overall process of managing such information, as well as its analysis.

In some cases, transforming unstructured data into structured data will provide significant assistance. This can be achieved during the analysis of customer feedback, as an option, by setting moods or trends in customer satisfaction as a trigger. You can also structure medical records, including various transcripts, notes. This is what will allow you to set up high-quality integration with electronic medical record systems and improve patient service in general. In the field of Internet marketing, it is possible to structure information from surveys, focus groups, identifying current market trends and patterns in user behavior.

Efficient processing of unstructured data using artificial intelligence technologies

Today artificial intelligence is actively integrated into various areas of human activity. Platforms for processing and managing data are no exception. Moreover, its capabilities also include solving the most common problems associated with the collection and analysis of unstructured data. It is this feature that has led to the fact that many enterprises and business representatives use neural networks to modernize their own methods of processing unstructured information. Thanks to this, you get:

1. More extensive useful information that can give your company a number of significant competitive advantages. This is due to the fact that with the help of neural networks, it is possible to collect a huge amount of information from various sources, automatically compare them with each other, analyze them. As a result, you get the most detailed, voluminous picture, which will ultimately help you make a decision that will be the most appropriate and effective for your business. For example, the same analysis of user reviews, completed purchases, recordings of telephone conversations between potential clients and managers, their correspondence - this is what will allow you to learn much more about your clients and ultimately make personalized offers for them, ultimately receiving more orders and, as a result, income.
2. Quick and easy decision making. A better understanding of the state of the market as a whole, the requests and interests of your potential buyers - this is what will allow you to lead your business in the right direction, avoid mistakes of unnecessary material costs. By using artificial intelligence, you will receive detailed, consistent reports from a huge array of unstructured data that you can perceive literally at a visual level. By analyzing them, you will be able to accurately predict market trends for the foreseeable future, understand what the end consumer expects from the business, identify existing gaps and errors in work, assess potential risks before launching a new product, opening a local representative office and developing the business as a whole. Thanks to this, you will be able to develop a strategy that will allow you to strengthen your position in the market, to compete with other companies operating in the same niche as you. It is convenient that in this case, the author can develop a development strategy for both a separate area and the business as a whole.
3. High personalization rates. Surely, as a business representative, you understand how important it is to understand your customers, their wishes for the product, the level of service. The deeper you delve into all this, the better you will be able to satisfy the requests of potential buyers, which will ultimately affect their level of satisfaction, will contribute to repeat orders, positive reviews, recommendations. Having access to detailed information about the behavior of the audience, you will be able to make personalized offers, which is guaranteed to justify the efforts and investments.

That is, artificial intelligence — This is the tool that can significantly simplify your work on collecting and processing unstructured data, and at the same time help to reach a completely new level of work, both at the micro and macro levels.

TOP 5 AI tools for working with unstructured data

Today, we can highlight the following tools for processing unstructured data based on artificial intelligence, which are actively used by businesses:

1. Natural language processing. The NLP technique can be implemented in relation to any unstructured sets of text information, allowing you to recognize names, titles, perform generalization, and thematic modeling. Also today, this technique is actively used to translate materials, generate new texts.
2. Machine learning. This direction allows you to identify trends, patterns, and outliers in unstructured data. Artificial intelligence can analyze the information received and, based on this, predict future developments, identify current trends in the markets, potential problems, and consumer behavior. It will also be possible to predict future sales volumes with high accuracy.
3. Computer vision. This technology allows neural networks to analyze various images, classify the images, objects, and scenes presented on them. This greatly simplifies the recognition of faces and markings. It is also possible to identify certain objects. Similarly, neural network algorithms can process video materials with graphic content, extracting relevant information from video streams.
4. Extraction templates. In this case, we are talking about business representatives creating special templates that take into account the specifics of their company and the market as a whole. As a result, neural networks will analyze large volumes of unstructured data and extract from them only the information that will correspond to previously created templates. The only caveat is that manual creation of such templates will take a lot of time. The main difficulty is that they need not only to be developed, but also tested to ensure that they work correctly. But today, these tasks can be easily automated and simplified using the capabilities of artificial intelligence.
5. Understanding the context. Modern artificial intelligence models analyze unstructured information not in a closed environment, but can interpret it depending on the overall picture. In particular, behavioral factors of the audience, its location, and viewing patterns can be taken into account as additional factors. This is what will allow you to delve deeper into the context and ensure its understanding with high accuracy.

Each of these tools based on artificial intelligence technologies can significantly speed up and simplify the process of collecting and processing unstructured data. As a result, the business gets access to truly valuable and accurate information in the shortest possible time, literally in real time.

Why should you trust the processing of unstructured data to artificial intelligence?

If you are still only thinking about the issue of including artificial intelligence technologies in the processing of unstructured information, then do not postpone the implementation of this idea for a long period of time. The fact is that this is one of the most effective and efficient ways to simplify the processing of unstructured data. In particular, in this way you can ensure yourself:

• Higher efficiency of work processes. Processing of unstructured data will be carried out much faster than an ordinary person can do. And this means that relevant information will appear at your disposal literally in real time, allowing you to quickly make important strategic decisions.
• High adaptability. All these technologies that machine learning uses today, as well as artificial intelligence in general, can be easily adjusted to the specifics of a particular business. Moreover, thanks to constant feedback, the receipt of new information will lead to a gradual accumulation of useful information. This is what will become the basis for high reliability and accuracy of data in dynamic environments.
• Accuracy of the information received. Modern neural networks will be able not only to collect, but also to analyze data based on pre-specified templates and requirements. This is what minimizes errors, increases the accuracy of the data received, and increases the reliability of the results.
• Relevance, innovation. Using the capabilities of artificial intelligence in processing unstructured data, you will be able to look at ordinary business processes from a different angle, making quite non-standard, and in some cases even innovative decisions. You will be able to see unusual approaches to solving a particular problem. This means that you will literally keep up with the times, ahead of your competitors.

That is, artificial intelligence can significantly simplify your work with unstructured data and provide new prospects and opportunities for business development in general.

Let's summarize

Artificial intelligence is a truly reliable assistant for modern business in many areas, including in the process of collecting, processing, and analyzing unstructured data. But we want to draw your attention to the fact that it is very important to choose a good tool here, as well as ensure its stable operation without any restrictions. This can be implemented by additionally connecting to the work of mobile proxies from the MobileProxy.Space service.

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