Data is everywhere. With the internet and digitization of processes and business, we continuously create and consume data. Some people have gone as far as to say that "Data is the new oil." But most of the data on the internet is unstructured and cannot conveniently fit into a table to store and analyze it; thus, it becomes necessary to learn how to make sense of this unstructured data. We'll explore unstructured data in this article, but before we move forward, let's cover the basics first by asking some fundamental questions.
What is structured data?
The term structured data refers to organized data that fits perfectly into relational databases and spreadsheets like names, addresses, credit card numbers, and stock information. Relational database management systems (RDBMS) store this structured data. RDBMS is the basis for SQL and all modern database systems like MS SQL Server, IBM DB2, Oracle, MySQL, and Microsoft Access.
Structured data is primarily quantitative and displayed as numbers, dates, values, and strings. It makes up approximately 20% of business data and reveals patterns and trends that help you understand what is happening. In addition, it requires less storage space and is usually easy to analyze with tools like Excel, MySQL, and Postgres.
What is unstructured data?
Unstructured data has no pre-defined construction or systemization. It is qualitative data and comes in a variety of shapes and sizes. For example, it can comprise audio, video, and images, email and sensor data, media and entertainment data, surveillance data, geospatial data, or weather data. There is no specific data model for unstructured data, and we often natively store it (in the original format) or in a data lake.
The importance of unstructured data
Even though unstructured data is more difficult to search than structured data, needs more space, and requires processing to become truly useful, the amount of it is rapidly growing as digital applications and services proliferate. Unstructured data makes up approximately 80% - 90% of business data and continues to grow every year. It can provide you with countless insights that help you make informed and data-driven decisions when planned correctly.
We store unstructured data in applications, NoSQL (non-relational) databases, MongoDB, or data lakes (a data lake is a repository that stores data in its original format or after undergoing a basic cleaning process). Unstructured data reveals patterns and trends that help you to understand why something is happening.
Using AI-powered analysis tools is the most effective way to transform this data into valuable insights. AI allows you to automatically analyze and manage your unstructured data. That means you can get rid of repetitive tasks like manually sifting through social media posts or tagging and routing tickets. AI technology learns automatically how to extract names, locations, keywords, phone numbers and recognize topics and understand opinions that are useful in your business.
We can divide unstructured data into text, audio, image, video, and animation categories:
Text Data: Comprising business documents, email, social media, customer feedback, webpages, and open-ended survey responses.
- Business documents have an enormous amount of unstructured data that often goes untouched, as it takes more time to analyze. But by using text analysis techniques, companies can gather useful knowledge about employees and customers for competitive research.
- People send dozens of emails in a day, which is then translated into vast amounts of unstructured data. Text analysis software usually scans through thousands of these emails within seconds and then retrieves customer information and organizes them by categories.
- In social media, you might follow interesting trends in real-time. Once the search parameters are set and text analysis models are trained for your business, you can gain precious insights from social media about customer buying behavior and what they think of your brand.
- Customer feedback can be in various forms, such as phone calls, surveys, online reviews, and unsolicited social media posts. When you can gather this information and analyze it, you can get a fair idea of the customer's thoughts. With the help of customer feedback analysis, you will have hard data of customers' voices and that will also help you understand your area of expertise.
- While surveys usually include multiple-choice questions, there are times the responder answers in their own words. This requires that the text or recording be broken down into usable data so that we can analyze it.
Other Multimedia data: Image, video, and audio content are constantly being created by the media and entertainment industry, professional publishers, surveillance systems, and even individuals using TikTok, or, Instagram before uploading it on YouTube and other platforms.
Multimedia files are tagged with titles and stored in databases as JPG, GIF, etc. They are unstructured because we do not always know what these images, audio, and video files represent.
Even though a video is basically a sequence of images, accompanied by sound, it provides you with more information in less time. Digital video is useful in multimedia applications for documenting real-life objects. Examples of this include film, TV, documentaries, and surveillance.
In consideration of the enormous volume of data involved, analyzing the contents of media files is daunting. Because of this issue, automation solutions are currently being developed. Systems like natural language processing can extract text out of audio files using speech-to-text and then analyze it for sentiment analysis. Automatically generated meta Tags are helpful to classify media files and to perform search operations.
There has been a slow utilization of databases to manage unstructured data, mainly multimedia data. What is preventing sites from storing multimedia in a database? Predominantly, we attribute this to a lack of expertise, understanding, and a conservative view fostered by several factors including historical issues with performance and integration software.
How can information extracted from all these sources be used in the Insurance Industry?
Unstructured data on the internet rarely exists in just one form. Usually, text data is accompanied by images or videos, thus it becomes necessary to meaningfully combine or correlate information extracted from these sources. Below are some examples of how this is done:
- Claims Processing: For claims processing, large volumes of documents that are handwritten, faxed, mailed, or scanned have to be processed. We can process these documents faster and with higher accuracy by Intelligent Document Processing Tools. This technology leverages handwriting recognition (image classification) and information retrieval (NER). Besides the written documents, supporting images, along with audio and video contents, need to be processed. Image processing, audio processing, video processing, natural language processing, and deep learning (a form of AI) are used to automatically extract information from unstructured claims data and augment text-based information. With document automation, insurers can automatically extract data from documents, identify fraudulent claims and validate claims that are in line with policies. This, in turn, leads to a better customer experience.
- Targeted Insurance Marketing: With the help of unstructured data collected from social media, insurance companies can better target insurance policies to the right people. By leveraging the fact that people share their life events on social media- these events can be captured and be used to pitch an appropriate insurance policy. For example, if someone has just shared information on Twitter about buying a house, the insurance company can recommend a house insurance policy. Obviously, information on the internet is not always in the text format, thus images and videos also need to be leveraged to achieve the goal.
Since ML models are not perfect, information gathered from different data sources should be correlated to make a prediction with high confidence. If a person has tweeted about buying a new house vaguely, "Got the best deal on this one", having an image of the house in the tweet would dramatically increase the accuracy of our prediction.
We looked at how unstructured data exists on the internet and how important it is to leverage its true potential. We also looked at data processing and modeling pipelines for audio, text, video, and image data. There are a plethora of problems that can be solved by correctly using unstructured data. Marketing and sales in Insurance are one of them. Harvesting the power of unstructured data in this domain will only lead to progress.
- Structured vs Unstructured Data: 5 Key Differences | Xplenty. https://www.xplenty.com/blog/structured-vs-unstructured-data-key-differences/