According to CIO, more than 90% of data generated by businesses today is unstructured. You may already be using data analytics for making business decisions. However, if your analytics work is still confined to structured data, you may be missing out on key insights. Here’s where unstructured data analysis comes into play..
If you have been disregarding the unstructured part of the data in your business, you don’t have to do that any longer. With the aid of next-generation unstructured data analysis tools, you can immediately start uncovering insights that were hidden beneath large volumes of unstructured data before.
In the following article, we’re going to shed light on the differences between structured, unstructured, and semi-structured data. Next, we will discuss how each of these data types is used in next-gen tools to provide value for your business.
Structured, unstructured, and semi-structured data - definitions and general overview
Let’s start off by clarifying how unstructured data differs from structured data.
What is structured data?
The Enterprise Big Data Framework defines structured data as one that is formatted according to a predefined model. It’s common in enterprise applications that handle customer relationships, inventory, and orders.
To explain this further, let’s see how a CRM (customer relationship management) tool handles customer information. It stores customer data such as name, address, email, mobile number, and date-of-birth, etc. Each of these data types does not vary from one customer to another. So, this type of software application can be designed to work with a predetermined information structure.
Standard enterprise applications responsible for inventory control, order management, and others, also handle datasets with similar features. Structured data characteristics make it possible to arrange it in tabular formats. Then, the new data coming in can be inserted as new rows into the same set of tables.
This table structure forms the basis of relational databases, which are widely used to store structured data. These databases also offer a domain-specific language – the famous SQL - to support data manipulation. The data stored in relational databases are searchable via either human or machine-generated queries. SQL offers a rich set of features such as filtering and joining for robust analysis of data in these databases.
As the name implies, structured data has a predetermined model which makes data analysis straightforward.
What is unstructured data?
In contrast to structured data, unstructured data doesn’t have a predetermined data model. Long text, images, videos, and binaries are generally categorized as unstructured data. Emails, social media content, chat messages, and web forum content are some of the most common sources of unstructured data today’s enterprises have to deal with. Business documents like legal contracts, product descriptions, technical specifications, and customer survey questionnaires also contain huge amounts of unstructured data.
That said, data categorization can be context-dependent. To clarify what this means, let’s consider two unstructured data examples.
An email has a sender, one or more receivers, sent time, and a message body with some arbitrary text, and images. Sometimes, it includes one or more attachments as well. These data types, i.e., senders, receivers, and time sent, fit into a structured data model. However, when we take a look at the message body, it contains unstructured information.
The same can be observed in the case of social media, which is another widely used example for unstructured data. Social media contents have some data types such as user and time of activity which belong to the structured data category. But, an analysis limited to these data types fails to deliver any actionable insights. To gain a real understanding of the context, we have to tap into the actual content which includes text, pictures, and often videos. They are inherently unstructured and do not follow any data model.
Unlike structured data, unstructured data cannot be arranged into tables with fixed columns and rows quite so neatly. It is generally stored in so-called ‘data lakes’ as raw information in their native format. If you're looking for a more detailed market view, we've written an article about that too!
To sum up, while it could help to derive insights for crucial business decisions, unstructured data analysis demands the use of specialized tools with built in AI capabilities.
What is semi-structured data?
Semi-structured data shares some characteristics with both unstructured and structured data.
Its structure is formed through metadata, i.e. data that supplements information on other types of data, or markup tags. However, it does not fit into tables in a relational database.
As an example, a web page that consists of HTML tags has a certain structure. But, it’s not possible to arrange it in a tabular format. Some documents in JSON and XML have similar characteristics. Social media like Twitter have hashtags, which can also be used to create a certain data structure.
In terms of complexity in data analytics, semi-structured data sits in between structured and unstructured data. That said, most semi-structured data contains long text and images.
Overall, it’s worth mentioning that semi-structured data, which often comes in the form of the above-mentioned metadata, is more important than data itself.
"Data on its own can be meaningless, but when combined with metadata, it turns into information that can be exploited and, when aggregated with other datasets, delivers the insight that every organization needs to improve decision-making.” - Nabil Lodey
Structured vs unstructured data in next-gen tools
Traditional data analytics tools are not capable of handling the volume and complexity of unstructured data. In order to analyze it, we must turn to next-generation data analytics tools, built on AI and ML.
Unstructured data analysis involves parsing lots of text written by people on various channels like emails, social media posts, and comments. The recent developments in NLP algorithms such as transformers have enabled data analytics tools to efficiently parse such long text content.
The availability of AI services from major cloud service providers has also led to many developments in cloud-based, AI-powered data analytics tools. They have created a variety of new data analysis use cases.
We can use these tools not only to make business decisions but also to automate complex business processes. Let’s take a closer look.
With 3.6 billion users worldwide, social media accounts are considered one of the biggest data sources for most businesses. There are about twenty major social media platforms and hundreds of smaller players, and chances are, your customers are actively discussing your products on more than one of them.
Social listening is the process of monitoring social media platforms for user activities related to your organization. This could be direct messages, comments for the content you have created, mentions of your brand in public discussions, etc. While it may not be possible to monitor every social media platform on the planet, you should work on a selected set of platforms where your customers are most active.
Social listening can reveal valuable information on the brand’s sentiment and can also help in estimating how satisfied customers are with your products and offerings. It can also be used to identify key improvement areas. As a result, your team can focus their energy and resources on achieving these goals.
Proactive analysis of social media channels can help you avoid any PR crisis and stop any negative issues from going viral. Analysis of historical data in social media can help uncover facts about what your customers liked or disliked in the past, so that future product offerings can be planned accordingly.
Due to the sheer volume and unstructured nature of social media data, there is no place for manual data analysis. Being equipped with proper tools is a must.
Modern data analytics tools with AI capabilities can help you navigate through these vast oceans of content and discover actionable insights.
Content Moderation is another important social media management use case. For some businesses such as social gaming that rely on user engagement and interaction, content moderation can be more significant than to others. To name a few, content moderation can help identify unethical user behavior like mobbing, harassment, discrimination, and hate speech.
Based on this information, appropriate actions can be taken to restrict such activities. This will improve brand loyalty among legitimate users. Unstructured data analytics tools that leverage AI are an essential part of content moderation. They can apply NLP and image processing algorithms to a large collection of social media content. As a result, they can automatically block unhealthy content.
Categorizing Support Tickets
More and more users tend to interact with businesses via online mediums. Unstructured data analytics techniques can help you build a scalable online customer support system by automating the categorization of support tickets.
Such a system can process the content of the support ticket using NLP algorithms to identify what type of problem the customer is facing and channel the ticket to the appropriate department within your organization.
Track customer conversations & gain market intelligence
Modern customer relationship management should be based not only on reactive but also proactive measures to identify every opportunity for improvement. Analyzing customer conversations can provide valuable insights in this regard.
Customer conversations include communication via email, chat, support forums, social media, etc. Analyzing this data manually is impractical and time-consuming. You must be equipped with a robust AI-powered data analytics tool to do it effectively and to gain useful insights. They should not only analyze the individual text messages but also look at past customer behavior to identify and prioritize problems that must be solved first.
Analyzing outbound campaigns to build an ICP
Last, but not least, let’s discuss customer profiling, which is an important concept when carrying out marketing campaigns. Back in the olden days, customer profile tools used simple criteria such as age, gender, spending limits, etc., to create profiles. However, these are no longer enough for serving the modern markets and for gaining a competitive edge.
Next-generation analytics tools with AI capabilities can help you build Ideal Customer Profiles (ICPs) based on multiple and complex criteria such as customer feedback, buying history, demographic and psychographic characteristics, etc. The customer profiles created in this manner will more closely resemble real-world customers.
In the digital era, most organizations agree that effective business should revolve around making data-informed decisions. However, it doesn’t mean that all companies know how to do it effectively.
How so? Some businesses might still be using traditional data analysis tools, which have limited capabilities - and if companies are incapable of digging into unstructured content like customer reviews, comments, images, or video, they might be missing out on crucial business insights.
So, what’s the solution? Using a next-gen tool equipped with AI algorithms that analyze all three data types – structured, unstructured, and semi-structured.
Looking for more possibilities? Take a look at our full list of use cases.