What is data classification?
The short answer: Data Classification is the process of organizing data into categories for its most effective and efficient use.
In a time where nearly everything is digitized, from personal records to highly sensitive corporate data, it's about time we take a closer look into classification. Data classification in data science refers to the process that tags and categorizes any kind of data so that it can be better understood and analyzed. The latter is what we'll be focusing on.
But also, a well-planned data classification system makes essential data easy to find and retrieve. This can be of particular importance for risk management, legal discovery, and compliance.
Upward of 80% of enterprise data today is unstructured. - Gartner
Unstructured data specifically reveals insights that structured data is unable to deliver. Images are analyzed for content moderation can serve as a great example – without a way to understand and classify visual data, there is a risk of not being able to filter out inappropriate content.
Before we proceed to review the various data types and applications, let’s answer a question:
How do we understand data?
Data is a collection of facts and statistics and is essentially anything that can be classified, including text, images, files, and audio. It can be formatted in both structured and unstructured forms. While structured data is easy to search and analyze, unstructured data is generally in its original format and not organized in a predefined manner. This makes it harder to interpret - unless you use AI-powered tools or put in hours of manual labor.
That said, it is also important to mention that data can be categorized in several ways. Firstly, you can approach this process creatively by the end-goal, where it comes down to your pain points and bottlenecks:
The Big Question: What information and insight do you actually want to get out of classification?
sIn such a case, your team determines the labels (or classes) that will result in the highest business value. Another common method is to classify data per how it is being performed and further used – i.e., rule-based or Machine Learning-based.
Types of data classification
In the most simple terms, data can be recognized and categorized in three approaches. These are:
- Content-based classification: In this classification type, the contents of each file are the basis for categorization.
- User-based classification: User-based classification relies on the user’s knowledge of creation, editing, reviewing, or dissemination to label sensitive documents. These individuals can specify how sensitive each document is.
- Context-based classification: Context-based classification focuses on the context of the data, such as the location, application, and creator, as well as other variables that affect the data.
How do you create a classifier?
While some might assume that setting up a system to categorize data is difficult - we'll vouch for it - it simply isn’t the case:
- Define the tags for the classifier of your choice making sure the terms aren’t too vague. To be effective, a classification scheme should be simple enough that all employees can execute it properly.
- Tag examples of the data to help teach the classifier
- Continuously test and adjust the classifier. In the end, it all comes down to using the right software, which can help you categorize data - even without coding skills.
The business value of data classification
Data classification has many benefits, such as helping your company successfully pass audits, knowing who needs access to what information, understanding the value of sensitive data, and empowering end-users. Problems such as what should be labeled as urgent, determining what language a text is written in, or what to tag topics with can be solved with versions of data classification.
Empowering your employees to do meaningful work is a value driver for businesses, and data classification makes it possible. That being said, let’s take security benefits as an example.
With a solid data classification strategy, data leaks can be prevented. For instance, just by classifying documents or emails by permission (such as ‘confidential’ or ‘C-level suite information only’), users could become more security-oriented and recognize the different data sensitivity tiers. Plus you can build a workflow that considers who should have access to what.
Some of the problems it can solve include:
- Urgency detection: A pre-trained model can classify inbound texts and support tickets to determine whether they should be labeled as urgent or not urgent.
- Sentiment detection: NLP, or Natural Language Processing, can be used to detect what the sentiment of any given content is - save time by routing the right messages to the right people.
- Topic labeling: Topic labeling consists of tagging topics with a couple of descriptive words or phrases. This is done by using an NLP technique to identify themes and meanings - e.g. classify any incoming email attachment and forward it to the right folder in your storage system.
Classifying data can also be helpful in terms of meeting legal compliance. A lack of data classification doesn’t confine to informational chaos – it can also mean you’re not GDPR or HIPAA compliant. How so?
For instance, without data classification, you might not be able to recognize that a newsletter subscriber requested to be removed from the mailing list. Let’s assume they haven’t clicked the “unsubscribe” button, but have hit reply and asked to be removed via email. If you don’t catch this, you might end up keeping data against GDPR and look at a potential fine if your company is reported.
Time and manual task management go hand in hand. Imagine conducting an NPS or any other customer satisfaction survey, and going through all the free-text answers manually. Build a classifier to categorize responses by sentiment, or topic, uncover underlying trends or test out your assumptions. Combine it with other data visualization tools (e.g. word clustering), and you'll get better insights into what your customers are saying.
Data classification applications
Alright, so we now understand the value of classification. Let's dig in how we translate all this knowledge to practice.
Text classification is a powerful tool for utilizing these unstructured data we all sit on top of by utilizing NLP. In the words of our users, it feels like wizardry when you create your first classifier and see hundreds of survey responses categorized in seconds.
Document classification focuses on processes that mainly apply content-specific classification - e.g. classifying incoming email attachments by type. It differs from text classification, as instead of specific phrases or paragraphs being classified, the whole document is taken into consideration.
Take shipping documents as an example - more often than not, a signature is needed on multiple pages. By training a model to classify between correctly filled documents versus documents where one or more signatures are missing, the process can be sped up significantly.
And time saved is the value gained.
Image classification categorizes any incoming image file by predetermined labels. It is often combined with object detection. These days you can create your own image classifier and teach the model to make subjective decisions based on your logic: whether an incoming ad creative is good or not; whether the image fits into the product portfolio; whether an image you snapped on your holidays is appropriate to show to your grandparents.
Or let's say that you work with an e-commerce platform where image content is user-generated. It's a marketplace where anyone can sell their goods. Even if you can handle manually moderating the content by filtering out low-quality or inappropriate images, there will come a time where the scale of this task is just not efficient.
Identifying business areas for the biggest benefit
Here are some examples of how to apply data classification in your business:
Customer support is one of the lifelines of any organization. Data classification can be used for recording and sorting support tickets, incoming emails, and text messages - or even contact management for transaction history, tasks, and reminders.
Let's zoom into support tickets: customer support messages are often subjective in their nature. By leveraging AI-powered tools, the system flags the tone of each of the tickets as either positive, negative, or neutral, allowing for better prioritization.
Another example of data classification apps is AIaaS tools which use data classification to categorize support tickets or recognize images for content moderation. There are also chatbots, which can organize data and either respond or tag your query as “product”, “payment,” “refund,” etc., before taking you to a human agent.
Customer care is also significantly improved through systems such as NPS, CSAT, and CES. They all often include long free-form text answers that more often than not are analyzed manually. When you scale, it doesn't sound very efficient, does it?
By training an AI-powered assistant, thousands of these responses can be categorized into clusters that matter to you most. Automatically.
Companies use data classification if they need to fix a software bug quickly. For instance, categorizing crashes and bug reports allow them to identify the type of software defect. For companies with a lack of resources such as skilled employees and time, this triage process is essential for software development.
Content moderation is a field mainly shifting to a data classification moderation system. With humongous amounts of images and articles being created every day, it is nearly impossible for Ops to keep up with moderating the content.
With NLP, it is possible to learn what is the tone of voice surrounding your brand. Classifying data can also be used to help make better strategic decisions. Sentiment analysis shows whether people generally have a positive, negative, or neutral feeling towards your brand as a percentage breakdown
Data classification uses both content-based classification and context-based classification to moderate what is being posted online. These classification systems are able to screen both text and video for inappropriate and illegal content that should be removed from the public.
Analyzing text responses lets you categorize your customer feedback based on the sentiment and uncover any underlying patterns. In most cases this is where rule-based automation fails - people don't naturally speak in keywords.
Data classification can also be used for quality assurance. The classifier just needs to be programmed to screen for defects in images. The performance level of data classification is often higher than manual quality assurance - there is just no room for human error.
Speed is necessary when it comes to inspecting image or file quality. With ML (Machine Learning) type of classification, a visual quality inspection can be performed for 100 images in just one or two seconds.
Though data classification sounds daunting, it is easier to implement than it sounds. It is simply the process of tagging and labeling any form of data to be presented in a structured manner. By classifying data, businesses can be more efficient, improve their customer service, and implement better data security... You name it!
You can of course always hire a team of engineers to do it for you. But there's plenty of cost-efficient software out there. If you're ready to get started - we'd love to hear from you!