Unstructured Data Management Made Easy with AI

Every digital interaction creates data. And nearly 80% of this data is unstructured.

Unlike quantitative data, often stored in spreadsheets and databases, unstructured data is not organized or formatted in any predefined manner. Data like social media comments, images, video, emails, and audio, are all unstructured and difficult to store, search for, and process.

But, cloud computing and AI tools, like MonkeyLearn, are introducing new ways to manage this valuable data. Allowing companies to sort it in next to no time, and glean insights that they may have otherwise missed.

Read on to learn how to better handle unstructured data, the valuable insights you can unearth, and how structuring unstructured data can lead to better decision-making.

What Is Unstructured Data Management?

Unstructured data management is the process of collecting, storing, organizing, and analyzing data that doesn’t have any predefined structure.

Structured data management can easily be performed using everyday tools like Excel, Google Sheets, and databases. Unstructured data management, however, requires more advanced tools, complex rules, and techniques to transform it into structured data.

The goal of unstructured data management is to make business data accessible, secure, and reliable so that companies can draw insights to make informed decisions and shape data-driven strategies.

Companies handle large amounts of unstructured data from different sources every day: market data, customer feedback, in-app reviews, social media, and so on. All this data contains valuable information to support decision-making, improve processes and products, reduce costs, and gain a competitive advantage.

Valuable insights from a tweet asking Zoom for a feature request: more emojis

But when it comes to managing their unstructured information, businesses often face a series of challenges, including:

  • Poor-quality data: duplicate, outdated, unreliable, or inaccurate data that contains outliers.
  • Inconsistency: data is stored in different formats and systems.
  • Siloed data: each team collects its own data and it’s not open to everyone, which prevents businesses from seeing the bigger picture.

Having a solid unstructured data management strategy to collect, organize, and analyze data can help overcome these challenges, and eventually lead to:

  • Increased productivity. Employees know where to find data when they need it because it’s all in one place and it’s easy to search. If you’re using machine learning tools to manage your data, you can even speed up internal processes and reduce response times.
  • Accurate & fast decisions. High-quality data is reliable and drives better decision-making. Using tools to analyze unstructured data in real-time allows you to detect urgent issues and act quickly. Also, uncovering trends in large datasets helps you anticipate market shifts.
  • Better compliance. Ensuring your data is organized and always up-to-date makes it easier to keep up with current regulations and standards and avoid any kind of legal trouble.
  • Improved data security. Data breaches and cyber-attacks are a common threat to organizations. Effective data management helps you keep your data safe, create backups, and monitor in real-time to identify potential risks.

In short, knowing how to manage your data effectively can help you extract more value from unstructured data and translate this value into opportunity.

How to Manage Unstructured Data?

Unstructured data (or qualitative data) is a highly valuable business asset. Product reviews, brand mentions, open-ended survey responses, and general feedback about your support team, all provide valuable customer insights.

But customers’ opinions, habits, and expectations are not always easy to manage and understand, because it’s disorganized and doesn’t fit neatly into a spreadsheet or relational database.

Manually analyzing unstructured text is time-consuming (hence, expensive), and prone to human error and bias. Plus, it doesn’t scale. As your business grows, along with your data, you’ll need tools that help you cut through the noise and find what's relevant.

So, how can you get started? There are four steps you’ll need to follow to manage unstructured data:

1. Make Content Accessible, Organized, and Searchable

First, you’ll need space to store unstructured data.

Public cloud-based storage is the obvious option because it’s easy for everyone to access, and enables remote collaboration. Plus, it’s scalable and cost-effective: if you need more space, you can always upgrade to a higher tier. Amazon Simple Storage Service (Amazon S3), Google Cloud, and Azure Data Lake Storage are the main cloud storage providers for big data.

The alternative to storing data in the cloud is to invest in on-premise storage hardware, like servers or external drives. Some businesses prefer to store their critical data in-house due to security concerns or data protection regulations. While on-premise storage offers full control over your data, it also involves higher costs like IT support, maintenance, and security infrastructure.

You can also opt for hybrid data storage, which combines on-premise and cloud-based storage.

To decide which storage option is best for your business, you’ll need to consider the type of data that you handle (highly-regulated industries like finance or healthcare may require more security), your budget, and other requirements (for example, a start-up that’s planning to grow quickly may prefer a flexible cloud-based solution).

When storing your data, make sure that it's easy to search and filter, so you can explore datasets using keywords and quickly identify what you need. Adding metadata to files and documents is essential for summarizing content and making it searchable.

If you change your data storage you’ll need to migrate data, which involves careful planning. Plus, you may also need to convert data from one format to another.

2. Clean your Data

Unstructured datasets are very noisy. They often contain spelling mistakes, HTML tags, punctuation marks, hashtags, special characters, and so on.

To improve the quality of your datasets you need to preprocess data, also known as ‘data cleaning’. This step must be completed before performing any kind of text analysis.

Preprocessing data involves a series of techniques, including reducing noise, eliminating irrelevant information (for example, stop words), and slicing data into more manageable pieces of content (like opinion units).

MonkeyLearn hosts an array of tools that you can use to clean your data, such as:

  • Boilerplate extractor: removes HTML tags from text
  • Email cleaner: extracts the last reply from an email, while removing the rest of unnecessary information (threads, signatures, confidentiality clauses, etc).
  • Opinion unit extractor: separates paragraphs into smaller opinion units, so you can obtain fine-grained insights from customer feedback, and beyond.

Test with your own text

Results

TagValue
OPINIONThe hotel has a great location
OPINIONbut all in all it was a horrible experience!
OPINIONOnly stayed here because it was the pre-accommodation choice for one of our tours
OPINIONWill never stay here again!

3. Analyze your Data with AI Tools

Once you’ve stored, organized, and cleaned unstructured data, the next step is to analyze it. Using AI-powered tools, like MonkeyLearn Studio, to analyze your data is the most effective way to transform text data into something useful.

You can automatically classify text by topic, sentiment, intent, and more. Extract specific information like keywords or names entities, and detect patterns.

Text analysis tools combine machine learning and Natural Language Processing (NLP) to understand and process text data at scale. Maybe you need help classifying the sentiment of Twitter mentions in real-time. This sentiment analyzer can analyze sentiments in next to no time and quickly identify urgent issues.

MonkeyLearn suite of no-code tools is perfect for text analysis beginners. You can easily train models in a user-friendly interface using your own unstrcutured data and connect them to your apps through the API and available integrations. Or you can use pre-trained models and start gaining insights from your data in no time.

4. Visualize your Data

Compelling data visualizations help summarize unstructured data. Through charts, reports, or interactive dashboards, you can transform boring spreadsheets into clear and actionable information to share with your teammates.

MonkeyLearn Studio provides an all-in-one solution that allows you to analyze your data with machine learning and create customized data visualizations that help you dig deeper into your data.

data visualization dashboard showing granular insights extracted from unstructured data.

Get Started with Unstructured Data Management Tools

Unstructured data holds valuable insights to help your business grow. With the right set of tools, you can easily manage this data.

From cloud-based storage to artificial intelligence for data analysis, you’ll be able to set up a data management system that works for your business.

To learn more about how to analyze and visualize your unstructured data, visit MonkeyLearn.

Rachel Wolff

September 28th, 2020