The vast majority of data that businesses deal with these days is unstructured. In fact, IDG Research estimates that 85% of all data will be unstructured by 2025. There are huge insights to be gathered from this data, but they’re hard to draw out.
Once you learn how to break down unstructured data and analyze it, however, you can perform unstructured data analytics automatically, with little need for human input.
Unstructured data has no set framework or regular design. It is usually qualitative data, like images, audio, and video, but most of it is unstructured text data: documents, social media data, emails, open-ended surveys, etc.
Unstructured text data goes beyond just numerical values and facts, into thoughts, opinions, and emotions. It can be analyzed to provide both quantitative and qualitative results: follow market trends, monitor brand reputation, understand the voice of the customer (VoC), and more.
Read on to learn how to analyze unstructured data.
Unstructured data analysis tools use machine learning to gather and analyze data that has no pre-defined framework – like human language. Natural language processing (NLP) allows software to understand and analyze text for deep insights, much as a human would.
Unstructured data analysis can help your business answer more than just the “What is happening?” of numbers and statistics and go into qualitative results to understand, “Why is this happening?”
MonkeyLearn is a SaaS platform with powerful text analysis tools to pull real-world and real-time insights from your unstructured information, whether it’s public data from the internet, communications between your company and your customers, or almost any other source.
Among the most common and most useful tools for unstructured data analysis are:
Read on to learn how to put these text analysis tools, and more, to work on your unstructured text data.
With the right tools analyzing unstructured data can be easy. Follow this step-by-step guide:
Are you looking to follow trends in the market or do you just need a number or statistic to assess sales or growth? Do you need to evaluate open-ended surveys or automatically read and route customer support tickets? Do you want to do social listening to find out what customers (and the public at large) are saying about your brand and compare it to your competition?
Start with a solid idea of what you want to accomplish. Text analysis methods, like keyword extraction, sentiment analysis, and topic classification, allow you to pull opinions and ideas from text, then organize and analyze them more thoroughly for quantitative and qualitative results, so the possibilities are vast.
Once you’ve decided what you want to accomplish, you need to find your data. Make sure to use data sources that are relevant to your topic and the goals you set, like customer surveys and online reviews.
Whatever technique you use, make sure no data is lost. Databases and data warehouses can provide access to structured data. But “data lakes” – repositories that store data in its raw format – offer better access to unstructured data and retain all useful information.
Tools like MonkeyLearn allow you to connect directly to Twitter or pull data from other social media sites, news articles, etc. As data moves fast in our current business climate, you’ll want to learn how to collect real-time data to stay on top of your brand image.
You can use integrations with programs you may already use, like Google Sheets, Zapier, Zendesk, Rapidminer, SurveyMonkey, and more. Or use web scraping tools, like ScrapeStorm, Content Grabber, and Pattern.
You can collect emails, voice recordings, chatbot data, news reports, product reviews – unstructured data is practically endless.
Unstructured text data often comes with repetitive text or irrelevant text and symbols, like email signatures, URL links, emojis, banner ads, etc. This information is unnecessary to your analysis and will only skew the results, so it’s important you learn how to clean your data.
You can start with some simple word processing tasks, like running spell check, removing repetitious words, special characters, and URL links, or give a quick read to make sure words are used correctly.
MonkeyLearn offers several models to save time and make data cleaning easy. The email cleaner automatically removes signatures, legal clauses, and previous replies from within a thread, so you’ll end up with only the most recent reply:
The boilerplate extractor extracts only relevant text from HTML. You can use it on websites or emails to remove clutter, like templates, navigation bars, ads, etc.
And the opinion units extractor can break sentences or entire pages into individual thoughts or statements called “opinion units”:
It can automatically go to work on hundreds of pages of text in a single go to get your data prepped and ready for analysis.
Text analysis machine learning programs use natural language processing algorithms to break down unstructured text data. Data preparation techniques like tokenization, part-of-speech tagging, stemming, and lemmatization effectively transform unstructured text into a format that can be understood by machines. This is then compared to similarly prepared data in search of patterns and deviations in order to make interpretations.
This can all be done in just seconds using machine learning tools, like MonkeyLearn.
Once the data is structured, you're ready for analysis. Depending on your goals, you can calculate whatever metrics you need. SaaS tools allow you to pick and choose from many different extraction and classification techniques and use them in concert to get a view of the big picture or super minute details.
Maybe you’re following a new product launch or marketing campaign and you need to know how customers feel about it. You can extract data from social media posts or online reviews relating only to the subject you need, perform sentiment analysis on them, and follow the sentiment over time.
Creating charts and graphs to visualize your data can make analyses much easier to comprehend and compare. MonkeyLearn Studio is an all-in-one business intelligence platform where you can perform all of the above in one single interface, and then visualize your results in striking detail for an interactive data experience.
MonkeyLearn Studio offers templates (or you can design your own) with multiple text analyses chained together.
Below is an example of a MonkeyLearn Studio dashboard, with an analysis of customer reviews of Zoom.
Feedback is categorized by subjects: _Usability, Support, Reliability, _etc., then each category is run through sentiment analysis to show opinion from positive to negative.
See individual reviews by date, how categories change over time and read the intent of each comment.
Your data analysis becomes even more detailed and brings to light more insights when you connect multiple machine learning techniques together. And, with MonkeyLearn Studio you can manipulate your data, add new charts and graphs, and link new analyses right in the interface. It’s a single, connected process – no more downloading and uploading between applications.
When you can see all your results together, it’s easy to make data-driven decisions. See how customer opinions change over time to follow brand sentiment and individual campaigns. Follow different aspects of your business in real time to find out where you excel and where you may need some work. With machine learning text analysis you can pull data from almost anywhere for real, actionable insights.
Unstructured data requires more steps and more computer analysis than structured data, because it can’t easily fit into spreadsheets and databases. However, when you learn to use machine learning tools, the process can be pretty painless and the results formidable.
Whether it comes from social media, customer surveys, customer service interactions, emails, etc., our suite of machine learning tools will ensure you get the most from your data.
September 10th, 2020