Text analytics is the process of breaking apart textual content into conceptual areas to uncover insights, trends, and patterns once the content is digested and transformed. As a technique, text analytics allows businesses to “connect the dots” with data they did not even know they had to get into their customer’s minds.
Text Analytics Uses These Unstructured Forms Of Data
– Word documents
– PowerPoint decks
– Excel files
– Geospatial data
– Sensor data
– Voice messages
Most companies have a generous source of unstructured data that accumulates over the years on internal mapped drives or other locations.
Unstructured data is free form data that is not organized in a pre-defined manner consisting of documents of written content such as articles or specifications documents.
Unstructured information is typically text-heavy but sometimes features dates and numbers. The nature of unstructured data makes it challenging to get to, like being “trapped” in physical or virtual cabinets. Moreover, unstructured data is labor-intensive to index, structure, and report on. As text analytics evolved into a more comprehensive technology, Artificial Intelligence (AI) and Natural Language Processing (NLP) tools have been brought in to help with content analysis.
This development is especially relevant for how technology tools gain insight into unstructured data. For example, text has structure, such as the rules of grammar and spelling. However, true textual analysis requires an understanding of context and tone. Computers have yet to understand the full subtleties of language.
With so much data in text form, businesses are leveraging text analytics to liberate value within the text to make decisions and act. However, a decision or action made on incomplete data is a barrier rather than an accelerator to the evolution of a business.
How Text Analytics Provides Value With Unstructured Data
Less than 20% of unstructured data is analyzed by companies
Text Analytics is an evolving technique with practical application that is revolutionizing industries
Uses large text collections to identify trending patterns in data for new insights
Using unstructured data in text analytics is projected to change the future, with a report that predicts a North American industry valuation of $4.75 billion by the year 2024.
This enormous growth is driven by several factors, including:
· Fast advancements in the AI, machine learning, and text analytics industries
· Increased activity on social media platforms and demand for social analytics
· Widespread use of mobile devices and other mobile technologies
Text Analytics Benefits
As tech analytics shapes the future, it is important to understand the text analytics benefits of the technique in the real world. At the core of each business, the following text analytics benefits can be expected:
Spot emerging trends or concerns about your business
Rank issues based on what is important to your customer
Capitalize on insights into your customer segments and feedback
The application of text analytics makes a business more user-centric, responsive, and agile. A single insight into your customer’s mindset can cause a paradigm shift for the way business operates, in the following ways:
o Improve user experience and decision making
o Enhance business intelligence
o Help businesses to understand customer trends, product performance, and service quality
o Increase productivity and cost savings
Text Analytics Examples
Other than business applications, text analytics has also been used for a variety of government and research needs such as searching documents relevant to daily activities.
More specifically, government and military groups use text analytics for national security and intelligence purposes.
Exciting developments in scientific research have incorporated text analytics to organize large sets of text data i.e. unstructured data. Text analytics has enabled scientific discovery and sentiment analysis in social media that resulted in societal progress.
The gains that society has made by harnessing unstructured data can be attributed to the accessibility of the internet. In 2020, it is estimated that 4.57 billion people have access to the internet. That figure represents more than half of the world’s population. Out of which, about 49% of the population are active on social media. The large data sets available from social engagement have fueled text analytics. A vast amount of text data is generated every day in the form of surveys, reviews, blogs, and tweets. Add in digital customer interactions and significant use cases for text analytics are generated every day.
An example, in the healthcare space, text analytics was utilized for virtual doctor routing.
A text analytics model helped:
– Identify health conditions for patients based on a description of symptoms
– As the patient self identifies, they are routed to an appropriate virtual doctor
– This changes the paradigm and service delivery of healthcare providers
As industries learn lessons from implementing text analytics, they see new growth trajectories and innovate.
Another use case of text analytics is in finance. It is estimated that the price of non-compliance in finance is as high as $39.22 million a year in lost revenue, business, disruption, and penalties. Financial advisors are in danger of noncompliance when they fail to provide proper disclosures in “client advice” documents. Such disclosures may cover conflicts of interest, commission structure, cost of credit, and more.
In finance, text analytics uses AI to structure the data of disclosures into an easy-to-review form like an Excel document. As a result, human auditors that might spend hours reviewing documents can process the same information in less time. Finance can leverage text analytics solutions to turn hours of work into minutes of productivity.
Steps To Text Analytics
Productivity gains are a result of analytical processes that vary based on specific text analysis tools. However, certain fundamental steps are always present in text analytics. Here are the steps in the text analytics technique:
In the first step of how text analytics works, identify locations from which to source data.
Next, the text collection is parsed. Parsing is the process of understanding the syntactical structure of the data set. The syntactical structure is based on the structural organization of a given sentence. The structural organization of a sentence is dependent upon the grammatical function or meaning of a sentence. Therefore, parsing looks at the arrangement of words, phrases, and clauses in the sentence of the text for further analysis.
After the text analytics technology transforms the text into structured data, Natural Language Processing (NLP) technology generates the needed metadata and understanding. This happens when the extracted text is transformed into machine-readable formats.
The critical step for decision making, mining happens when Machine Learning or AI algorithms identify insights across user-bases and buyer personas. This allows businesses to act based on tangible data results rather than guesswork.
Text Analytics Uses These Types Data Mining
This type of mining categorizes phrases as positive, neutral, or negative. This process yields valuable insights about market conditions and customer perceptions of the business.
This type of mining is based on spotting trends. Specific user desires are assigned to statements and calculated as trends. These trend findings can be used to personalize service offerings.
This type of mining is a big picture trend overview. Large textual data sets are used to identify significant trends in consumer behavior across an industry or product.
This type of mining is focused on isolating specific ideas from documents and ranking them by predetermined criteria. Concept mining is effective for garnering insight into consumer-driven operational needs for businesses.
Text Analytics vs. Text Mining
An understanding of how text analytics works is important in assessing the potential of data-driven growth in a globalized economy. Text analytics is an opportunity to discover the unexpected in the voice of your customers and innovate your business. While text analytics is often used interchangeably with text mining, these are not quite the same concepts.
Text analytics vs. text mining can be distinguished by the fact that text mining works on qualitative insights while text analytics produces quantitative insights. For example, text mining can analyze surveys and reviews to see if customers are happy with a product. Text analytics can yield deeper insights on customer behavior from unstructured text, such as identifying a pattern or trend of customer behavior. For example, text analytics can be used to interpret an upswing in the popularity of a product over time.
Imagine what you can do with text analytics for your business.
According to Markets and Markets, the text analysis industry will be worth $22 billion by 2022. This technique has changed the way the world interprets trends, consumers behave on social media and in real-time, and businesses operate. Text analytics has moved society forward into a bright future as part of a solution that changes the way we live.
Seeing exciting new results from text analytics poses the question: are all text analytics solutions created equal? User experience and a robust industry show that each text analytics platform has a specific offering and use cases where it excels.
At Scion Analytics, we are a leader in partnering with companies to transform and scale their business by harnessing the Content Analytics Platform (CAP). The CAP pioneered using AI and NLP to enhance the capabilities of text analytics.
We empower people to liberate value in the content. Together, we are on a transformational journey with content analytics becoming a ubiquitous form of communication. With personalized support for CAP, global businesses can increase impact, extend technical capabilities, improve operational efficiency, and scale their business. We are building a bright future together through technology.