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  • Satoshi Asari

Four misconceptions about AI text analytics

Organizations receive feedback about their products and services through more channels than ever before. At the same time, text analytics applications have transformed and are increasingly more useful, performing even the most advanced analyses of unstructured text.

Organizations that can figure out how to deliver great experiences ultimately win the game of increased loyalty and lifetime value. As a result, the analytics market has driven the ability to unlock insights from feedback as a significant competitive goal for leading brands. But with demand for data scientists continuing to outpace supply by as much as 12% each year, organizations at all analytics experience levels are being forced to explore ways to more effectively analyze their trove of available text data.

Enter no-code text analytics, a solution that can deliver insights from conversational text data without training, setup, or even data scientists or developers. But is text analytics just another buzzy trend being glamorized as part of the the no-code movement? Or is it an impactful advancement that will have lasting benefits for adopters?

A brief overview of text analytics

Text analytics refers to the process of analyzing unstructured text to obtain information. Historically, text analytics software could only complete simple tasks like calculating the number of occurrences of specific words or phrases. As computing power and artificial intelligence (AI) capabilities have evolved over the last decade, significant improvements have been made. Technologies such as natural language understanding (NLU) have made advanced analyses possible, such as unearthing insights about sentiment, emergence of trends, or unknown problems.

Today’s text analytics solutions mostly seek to automate the insurmountable task of manually reading text feedback for understanding. Text analytics software can extract meaning from open-ended text with processes such as theme identification, sentiment analysis, and correlation between words and quantitative scores. However, the majority of these solutions still require a heavy lift in terms of data science and programming. So even for the most basic text analytics solutions, large organizations are at an extreme advantage as both buyers and builders.

The no-code movement and analytics

The ability to gather massive amounts of data from customer feedback and connected devices like phones, cars, and even appliances has left organizations with piles of data to analyze at every turn. Hidden in this constant wave of information are insights that go undetected without advanced mining solutions.

Companies with advanced analytics know that a sustainable competitive advantage comes from the ability to know what data is saying. But in the race for digital innovation in analytics – a critical component to modern business strategy – size and budget are intensely important. Whether bought or built in-house, analytics software is expensive and time-consuming to set up, requiring heavy support from data science and software development teams for the smallest-scale projects. Without a large budget or staff on-hand to help, many organizations are left at a severe disadvantage.

Common misconceptions debunked

The no-code wave has finally made its way to text analytics. So why is this a huge deal?

1. No-code text analytics requires training – meaning you need data scientists.

Traditional text analytics solutions need models and data training to perform the most basic tasks. Even at organizations with in-house dev teams, data scientists are needed to monitor accuracy against training models. And although a business user might be able to upload data into software once it’s set up, making sense of the results typically requires basic knowledge of data science. The moment a user says, “I can’t really understand this, but I’m sure someone on my data science team can help me out,” the text analytics application isn’t an out-of-the-box product for non-technical users.

In no-code text analytics, setup is as simple as uploading a dataset into the application. No data training, modeling, or even background prep like library- or ontology-building is required to run analyses. Since non-technical users can gain insights through an easy-to-use interface, interpreting the data is a self-explanatory journey of discovery. These users, typically more closely aligned with critical business objectives, can manage entire analyses end to end, from problem identification, to discovering insights, to applying them forward into actionable solutions.

2. No-code text analytics requires developers.

Whether you built your own solution or you bought one that can’t do the heavy lifting for you, the tech expertise doesn’t stop at the background data science. You’ll need developers to build your own custom tool, or implement, supervise, and maintain one you’ve bought. And to customize the output of your analyses, you’ll need a developer to write a Python script. This will create an impenetrable wall over which your business stakeholders will need to toss requirements, and will severely slow the time between analysis and fulfilling critical objectives.

No-code text analytics applications, by their very nature, do not require any code to set up, analyze, or interpret projects. This means business users can use them immediately without the need for APIs or complicated integrations. There’s no technical know-how required beyond uploading a .csv file. This is a huge win for organizations without developers. For those with development teams, programmers can refocus on high-value builds and fixes, such as those for products and customers.

3. Manual review of text produces meaningful results.

You may be thinking to yourself, “I only receive a few hundred pieces of feedback a month, and we process them manually. What’s the benefit of a text analytics solution?”

Think of it this way: an in-house customer experience or insights team, especially if it’s composed entirely of non-technical stakeholders, must manually mine the feedback they receive. Days later, they’ve only managed to review a portion of the responses. And in manual review, humans can’t quickly or effectively correlate numerical scores to text ratings. As a result, the team has not only lost valuable time it could have spent addressing customer issues, but could not completely mine the mountain of data to find actual insights, or truly understand what people are trying to tell them.

Text analytics software in its simplest form automates the manual review process – and that’s a huge benefit not to be understated. An effective solution also allows organizations to mine all of their data, from just large enough for analysis to the largest projects. The accessibility of no-code text analytics means that all teams and organizations – regardless of technical experience or staff – can see a complete picture of their data projects without the large price tag. Using software that does the actual review work and instantly displays insights from the entirety of a dataset, teams refocus from time-consuming assignments to high-value tasks such as acting on insights from analyses, and serving customers.

4. "Do it yourself" solutions can achieve the same level of productivity as a no-code solution.

If global organizations want to consider feedback from all people they serve, they need to build or buy software that analyzes multiple languages. So if they’re building in-house or using a traditional solution, this means solving the library or ontology challenge. In addition to these difficulties, humans communicate based on common sense and context. The nuances that have evolved in language and communication over thousands of years are difficult to contain in homegrown systems. Add the fact that most organizations have products or services with specific terminology, product names, and cultural references that sneak their way into the text feedback they receive, and you have the perfect storm: an expensive, ineffective solution.

If you’re building your own solution – or using one that doesn’t reference a powerful background space – you’re missing out on the majority of critical insights you need for your text analytics solution to be effective. In a no-code solution, there’s no requirement to build or add to the background space. The software’s AI accesses it automatically as part of the application’s process, meaning you can skip the months of prep and research, and get straight to the good stuff: analyzing your text data.

In short

No-code text analytics solutions have made understanding conversational text an accessible and valuable opportunity for teams and organizations at all levels of analytics experience.

Experience enterprise-grade text analytics without the code, and understand what matters most to the people you serve. Get started today with Express for Luminoso Daylight.


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