Warning: the future of text analytics is extremely difficult to predict, because so many small elements will influence how and with what tools
we will analyze data in the future. Nevertheless, in this article we will try to outline the most important triggers and trends in the industry that can shape this future today. It is already evident that the possibilities of text analytics are strongly marked by technological possibilities. Yes, this is exactly the case when it makes sense to talk about technological determinism.
Tools and technological determinism
Nevertheless, in this article we will try to outline the most important triggers and trends in the industry that can shape this future today. It is already evident that the possibilities of text analytics are strongly marked by technological possibilities. Yes, this is exactly the case when it makes sense to talk about technological determinism. Without going too far or too deep, the most notorious and already annoying case of Chat GPT emphasises how much practical capabilities are shaping the future. Chat GPT was a phenomenon and a furore, unexpected for some, but in fact, with the intensity with which machine learning and artificial intelligence have been developing, it was quite expected. In text analytics, Chat GPT can gauge sentiment from customer feedback, social media posts, or other textual data. With Chat GPT, you can determine whether the sentiment in a text is positive, negative, or neutral, allowing you to better understand customer feedback and make data-driven decisions.
Chat bots are a great way to automate customer service tasks and save time on other work. With Chat GPT. you can create dialogue chatbots that can answer customer questions and provide support. Chat GPT can quickly analyse large amounts of text data such as customer feedback or social media. posts to identify trends, insights or patterns. All in all, Chat GPT can be a powerful tool for analytics professionals .So as a conclusion we can say- keep an eye on technology, on discussions and debates, on what’s evolving and on what hasn’t worked already. Being just in the field of this discussion will definitely allow you not to waste time on what has already passed or failed and shift your focus to where people see practical potential for optimising text analytics.
Internal observations
And as cliché as it sounds, an important point of reference is your own experience with text analytics. Here, it’s important to go beyond the bounds of the stodgy, day-to-day practices and reflect. This means periodically thinking about what else you could do with the data you’re getting, whether there is information we’re not getting yet.
Social media
In recent years, the commercial use of social media as a business tool has had a similar impact on the need for text analytics. Today we are at the stage of developing tools designed specifically to handle all the data from all sorts of social networks. So the next stage is to master these tools first.
Segmentation of text analytics by industry
Segmentation as one of the global trends in many industries also affects the current and future state of text analytics. The huge availability of data has created a need for a tool for industry-specific data analysis. Companies such as IBM, SAP SE and Microsoft have assessed the market requirements and provided services to develop industry-specific text analytics tools.
Focus on real-time analysis and trends towards predictive text analytics
Historical data analysis has long been established as a tried and trusted tool for highlighting patterns of behaviour and needs of their customers. But, the marked acceleration of customer-company interactions today leaves no room for pause. It used to be the norm to wait for a response or brand reaction to a customer’s enquiry, but today the pace is so fast that it is no longer acceptable. This means that the need for real-time text analytics is growing at times. Previously, conclusions and work on errors were left for later, today it must be done in the moment. And if you look half a step ahead, the next trend is predictive text analytics – to know and understand ahead of time. Qualitatively extrapolate reactions, requests and needs into today and minimise difficulties in the future.
Integration with other business systems
Text analytics is used in industry applications in integration with various software such as customer relationship management, competitive intelligence software and others. For example, the banking and insurance sectors use customer relationship management applications to better establish proper communication with customers through automated systems. The pharmaceutical and healthcare sectors use competitive intelligence based applications to mine, classify and analyse information related to scientific articles and patents.
The emergence of multilingual text analytics to overcome the language barrier
Another example of how paying close attention to universal trends allows you to better predict changes in your niche is globalisation. More and more companies, even non-large companies, are going global and expanding their audience. In this regard, there is a growing need to understand more and more people, in different languages. That is why another positive and effective trend is the emergence of multilingual text analytics. This allows you to separate and combine text data in different languages, depending on the tasks and needs of the company.
Convergence of text analytics with big data
A favourable trend is the increasing compatibility of textual data with big data. This enables more voluminous and accurate insights and forecasts for companies. This trend will only accelerate in the future, being implemented in more and more analytics tools.
Increased awareness among end users
A critically noticeable trend is the growing awareness among customers and users about the collection and analysis of their personal data in all interactions with a company. According to recent data, awareness rates have doubled, especially among the younger generation. This raises a lot of questions for analysts about how exactly this affects the quality of the text data obtained, and how much companies can trust it to derive actionable insights. There is a good chance that this very issue in the industry will change the way information is collected and the tools used to analyse it.
Text analytics is the way to go. As companies learn the basics of the tools, they also learn about themselves. Each journey begins with a detailed study of historical text data, continues with monitoring data as it becomes available, and ends with making decisions that change the way work gets done. That’s why predicting changes in text analytics is also about taking a close look at successful and failed new products, global trends, and your own needs.