As the annual meeting of 2025 of the World Economic Forum wrapped up in Davos, Switzerland, it was capped off with a scathing prediction by one of the most prominent voices. Presenting 2023’s Global Cybersecurity Outlook report, Forum director Jeremy Jurgens revealed that 93 percent of respondents believe that an “catastrophic” cyber security event will occur within the coming two years.
One among the strengths in AI is its capacity to comprehend huge amounts of data by looking for patterns and putting it into documents, reports and formats that humans can easily comprehend. This is the everyday “bread and butter” of data analysts, as well as other experts in the knowledge economy that work in data analysis and analysis.
The truth is it’s true that artificial intelligence is a term that is used in general, both in industry and in business, used to refer to machine learning and has been utilized for many years in these areas. What Chat GPT and other tools based using large language models (LLM) and natural language processing (NLP) bring to the table is that they can be quickly and efficiently employed by any. If a CEO is able to tell a computer “what do I need to do to improve customer satisfaction?” or “how can I make more sales?” Do they have to think about hiring, training, or managing a costly analytical team to address these questions?
Well I think the answer, most likely, is yes. In actual fact, as AI becomes more widely available and commonplace, that team could become more crucial to the company than it is currently. It is unquestionable however it is that their roles are likely to change drastically. Here’s a brief overview of how this technology could impact the area of analytics and data when it becomes commonplace in the near future.
First of all, what are ChatGPT, LLMs, and NLP?
ChatGPT is a freely-accessible chatbot (or chatbot) interface powered by an LLM known as GPT-3 created in collaboration with the Research Institute OpenAI. Its LLM (Large Language Model) is a part of the field that studies computer-aided learning that is known by the name of natural language processing which basically means it allows us to communicate with machines and allow them to reply using “natural” (i.e., human) languages. In simple terms, this means we can ask a machine a question in English or, in reality, it can speak in one of more than 100 languages. It also has the ability to comprehend, read and write computer code using a range of well-known programming languages such as Python, Java script, and C++. We’ve become accustomed to working via NLP technology for a while in the past, thanks to AI assistants such as Alexa and Siri however, the LLM driving GPT-3 as well as Chat GPT is a tenth of a magnitude greater, allowing it to comprehend more complicated inputs and produce much more advanced outputs.
The GPT-3 LLM seems to be capable of using language in an advanced manner because it was based on an enormous amount of data that is believed to comprise more than 175 billion of parameters. It includes an online database of data from the web called Common Crawl as well as a variety of books online. In processing this information, it’s capable of determining how words relate to one another and determining what will be the most appropriate response to any request (an inquiry or any other input) that it is given. It’s often referred to as “generative AI” because it generates outputs that aren’t previously seen.
What limitations are there with Chat GPT?
Before we get enthusiastic about what it could accomplish, it’s important to point out that despite the hype, there are very significant limitations to what technology can accomplish currently. First of all, it often makes mistakes, sometimes even simple ones that could quickly leave anyone who is relying on it for professional purposes appearing a bit silly If they’re not careful.
As an example, when I was writing this piece, the obvious step was to inquire with ChatGPT what aspects of the work of a data analyst it could automate. One of the initial answers it offered was “ChatGPT can generate graphs, charts, and other visualizations.” It’s clearly not true, as it’s just capable of creating text.
When it comes to data analytics, ChatGPT is also limited because it isn’t able to upload data beyond the information that could be entered in text. It’s not possible to, for instance, send the Excel spreadsheet of figures for sales, and then ask it to provide insights. Of course, it’s impossible to predict what the future versions of NLP can do. With this being said, let’s take a look at the ways it could be utilized and speculate about what is possible with LLMs and NLP in the near future.
What is HTML0? ChatGPT, LLMs, and NLP be utilized in the field of analytics and data?
Here are a few principal methods ChatGPT, LLMs, and NLP can be utilized for data analysis and data mining:
* Develop code and programs that analyze data or automate processes, such as data collection data formatting, data collection, or cleaning data.
* Define data structures — for instance which fields are required to be recorded in a database, or what columns and row headings are required for an Excel spreadsheet.
• Tell us the way charts, diagrams, graphs and diagrams or infographics are constructed and what information should be included.
• Describe the information to include in the reports in an order that the various audiences such as departmental heads, executives managers, departmental heads and others are able to make decisions based on these reports.
* Develop training materials to help workers learn about how they can apply their analytics skills to data.
Find data sources which are likely to provide the data we require for a specific job – for instance, “Where can I find data on financial fraud in India?”
* Create fake as well as fake data to serve a variety of reasons, including making other models of machine learning as well as testing the algorithms.
* Offer advice regarding compliance, regulation, and the practical steps that could take to make sure that that data processing is legal, unbiased, and ethical.
• Identify the analytical processes that are being used and suggest the best practices likely to produce the desired outcomes.
Is ChatGPT a danger to jobs in analytics and data?
As we’ve observed, ChatGPT can easily automate certain tasks done in analytical positions including data, business and financial analyst positions. The future versions of the technology are expected to become more effective and efficient at it.
But it doesn’t mean those who work in an analytical capacity will lose their job in a matter of hours. It’s because the latest and most sophisticated LLMs as well as NLP tools do not have the capabilities such as analytical thinking, planning for strategic purposes and sophisticated problem-solving. Many experts are of the opinion that it’s not likely that machines learning-based tools are able to accomplish these tasks on the same human level anytime in the near future.
It’s likely that companies as well as other businesses will require human beings who have expertise in this area for a while to come.
Although jobs in analytics that require repetitive work are expected to be mostly automated in near time and it’s likely that certain jobs will disappear because of this.
In the meantime there will be new jobs made. They are likely to be centered around the use of tools such as ChatGPT as well as doing the same thing with human decision-making, problem-solving management, strategy, leadership, and team-building.
I am in the field of analytics and data; how do I ensure that I don’t get redundant?
There are two crucial guidelines to be followed in this regard. First, no matter what you do, don’t keep to the side of the road, and pretend that this isn’t happening, and that AI will not dramatically alter your way of working.
Then, master the use of this technology to aid in your work. Know what it can do to improve your abilities with tools like Chat GPT or the next one to automate repetitive tasks. In this article I’ve listed a variety of tasks this technology can be applied to immediately . Explore them, and be sure to be aware of how each one could be completed. Also, you should learn to benefit from the efficiency and time savings it brings for you to improve your abilities and concentrate on areas where you could truly make an impact.
The absence of AI in your industry is likely to cause you to be left in the dust, while colleagues and competitions ready to change with the current trends reap the benefits. As of now, what we’re seeing is the top of the Iceberg. As technology advances every aspect of our daily work will be automated. Being ahead of the technology, learning to make use of new tools when they are made available, and ensuring you are aware of the areas in which personal touch of humans is needed is crucial to succeeding in this day of AI.