Will ChatGPT Put Data Analysts Out of Work?

As the 2023 annual meeting of the World Economic Forum wrapped up in Davos, Switzerland, it ended with a disturbing prediction from one of the leading voices. Delivering a presentation on the 2023 Global Cybersecurity Outlook report, forum Managing Director Jeremy Jurgens revealed that 93 percent of those surveyed believe that a “catastrophic” cyber security event is likely in the next two years.

Undoubtedly, one of the strengths of AI is its ability to make sense of large amounts of data – searching out patterns and putting it into reports, documents, and formats that humans can easily understand. This is the day-to-day “bread and butter” of data analysts as well as many other knowledge economy professionals whose work involves working with data and analytics.

It’s true that artificial intelligence – a term that generally, in business and industry, refers to machine learning – has been used for years in these fields. What ChatGPT and similar tools built on large language models (LLM) and natural language processing (NLP) bring to the table is that it can be easily and effectively used by anybody. If a CEO can simply say to a computer, “what do I need to do to improve customer satisfaction?” or “how can I make more sales?” do they need to worry about hiring, training, and maintaining an expensive analytics team to answer those questions?

Well, fortunately, the answer probably, is yes. In fact, as AI becomes more accessible and mainstream, that team may well become even more critical to the business than it already is. What is beyond doubt, though, is that their jobs will substantially change. So, here’s my rundown of how this technology may affect the field of data and analytics as it becomes mainstream in the near future.

Firstly, what are ChatGPT, LLMs, and NLP?

ChatGPT is a publicly-available conversational (or chatbot) interface powered by a LLM called GPT-3, developed by the research institute OpenAI. The LLM (Large Language Model) is part of a field of machine learning known as natural language processing, which essentially means that it enables us to talk to machines, and for them to reply to us in “natural” (i.e., human) languages. In short, this means that we can ask it a question in English, or in fact, one of almost 100 languages. It can also read, understand and generate computer code in a number of popular programming languages, including Python, Javascript, and C++. We've gotten used to interacting with NLP technology for some time now thanks largely to AI assistants like Alexa and Siri, but the LLM powering GPT-3 and ChatGPT is orders of magnitude larger, enabling it to understand far more complex inputs and provide far more sophisticated outputs.

The GPT-3 LLM appears to be able to use language in a very sophisticated way because it was trained on a huge dataset of information, said to consist of over 175 billion parameters. This includes an open repository of web data called Common Crawl and several online book archives. By processing all of this data, it is able to learn how words are connected to each other and predict what is likely to be the most suitable response to any prompt (a question or other input) that it’s given. It’s sometimes called “generative AI” because it creates new outputs that haven’t been seen before.

What are the limitations of ChatGPT?

Before we get too excited about what it can do, it’s worth pointing out that despite the hype, there are some fairly significant limits on what the technology can do today. Firstly, it frequently makes mistakes – sometimes very basic ones – which could easily leave anyone relying on it in a professional capacity looking somewhat silly if they aren’t careful.

For example, when I was working on this article, an obvious thing to do was ask ChatGPT what parts of a data analyst's job it’s capable of automating. One of the first answers it gave was, “ChatGPT can generate graphs, charts, and other visualizations." This is clearly wrong, as it’s only capable of generating text.

Where data analytics is concerned, ChatGPT is also limited by the fact that we can’t upload data to it beyond any information that can be input as text. We can’t, for example, upload an Excel sheet of sales figures and ask it for insights. Of course, there’s no telling what future versions will be able to do. With that in mind, let’s look at how it can be used and speculate a little about what may be possible with LLMs and NLP in the near future.

How can ChatGPT, LLMs, and NLP be used in data and analytics?

Here are some of the key ways ChatGPT, LLMs, and NLP can be used in data and analytics:

·        Create code and applications that can analyze data or automate processes such as data gathering, data formatting, or data cleansing.

·        Define data structures – for example, what fields should be included in records in a database or what row and column headings are needed for a spreadsheet.

·        Tell us how charts, graphs, diagrams, or infographics should be constructed and what information needs to be included.

·        Suggest what information to include in reports in order that different audiences – executives, departmental heads, managers, and so on – will be able to take action based on them.

·        Create training material to teach workers how to apply analytics to their own data.

·        Identify data sources that are likely to contain the insights we need for a particular task – for example, "Where can I find data on financial fraud in India?”

·        Create dummy or synthetic data for a variety of purposes, such as training other machine learning models or testing algorithms.

·        Provide advice on compliance, regulation, and practical steps that can be taken to ensure data operations are legal, unbiased, and ethical.

·        Identify analytical processes and suggest best practices that are most likely to give the desired results.

Is ChatGPT a threat to jobs in data and analytics?

As we’ve seen, ChatGPT can easily automate some of the tasks that are traditionally carried out in analytical jobs – such as business, data, and financial analyst roles. Future iterations of the technology are likely to become even more effective and efficient at doing so.

But that doesn’t mean that anyone who works in an analytical role is going to be out of a job right away. This is primarily because today’s most sophisticated LLMs and NLP tools still lack abilities like critical thinking, strategic planning, and complex problem-solving. Most experts agree that it isn’t likely that machine learning-based tools will be able to carry out these functions at the same level as humans any time soon.

It's likely that businesses and other organizations will still have a need for humans who are experts in this field for some time to come.

Having said that, analytics roles that only require repetitive work are likely to become largely automated in the near future, and it’s probably inevitable that some jobs will be lost due to this.

At the same time, new jobs will be created. These are likely to revolve around the ability to deploy tools like ChatGPT while at the same time practicing human decision-making, problem-solving, leadership, strategy, leadership, and team-building.

I work in data and analytics; how can I make sure I don’t become redundant?

There are two very important rules to follow here. Firstly, whatever you do, do not stick your head in the sand and pretend this isn’t happening and that AI isn't about to dramatically change the way you work.

Secondly, learn to use this technology as a tool. Understand what its abilities are to augment your own skills by using tools like ChatGPT or whatever comes next to automate routine and repetitive tasks. In this piece, I’ve listed a number of tasks that this can be applied to right away – work through them and make sure you understand how each one can be done. Then, learn how to take advantage of the time and efficiency gains that this creates in order to develop your skillset and focus on areas where you can really make a difference.

Ignoring the arrival of AI in your profession is only likely to result in being left behind, as colleagues and competitors who are willing to move with the times reap the rewards. Right now, all we’re seeing is the tip of the iceberg. As the technology evolves, more and more aspects of all of our day-to-day work will become automated. Staying ahead of this curve, teaching yourself to use new tools as they become available, and maintaining awareness of areas where the human touch is still necessary, is the key to thriving in the age of AI.