Arguably, one of AI’s strengths is its ability to make sense of large amounts of data. That means looking for patterns and putting them into reports, documents, and formats that are easily understood by humans. This is the everyday “bread and butter” of not only data analysts, but many other knowledge economy professionals who work with data and analytics.
It’s true that artificial intelligence, a term commonly used in business and industry to refer to machine learning, has been used in these areas for years. Built on large-scale language models (LLM) and natural language processing (NLP), ChatGPT and similar tools make it easy and effective for everyone. What if a CEO could simply say to a computer, “What do we need to do to improve customer satisfaction?” or “How can we make more sales?” Answering those questions Do you have to worry about hiring, training, and maintaining an expensive analytics team for your business?
Fortunately, the answer is probably yes. In fact, as AI becomes more accessible and mainstream, that team may be more important to your business than ever. But there is no doubt that their jobs will change significantly. Below is my summary of how this technology may affect the field of data and analytics in the near future.
First, what are ChatGPT, LLM and NLP?
GPT-3 LLM seems to be able to use the language in a very sophisticated way as 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 some online book archives. By processing all this data, it learns how words are connected together and predicts what is likely to be the most appropriate response to a given prompt (question or other input) can. It is sometimes called “generative AI” because it produces new outputs that have never existed before.
What are ChatGPT’s limitations?
Before you get too excited about what it can do, it’s worth pointing out that despite the hype, there are some pretty big limitations to what technology can do today. Very basic stuff – if you’re not careful, you can easily make someone who relies on it in a professional capacity somewhat silly.
For example, when I was working on this article, it was only natural to ask what parts of a data analyst’s job ChatGPT could automate. One of the first answers it gave was “ChatGPT can generate graphs, charts, and other visualizations.” This is clearly wrong as it can only generate text.
When it comes to data analysis, ChatGPT is also limited by the fact that you cannot upload more data than you can enter as text. For example, you can’t upload an excel sheet of sales figures and ask for insights. Of course, I don’t know what future versions will be able to do. With that in mind, let’s take a look at how it can be used and speculate a bit about what’s possible with LLM and NLP in the near future.
How can ChatGPT, LLM, and NLP be used for data and analytics?
Here are some of the key ways ChatGPT, LLM, and NLP can be used in data and analytics.
· Create code and applications that can analyze data and automate processes such as data collection, data formatting, and data cleansing.
· Defining the data structure – for example, the fields to be included in records in a database and the row and column headings required in a spreadsheet.
· How should a chart, graph, diagram or infographic be created and what information should it contain?
· Suggest information to include in reports so that various audiences, such as executives, department heads, and managers, can take action on reports.
· Create training materials that teach employees how to apply analytics to their own data.
· Identify data sources that are likely to contain the insights needed for a particular task. For example, “Where can I get data on financial fraud in India?”
· Create dummy or synthetic data for various purposes, such as training other machine learning models or testing algorithms.
· Provide advice on practical steps that can be taken to ensure compliance, regulation and data manipulation are lawful, unbiased and ethical.
· Identify analytical processes and suggest best practices that are most likely to produce desired results.
Is ChatGPT a threat to data and analytics jobs?
As we have seen, ChatGPT can easily automate some of the tasks traditionally performed in analytical jobs, such as business, data, and financial analyst roles. Future iterations of the technology may be even more effective and efficient because of it.
But that doesn’t mean people in analytical roles will lose their jobs anytime soon. This is largely because today’s most sophisticated LLM and NLP tools still lack abilities such as critical thinking, strategic planning, and complex problem solving. Most experts agree that machine learning-based tools are unlikely to be able to perform these functions at the same level as humans anytime soon.
Businesses and other organizations may continue to need people who are experts in this field.
That said, analytical roles that require only repetitive work are likely to be largely automated in the near future, and some jobs will probably be lost due to this.
At the same time, new jobs will be created. These may revolve around the ability to practice human decision-making, problem-solving, leadership, strategy, leadership, and team-building while deploying tools like ChatGPT.
I work in data and analytics. How can I make it less verbose?
There are two very important rules to follow here. First, whatever you do, don’t pretend things aren’t happening and that AI isn’t going to dramatically change the way you work.
Then learn how to use this technology as a tool. See what it’s like to enhance your skills by automating mundane and repetitive tasks with tools like ChatGPT. This article has listed many tasks to which this can be applied immediately. Work on them and understand how each can perform. Then take advantage of the time and efficiency gains this creates to develop your skill sets and focus on areas where you can really make a difference. Learn how.
Ignoring the arrival of AI in your profession will only likely leave you behind. Colleagues and competitors who keep up with the times are rewarded. What we are currently seeing is the tip of the iceberg. As technology evolves, more and more aspects of our daily work are automated. Staying ahead of this era, learning to use new tools as they become available, and maintaining awareness of areas where human touch is still needed are the keys to success in the age of AI. .