BY: Malcolm de Bruyn, Data & Analytics Consultant, FirstTechnology Group
If you have been living under a rock and have not seen the news and social media rumblings, ChatGPT is a very cool tool developed by OpenAI. It is designed to facilitate the creation of chatbots and other conversational agents. Like other versions of GPT (Generative Pretrained Transformer), ChatGPT is trained on a massive dataset of human language and can generate human-like text based on a given prompt. However, this tool has been specifically optimised for generating text for chatbot conversations and can produce more coherent and natural-sounding responses than some other language models.
With the release of ChatGPT, the floodgates have been opened to the world of AI and multiple organisations are entering the race or accelerating the release of their own versions to ensure that they can corner that future market share. Ultimately, it’s important to understand that AI is here to stay. It’s more accessible than ever, and as such it’s essential to understand it’s potential impact and how to use it to your advantage.
AI is increasingly being integrated into data engineering and data warehouse projects, and its influence is expected to be enormous. In this post, I will look at how AI is being used in various areas and some of the inevitable benefits and challenges that could follow.
Task Automation:
Automating tasks is a crucial area in which AI is applied to data engineering and data warehousing projects. Machine learning algorithms,for example, can be used to analyse data sets, spot patterns and trends, and provide insights that humans would find difficult or impossible to unearth.This can significantly accelerate data processing and analysis, allowing data engineers and analysts to focus on higher-level activities.
Data warehouse development
ChatGPT and other language models could potentially aid in the development of data warehouses in a variety of ways, for example they might be used to extract and categorize data from unstructured text sources like customer reviews or social networking posts. This information could then be entered into a data warehouse for additional examination. This technology could also be used to generate documentation for data warehouse projects, such as data source descriptions, data element definitions, and data connection explanations.
Pattern and trend recognition
Another application would be to find patterns and trends in data sets that would be difficult for humans to detect and create suggestions for action based on those insights. This could help data engineers design and develop more efficient and successful data warehouses. It could also be used to automate common processes such as data purification and transformation in the process of establishing a data warehouse, which could speed up the whole process and allow data engineers to focus on more important duties.
Data correctness and reliability
Another application of AI in these circumstances is to increase data correctness and reliability. Natural language processing (NLP) techniques, for example, can be used to extract information from unstructured text data like customer reviews or social media posts. This can assist businesses in better understanding their clients and making more educated business decisions.
Data scaling and complexity
AI can also help data engineering and data warehouse initiatives scale more effectively. Human analysts are finding it increasingly difficult to keep up with the growing amount and complexity of data sets. Organisations could ensure they retain high levels of efficiency and productivity even as their data-needs expand by employing AI to perform some of the most time-consuming jobs.
AI bias
One major source of concern is the possibility of bias in machine learning algorithms. If the data used to train these algorithms is skewed in some way, the results they produce may be skewed as well. This can have major ramifications, especially if the data is used to make critical business choices.
Specialised human skill-sets
Another obstacle is the requirement for specialised skills and knowledge to apply AI effectively in certain circumstances. While many data professionals have a solid basis in classic statistical analysis techniques,applying AI frequently necessitates a more in-depth understanding of machine learning principles and methodologies. For certain organisations, this might be a substantial obstacle to adoption.
Job function shifts
AI is unlikely to totally replace data engineers in the immediate future. While AI can automate certain activities and make data engineers' jobs more efficient, it is difficult to fully mimic the creativity,problem-solving, and strategic thinking that is frequently necessary for data engineering work.
However, as AI becomes more ubiquitous in the sector, the job of data engineers may shift. Data engineers, for example, may need to learn new skills to work effectively with AI technology, or they may need to focus more on activities that require a human touch.
While ChatGPT and other language models have the potential to considerably aid in the development of data warehouses, they are not are placement for human skill and judgment. Data engineers and analysts will still be required to create and build data warehouses that are tailored to their organisation’s specific requirements. AI will most likely supplement rather than replace the work of data engineers. AI would likely free up data engineers to focus on higher-level jobs and strategic projects by automating regular tasks and delivering insights that would be difficult or impossible for humans to unearth.
