.webp)
Experience the power of Luzmo. Talk to our product experts for a guided demo or get your hands dirty with a free 10-day trial.
.webp)
If you do data analysis for a living in some shape or form, you may be concerned about artificial intelligence and how it may shape your career in the future. Machine learning and tools such as ChatGPT have made it possible to quickly analyze large datasets and do data visualization.
At the same time, if you have a software product and hire data analysts, you may be wondering if these advancements and AI tools can fully replace data scientists and engineers in your team. After all, if AI can do the job for free or at a low cost, why pay six figures a year for data analyst jobs?
Today, we take a look at both sides and answer the question: will AI take over data analytics?
AI has made it possible to automate many workflows and data science. Here is what AI in data analytics can do as of 2024:
In other words, these automation features can do a lot of the heavy lifting and eliminate manual tasks. But despite these technological advancements, the role of data analysts may be around for a while. Here is why.
AI is excellent at tackling some of the bigger tasks in data analysis. But if you want to make your next big strategic decision based on AI data analytics, you might want to hold off on that because these are some of the things AI cannot do.
In other words, the field of data analytics still needs the nuanced approach of data scientists, and not all of the work can be outsourced to AI tools.
Generative AI can do quite a lot, but it’s far from a place where it can replace human data analysts. Here is why we feel that way.
There are many data analytics and visualization tools available that have natural language processing (NLP) capabilities. The idea is simple: any stakeholder in your company can type a question in plain English about the available datasets, and the AI algorithms produce an answer to that question, also in plain English.
In theory, this makes it easy for everyone to make business decisions based on data because they can do basic data analysis just by asking questions. For example, they can do predictive analytics based on historical data.
In practice, before someone can ask questions, a few steps need to happen first: data cleaning, modeling, and preparation, and this is where a human touch is crucial. Even with the best AI systems, a data analyst is required to ensure the data is clean, consistent, and good quality.
Large language models (LLMs) that are employed in data analytics are trained on existing data. Unfortunately, some of that data can be biased, and that means your analyses and visualizations end up biased as well.
To remove biases in data analytics, AI needs to consider data points in real time and consider multiple factors before any decision-making.
AI technology can automate repetitive tasks and create dashboards, but it cannot tell whether the data is good quality or not. Unfortunately, clean and quality data is one of the basics of good business intelligence.
Before employing AI models, make sure to have a data scientist check the data sources and the quality of the data first. Garbage in = garbage out.
In some industries, it just won’t be possible to use AI for analyzing and visualizing data and making business decisions. Certain industries require human involvement and critical thinking, and using AI to work on data analytics can be against company guidelines and industry best practices.
There are many different use cases for AI but out of the box, tools such as OpenAI can’t meet unique demands. If you have highly customized AI needs, you’re going to need data scientists and analysts to develop specialized machine learning models for your needs.
The impact of AI on data analytics is undeniable. In the near future, it may be possible to shape machine learning algorithms to understand data in real-time and generate accurate outputs and visualizations. However, we’re not there yet.
Data scientists shouldn’t fear for their jobs because no matter how well-trained an algorithm is, that’s all that is: an algorithm. It can still make mistakes, ignore data coming in from real-time, not understand contexts, and make predictions based on biased and flawed data.
The data scientist of the future is going to have to be AI literate. They’ll need to know what AI models can and cannot do so they can fill that gap. With some upskilling, good data scientists will be around for many more years.
As for businesses, it’s important to know the possibilities and limitations of AI in data analytics across various industries. Nowadays, you can easily add AI features to your software thanks to many GPT plugins and scripts.
But more importantly, you can add AI data analytics features too. Tools such as Luzmo let your end users create dashboards, and analyze and visualize data in easier ways, all in your own app.
It’s our opinion that AI will make data analytics more available to the average software user. Instead of fearing complex tools such as Tableau or Microsoft Power BI, your app users will be able to add data sets and ask questions about their data. The results may not always be on the same level as having a team of data scientists, but it will be a major breakthrough for most users.
If you want to empower your end users and give them the possibility to analyze and visualize data with AI, you don’t have to spend six figures per year, learn Python or SQL or anything similar.
All it takes is signing up for Luzmo’s free trial and adding embedded dashboards in your software. You get a sleek, modern user interface while your end-users get the ability to explore, visualize, and learn from their data.
Book a free demo with our team to find out how Luzmo can help you!
Experience the power of Luzmo. Talk to our product experts for a guided demo or get your hands dirty with a free 10-day trial.
