How AI is Revolutionizing Data Analytics

Just as AI-assisted programming has turbocharged the productivity of developers, so it is expected to do with the productivity of data analysts—and, by extension, business users.

Developers are already having a beneficial impact on their use of generative AI, which helps in completing mundane tasks very easily, learning new languages, and generally increasing their productivity. Now, it’s the turn of data analysts. The advent of LLM models into data analytics platforms is bound to supercharge what analysts can do. Imagine all those routine tasks, like code generation, SQL queries, or even creating charts. AI would just make these things faster and easier.

A New Era for Data Analysts

AI is not only about reducing the boring, repetitive work. It also lets data be analyzed by a much larger group. The capability for business users to manage reporting and analytics by themselves allows data science teams to pay more attention to complicated strategic tasks. This results in smoother and quicker workflows throughout the organization.

Redefining the Data Analyst Role

AI, in fact, will allow analysts to spend more time focusing on insights from the data, which is what businesses really need from them. Those days of tedious wrangling are out. Artificial intelligence will write code, instruct thinking, and even carry out some pre-report preparation, which lets the analyst focus on matters that count. With AI, analysts will do less technical drudge work and more high-level business thinking.

Think about this: analysts spend hours writing code and hunting down documentation for certain libraries. AI can shorten this process to start with proposals that come out of their AI. Just like with developers, an analyst can dig further into the code if they need it or want it. AI will optimise their workflow to change how data analysts work; they’ll centre around business needs and research goals, not so much the mechanics of a project.

But this must be treated with caution. Coding skills will still be in demand by a data analyst and will not disappear overnight. But AI could do code generation and summarization of findings for more routine tasks already today. The requirements for coding would decrease over time, and the business domain-guided analyst would focus AI on delivery of results for the sake of business.

Upgrading the Data Analyst’s Toolkit

AI will be embedded inside tools that data analysts use day to day—in Jupyter Notebooks, for example. The notebook format won’t go away, but it’s going to transition to a much more prompt-driven interface while preserving the code cells and outputs we know now. You can then think of your notebook as being similar to ChatGPT but with a priori knowledge of your data and associated tools. That said, democratization will mean bringing data analysis to a wider audience.

Analytics platforms will also enhance the way analysts communicate results. Finding something in the data is one thing; convincing others about the importance of what you have found is another. It will customize reports for the audience and order the content according to the intent and audience of the report. While some of these functions can be performed by AI independently, humans will continue to be essential in solving more significant and challenging problems.

The Semantic Layer’s Role As large language models become more common in the analysis of data, it will be the organizational semantic layer, or the metrics layer, that will be its critical ingredient in moving the self-service in data analytics while integrating AI within analytics tools.

The semantic layer makes raw data meaningful for business insights. It links business terms with the underlying data and ensures a consistent definition across the enterprise. In companies that have different storage methodologies deployed for their data and plenty of different tools around, the semantic layer becomes critical. This is, of course, especially true for departments of finance, marketing, and IT, for which the plurality of tools and technologies is common practice.

The semantic layer should be providing a single definition of the data in consistent business terms across all applications. Think of it as a translation layer between raw data and business applications; this guarantees uniform application of metrics and dimensions so that the higher-level conceptual details, such as KPIs, are consistently accurate. For example, any report or analysis using the term “total revenue by month” would maintain a consistent definition.

The Rise of AI-Powered Analytics Just as AI supports developers with coding questions, it will aid data analysts and business users with report queries. Analysts will step in when needed, but AI is improving quite rapidly. In time, AI will make its way to integrate more company data silos, including CRM systems, support tickets, and ERP systems. Data analysis platforms evolve toward knowledge bases of companies combined with external sources, such as data from stock exchanges, news feeds, and market analyses. 

Huge amounts of data on AI-based platforms will narrow the current gap between data and business teams, thereby fostering better and more effective collaboration. In the end, AI results are a human responsibility. When generative AI is applied in consequential decision-making, as in what should be published in the news, the ability to explain the results becomes of paramount importance. The format will remain in the notebook fashion but will be much faster and automated. It will be understandable and traceable to various steps that lead to conclusions, enabling us to fully benefit from AI in data analytics. While AI is rapidly changing the game of data analytics to be faster and more available, the human touch is still needed to guide and interpret these powerful tools.