The Future of Data Analytics with Generative AI: My 10 Predictions

As Generative AI continues to develop and mature, it will undoubtedly have a significant impact on the field of data analytics. Generative AI products like Bard, ChatGPT, or Anania will revolutionize the way we approach data analytics in general. Overall, I expect data analytics to become much easier and accessible to general users and not only technical data teams.

I have summed up some of my thoughts and research into 10 predictions that I believe the future holds for data analytics with Generative AI.

Image is generated using Stable Difussion

1. Data exploration will fully be done using question answering
   Dashboards were never meant for data exploration. Their purpose has been reporting and monitoring insights, not exploring and diving deeper. That is why no platform or tool has yet been able to replace the power of data exploration with SQL or Python. However, leveraging Generative AI, the chat-based question-answering systems will enable both technical and non-technical users to ask natural language questions and receive answers in real time without writing a single line of code.
2. Data visualization will be generated and modified using chat
   PowerBI, Tableau, or even Excel are very easy to use for generating charts. Yet, have you tried to customize their charts? Highlighting a single bar in a bar chart is a total headache. As highlighting several outlier data points in a scatterplot. Generative AI will also play a crucial role in the creation but also, especially, in the modification of visualizations and charts. Users will be able to describe what they want in natural language, and the system will modify the chart accordingly.
3. AI systems will perform end-to-end analysis and generate reports
   Robot data analysts will learn how to perform an end-to-end analysis and generate full reports, including quarterly financial reports or cohort analysis reports for recent A/B test users. This will reduce the need for human intervention in the analysis process and the requirement of providing step-by-step tasks for data analysis.
4. Every BI tool will integrate a conversational AI feature
   As conversational and chat-based interfaces become more prevalent, every BI tool will have a conversational/chat feature integrated to provide users with a more natural way of interacting with data. This will also impact the data visualization generation and modification described above.
5. Fundamental models will be created for tabular data
   Generative AI models similar to GPT4 or StableDiffusion will also be created for tabular and structured data, allowing users to leverage these models to perform predictive analytics using a small amount of data.
6. Demand for data engineers will Increase
   As data analytics will enjoy wider demand, the market need for improved data pipelines will increase. This will drive the demand for data engineers as they will be key to correctly collecting and feeding data to AI systems.
7. Demand for the technical skills of analysts will decrease
   With the analytics process becoming easier, the demand for technical skills of analysts will decrease, but their ability to think analytically and make suggestions based on data insights will increase.
8. Data teams will not be separated based on the data source or type
   Currently, many companies enjoy separate teams for computer vision engineers, NLP specialists, and data scientists. With the increased adoption of multi-modal AI systems, data teams will not be separated based on data source or type. A single team will be responsible for 360 analysis of all data whether textual, tabular, image, or else.
9. More businesses will focus on digitization and utilization of their data
   As analytics becomes easier and more accessible, the success of the data-driven business will prompt the rest to put more focus and effort into digitization and utilization of their own data.
10. Universities will focus more on teaching decision-making rather than tools
    Finally, universities and training centers will focus more on teaching analytics students best practices, use cases, strategic planning, and decision-making rather than tools and techniques for data analysis.

In conclusion, the future of data analytics with Generative AI is bright, and the predictions listed above are just the beginning. As we continue to develop and refine Generative AI, it will undoubtedly play an increasingly critical role in the analysis and utilization of data. Of course, given the importance of data and insights, there are also a lot of challenges that will come up in the process, but that is a topic for another article.