Generative artificial intelligence (AI) has garnered so much interest in recent times. This technology has significant potential in the business setting, especially in the data analytics landscape. It can enrich all kinds of enterprise data analytics, including prescriptive, descriptive, and predictive analytics. While generative AI is still in its early stages, it could impact nearly every aspect of data analytics as we look into the future.  

This newly introduced technology can generate synthetic data, including variables that were initially immeasurable. This enables it to improve model performance and support decision-making. Here, we explore some ways in which generative AI could impact data analytics. 

Why is Generative AI Important in Data Analytics? 

Data analytics services are usually a resource-intensive and complex process. Luckily, generative AI has the potential to streamline that process. With generative AI, you can automate and speed up data analytics, allowing users of all skill levels to explore data through text-based commands and questions, known as prompts. Users can interact and collect valuable insights to support decision-making and better understand organizational performance. Therefore, this technology empowers everyone in the organization to make decisions based on data.

5 Ways in Which Generative AI Could Impact Data Analytics

1. Collecting the Data  

Data analytics is the art of analyzing unprocessed data to derive conclusions. This process involves collecting vast datasets, which can be time-consuming. Despite all the advances in modern technologies such as AI and automation, data collection and engineering remain one of the most time-intensive aspects in the data analytics domain. Specific elements, like data cleaning and classification, can be resource-intensive and demand significant human intervention and effort. Luckily, this could change, thanks to generative AI. 

Generative AI could bring efficiencies in the data collection process by leveraging large language models. This technology could be used to append third-party industry classification data to CRM datasets. As a result, it can help minimize processing times. 

 2. Analyzing the Data  

Analyzing data and gaining meaningful insights from it is often challenging. Creating the software code for analytics models requires highly skilled professionals. Otherwise, the models can be faulty and take ages to complete. In today’s highly competitive environment, you need a faster and more effective solution to speed up the analysis process. And that’s where generative AI may come in handy.  

Generative AI tools could be used to generate complex code for building analytics models. A recent study by GitHub showed that developers using the GitHub Copilot (based on OpenAI’s Codex) gain significant time savings. 88% of respondents indicate they’re more productive, while 96% noted they’re faster with routine and repetitive tasks.  

3. Augmenting Data  

Generative AI could be utilized in creating synthetic data that augments already existing datasets. This will allow for a more extensive analysis. This ability is specifically valuable in cases where data is difficult to collect or measure.  

Many issues may impede the ability to collect and observe data. These may include privacy, ethical, and legal concerns, small sample sizes, budget constraints, and physical limitations on sensors. And that’s where generative AI could be used to create synthetic data that imitates the actual data, overcoming the challenges of collecting it. 

4. Generating the Insights 

The ultimate goal of data analytics is to gain valuable insights that inform key business decisions. However, the process of generating insights often involves significant manual activity. While there have been talks about automated insights, this is still not the case, as current AI techniques only deliver basic-level customized insights in diagnostic and descriptive analytics. Therefore, complex prescriptive and predictive analytics insights are still largely driven by humans. But how could generative AI impact this scenario? 

Well, generative AI has the potential to leverage contextual data and imitate human inferencing processes to produce actionable insights and contextualize analytics results. What’s more, generative AI could be used to facilitate persona-based insight contextualization, enhancing the impact of data analytics. 

5. Delivering Insights and Powering Decisions 

Traditionally, analytics reports were generated manually. However, this will likely change thanks to generative AI. For instance, generative AI models could be used to create analytics reports and powerful business intelligence (BI) outputs. By automating the reporting process and contextualization, generative AI services could make a significant impact on data analytics. Also, it will promote near-to-real-time insights by eliminating the need for human intervention. 

How to Start Using Generative AI for Data Analytics 

It’s no secret that generative AI could transform data analytics for good. Here’s a quick guide on how to start using this transformative technology: 

Include generative AI in your data strategy. Whether the adoption of generative AI will not be immediate or will be on a smaller scale, outline it in your enterprise data strategy, plans, and frameworks. This way, it will form part of your organization’s goals. 

Address generative AI issues proactively. Generative AI is still an emerging technology. Therefore, it poses various challenges like accuracy, bias, and security. Since these issues can derail the generative AI-powered analytics solutions, organizations must address them proactively.  

Select programs that will foster business impact. Many analytics projects can leverage generative AI. However, organizations must be choosy when selecting those that help drive key business metrics. 

Final Thoughts 

Generative AI could potentially transform every aspect of data analytics. This technology could be used in data collection, augmentation, analysis, and reporting, streamlining the entire process. From creating synthetic data that mimics actual data to generating user-friendly visualizations, generative AI’s potential cannot be ignored in data analytics. If you’re looking to leverage this technology, it’s essential to include it in your enterprise data strategy, address its related challenges proactively, and select programs that drive business impact through analytics. This way, you can optimize your data analytics landscape through generative AI.