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Generative AI in data analytics: Insights for SaaS leaders

Generative AI in Data Analytics: Insights for SaaS Leaders

Generative AI is revolutionizing the way businesses work. It can automate and accelerate work across several functions and workflows in organizations. Breakthrough technologies like ChatGPT, DALL-E, and Bard are demonstrating these capabilities. 

In the data analytics space, generative AI can overcome some of the key bottlenecks that limit what we can accomplish with large-volume data in a finite time. Organizations can gain deep insights, make better decisions, and differentiate their user experiences. 

Through this blog, we explore how organizations can leverage generative AI effectively and responsibly to unlock the potential of data and stay competitive in the data ecosystem.

What is Generative AI?

Generative AI is a type of artificial intelligence (AI) technology that generates various types of unstructured content, including text, images, video, and audio, which are not represented in tables using rows and columns (structured data).  

The technology is powered by artificial neural networks called foundation models to identify patterns and relationships within existing data to generate new content. They are trained on huge sets of unstructured data using deep learning. 

For instance, Large Language Models (LLM)  can train on large datasets available on the internet and produce content. This is the basis of ChatGPT, which can create content from short text prompts, and Stable Diffusion, a tool creating photorealistic images based on a description. GPT-4, the next generation of ChatGPT, also supports inputs in the form of text, images, and audio now.

How does generative AI differ from traditional AI?

Generative AI can perform a wide range of tasks as opposed to traditional AI models that could perform just one task. For example, previous generations of AI can only predict one task like customer churn. Whereas generative AI can create a 10,000-word sales summary, develop a social media strategy, and write code to automate a workflow.

Further, users need not know about ML models to derive value out of generative AI. They can ask questions in any language and gain an advantage. 

Generative AI in data analytics 

Generative AI has a significant impact on data analytics. Data scientists and analysts to generate datasets and perform analytics in a more efficient and effective manner. 

The data professionals may only have time to conceive and evaluate a few hypotheses leaving behind unexplored areas. Generative AI can create and test hypotheses from all available data sources and derive insights. It also enables data analysts to create new data sources and understand patterns in data more effectively.

Generative AI finds a wide array of applications in the data analytics field. Here are some of them:

Predictive analytics

Generative AI can be used to analyze large data sets, identify trends and patterns, and make accurate predictions. Organizations can derive new insights and make data-driven decisions. For example, a business can analyze customer data and predict cross-selling opportunities.

Data Preparation

Data professionals can use generative AI to segment and enrich data during the data preparation process. The technology also enables users to mask and redact data, thereby removing sensitive or classified information.

Data modeling

Generative AI can be used to model complex systems to understand data and make informed decisions. It can create models of a particular and identify the challenges to optimize performance and efficiency. For instance, creating a model of traffic in a city and identifying areas with congestion.

Data visualization

Generative AI models can automate the creation of data visualizations that are easy to understand. The technology can be used to automatically format data visualizations based on best practices and provide recommendations to improve the experience. Recently, Tableau partnered with Salesforce to develop TableauGPT incorporating AI capabilities into the product.

Natural language Processing (NLP)

Generative AI in NLP involves providing natural language responses, summaries, and recommendations based on user inputs. It enables data scientists and analysts better understand customer sentiment. For instance, businesses can interact with customers and analyze their feedback to improve product performance and user satisfaction. 

Challenges in implementing generative AI

Generative AI is a double-edged sword. While it is a game-changer for every industry, there are also challenges that need to be addressed.

  • Generative AI models require large volumes of data to be effective. This can be an issue if data sources are incomplete or not fully available for analysis.
  • LLMs are prone to hallucination, answering questions with untrue assertions. Generative AI can provide misleading or incorrect information if the input is biased.
  • Organizations need to invest in specialized hardware and software systems to manage data generated by AI technologies.
  • Businesses should spend time and resources to hire the right talent and train employees on AI systems.
  • The lack of well-structured regulatory guidelines is a reason for privacy and security risks in AI implementation. 

Responsible AI practices

Generative AI poses a number of risks. Leaders should develop processes and guidelines to proactively mitigate harmful behavior and improve data transparency. One of the key takeaways from the Gartner D&A Summit 2023 is the importance of instilling responsible AI practices when using AI foundation models. 

Here are some of the areas to look into:

  1. Reliability

Models can provide different answers to the same questions, resulting in inaccurate and unreliable outputs. As mentioned earlier, hallucination and algorithmic bias are other challenges organizations should look into.

  1. Explainability

Generative AI is powered by neural networks with millions of parameters, which makes it difficult to explain how an answer is produced. Organizations should create an AI inventory to capture the extent of exposure and ensure the right level of explainability.

  1. Security and Privacy 

Generative AI is used to create malicious content such as deepfakes and hate speech. Further, it can include the personal data of customers. Businesses should adopt security measures and privacy policies to eliminate unwanted exposure to AI data. 

  1. Sustainability 

The development of foundation models may lead to social and environmental impacts. For example, training one LLM can emit around 315 tons of carbon dioxide.

Key points leaders should consider when starting their AI journey 

  • Organizations can start small or big. Generative AI has numerous use cases that are transformative and improve business performance. Depending on the business needs and aspirations, generative AI can be leveraged. 
  • The costs for implementing generative AI can vary. Various factors such as technical expertise, infrastructure, resources, and use cases affect the decision. Businesses should take into account the risks and embark on their AI journey.
  • A cross-functional team should be created in the initial stage to identify use cases and enable coordination across the organization.
  • New AI models are developed at record speed. Leaders must ensure a proof-of-concept is created to avoid getting stuck in the planning stages.
  • C-suite executives should stay up to date with the newest developments in data regulation policies. They must modify their internal policies to reduce risks and maintain compliance. 
  • Along with technical capabilities, organizations need a skillful and resilient workforce. Organizations should hire the right talent and train their existing employees to make the most out of AI tools.


Generative AI is no longer beyond the horizon; it has arrived, showcasing a world full of new possibilities. Organizations in the data analytics industry should embark on the generative AI journey by understanding its features and use cases. 

Generative AI can help data professionals across various applications, including data modeling, predictive analytics, and visualization. But this is not without challenges. The need for extremely large data sets, specialized hardware, and inherent bias are all limitations of generative AI. 

With the right talent, infrastructure, and regulatory compliance, organizations can leverage the power of generative AI and drive significant growth.

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