{"id":4363,"date":"2023-05-15T15:41:09","date_gmt":"2023-05-15T15:41:09","guid":{"rendered":"https:\/\/www.biconnector.com\/blog\/?p=4363"},"modified":"2023-09-07T06:28:49","modified_gmt":"2023-09-07T06:28:49","slug":"generative-ai-in-data-analytics-for-saas-leaders","status":"publish","type":"post","link":"https:\/\/dev.biconnector.com\/blog\/generative-ai-in-data-analytics-for-saas-leaders\/","title":{"rendered":"Generative AI in Data Analytics: Insights for SaaS Leaders"},"content":{"rendered":"\n

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<\/a>, DALL-E<\/a>, and Bard are demonstrating these capabilities. <\/p>\n\n\n\n

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. <\/p>\n\n\n\n

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.<\/p>\n\n\n\n

What is Generative AI?<\/h2>\n\n\n\n

Generative AI<\/a> 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).  <\/p>\n\n\n\n

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. <\/p>\n\n\n\n

For instance, Large Language Models (LLM)<\/a>  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.<\/p>\n\n\n\n

How does generative AI differ from traditional AI?<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

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. <\/p>\n\n\n\n

Generative AI in data analytics <\/h2>\n\n\n\n

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. <\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Generative AI finds a wide array of applications in the data analytics field. Here are some of them:<\/p>\n\n\n\n

Predictive analytics<\/h4>\n\n\n\n

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<\/a>. For example, a business can analyze customer data and predict cross-selling opportunities.<\/p>\n\n\n\n

Data Preparation<\/h4>\n\n\n\n

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.<\/p>\n\n\n\n

Data modeling<\/h4>\n\n\n\n

Generative AI can be used to model complex systems to understand data and make informed decisions. It can create models<\/a> 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.<\/p>\n\n\n\n

Data visualization<\/h4>\n\n\n\n

Generative AI models can automate the creation of data visualizations<\/a> 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<\/a> incorporating AI capabilities into the product.<\/p>\n\n\n\n

Natural language Processing (NLP)<\/h4>\n\n\n\n

Generative AI in NLP<\/a> 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. <\/p>\n\n\n\n

Challenges in implementing generative AI<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n