After taking the world by storm in 2022 with the introduction of OpenAI’s ChatGPT, generative AI is coming out of the hype phase to become a reality today. Generative AI has evolved to address many use cases like text summarization, content creation (including text, images, video, music, and animation), translations, and code generation in an automated manner and at faster speeds. Data-driven organizations are always looking for simpler and effective ways to analyze their data, extract insights, and maximize their analytics ROI. So there’s an increasing expectation to find implementations of generative AI for more complex business use cases like data analytics and insight generation.

In this blog, we will uncover how generative AI can impact data analytics strategies.

What is Generative AI and why is it relevant for data analytics?

Generative AI belongs to the broader set of artificial intelligence technologies. Generative AI refers to the technique of learning from existing data sets and using them as a base for generating new and unique content artifacts. It learns from large language models and existing sources of original content to generate new derived versions of content strategies, designs and prototypes based on the learned information.

Organizations can leverage generative AI to simplify end-to-end data workflows, scale processing of enterprise data, and improve productivity of teams. For example, the National Bureau of Economic Research recently conducted a study of the use of a generative AI-based customer support system by 5,179 customer support agents. Issues resolved per hour were considered as a measure of the agents’ productivity. The study revealed the use of the generative AI-based system led to an increased productivity of 14% among customer support agents, improved positive feedback from customers, reduced requests for managerial intervention, and improved employee retention. The system helped novice agents in particular to expedite learning and skill development. This shows how generative AI can help speed up processes and enrich the workforce.

How can Generative AI be used in data analytics?

Data analytics involves collecting, cleaning, processing, and preparing huge volumes of data for extracting business insights. Analysts need to carefully examine each data set for identifying patterns and emerging trends. This makes the analysis process more laborious, time-consuming, and resource-intensive. Generative AI can revolutionize the way organizations perform data analytics by enabling automation of manual tasks, expediting the analysis process, and boosting insight generation.

Here are some areas where generative AI can contribute to increase efficiency, accuracy, and speed.

  • Preparing and refining data: By leveraging large language models, generative AI can clean, tag, and classify structured and unstructured data in an automated manner. This helps cut down data processing time, ensures accuracy, and frees up data engineers to focus on more complex tasks.
  • Analyzing data: Generative AI can run detailed analysis within seconds to identify patterns, flag anomalies, and spot trends. Analysis by humans can be influenced by biases, errors, and limited knowledge. However, generative AI can circumvent such influences to explore and analyze data better.
  • Generating insights: With huge amounts of data coming in every minute, it is almost impossible to analyze it all at once. Generative AI has the capacity to perform real-time analysis and generate real-time insights that would otherwise be missed or delayed by human analysts.
  • Querying data: With generative AI, users can carry out fast and simple conversations with their enterprise data to get instant answers. This is beneficial especially for non-technical users who can use natural language to query data.
  • Delivering insights: Generative AI can create instant summaries in natural language, detailed reports, and engaging visualizations automatically to deliver insights in a clear, concise, and easily understandable manner.

What are the benefits of Generative AI in data analytics?

Here are some benefits of using generative AI in data analytics:

  • Simplify and automate processes: Generative AI helps reduce the time, efforts, and resources spent in carrying out mundane and routine tasks. By automating processes like data cleaning, classification, and preparation, generative AI helps organizations improve efficiency and scale analytics easily.
  • Improve accuracy of insights and decisions: By ensuring thorough analysis, generating accurate predictions, and making insights available in real time, generative AI empowers decision makers to make better-informed decisions at the right time. This way, human errors, time delays, and wrong actions can be prevented.
  • Increase productivity: Generative AI provides a simple and conversational way of interacting with enterprise data. This empowers users across organizations with quick and easy access to insights. Users become self-reliant, data-driven, and proactive in their work, leading to increase in confidence and productivity.
  • Produce content faster: Be it creating rich visuals, interactive animations, engaging visualizations or summarizing findings, generative AI can produce content better and faster. It can also be used to create automatic presentations and insights summaries that business users can directly use to present and deliver their work easily.
  • Improve recommendations and overall efficiency: Generative AI can help personalize insights, offer recommendations, and guide focused actions. This can help create targeted strategies, improve customer experiences, increase operational efficiency, and improve overall performance of the organization.

What is the future of Generative AI in data analytics?

The 2023 Gartner Hype Cycle identifies Generative AI as one of 25 must-know emerging technologies that are expected to greatly impact business and society. As per Gartner, generative AI can have profound business impacts on content and product development, automation of manual work, and enhancement of customer and employee experiences as it reaches mainstream adoption in two to five years.

While it has its benefits, Generative AI still faces some challenges especially with the use of large language models. LLMs have extensive vocabulary, but they are built for masses and general purpose use cases. For use cases in enterprise data analytics, LLMs have limitations, and in their current form, LLMs cannot cater to the business analytics requirements of an organization. There are concerns being raised about quality of responses, lack of transparency, data breaches and security, data governance, and copyright compliances.

Leveraging LLMs in a business context with real-world data sets requires the right applications and associated guardrails. Modern data analytics platforms should incorporate these guardrails right at their architecture level for integrating LLMs and extending the benefits of generative AI to its users. By enabling organizations to build smaller domain-specific models and customized language models rather than use general-purpose LLMs, modern data analytics platforms can bring the best of generative AI, without the unnecessary exposure to risks and security breaches.

How MachEye uses Generative AI to enhance data analytics?

At the core of MachEye’s modern analytics platform is its own custom language models and AI-powered architecture that were created from the ground up after carefully studying the customer requirements and analyzing the technology trends. SearchAI, MachEye’s user-friendly analytics experience, provides 100% accurate answers, insights, and generative content, along with enterprise-grade data governance and scalability.

MachEye integrates the power of generative AI and LLMs in its following offerings

  • Low-prep / no-prep onboarding and automated data catalog: MachEye directly connects and integrates data from multiple sources. Its automated data catalog identifies, classifies, and enriches data automatically. It also measures the data quality on a continuous basis and highlights data observability issues to ensure that accurate data is available for analysis and insight generation.
  • Intelligent search: MachEye’s intelligent search understands natural language queries like “how much were the iced coffee sales in Oregon over the last two quarters” or even “did the iced coffee sales improve in the last couple of quarters?” It parses vocabulary to map to SQL data, and generates the response back in natural language text summaries and interactive visualization. MachEye also learns from user queries to offer suggested searches that are based on usage patterns and rankings.
  • Actionable insights: MachEye constantly improves its insight generation based on feedback, relevancy, and deduplication to offer more than just what the user asks. It goes beyond answers to identify a series of signals that are relevant to the context of the user query and runs additional datasets with AI models on the fly to generate related actionable insights.
  • Interactive data stories: Going beyond charts, MachEye presents insights and why analysis in the form of interactive data stories. These audio-visual data stories are generated on the fly with best-fit visualizations, custom visuals, text summaries, and audio narratives.
  • Headlines: Learning from user searches, usage patterns, business metrics, and interests, MachEye generates relevant headlines automatically as they happen in data. This makes searching for insights optional and users don’t need to spend time thinking about what questions to ask.

To conclude, generative AI and LLMs can be coupled with the right modern data analytics platform like MachEye to handle enterprise data analytics use cases. This way, organizations can benefit from the speed and ease of generative AI while ensuring the data governance, enterprise security, and robust performance of a modern data analytics platform.