Top 3 Advantages of Generative AI for Data Analytics

Not even the figures lie. Data has emerged as a highly valuable asset, with 58% of organisations that base their revenue goals on data have a greater likelihood of doing so than those that do not. 

However, despite the ample availability of data, 41% of business executives claim they are unable to comprehend their data due to its complexity or accessibility challenges, and an additional one-third are unable to derive meaningful insights from it. Everything changes when generative AI technology is implemented.

The utilisation of generative AI in data analytics enables users to gain insights and investigate their data in a matter of seconds. Further elaborating on three distinct advantages of generative AI in the context of data analytics, we shall examine several use cases that underscore its worth.

1) Streamline and automate your data analytics

Generative AI, when integrated with a data analytics platform, exposes automated analytics. Users of varying proficiency levels are able to engage with the data via prompts, which are text-based inquiries and directives. They investigate and collect insightful information to aid in the comprehension of business performance and decision-making. 

When utilised in conjunction, generative AI and the analytics platform have the capability to discern patterns, detect inconsistencies, identify latent trends, and illuminate the fundamental origins of business obstacles.

Consider the implementation of generative AI in the context of data analytics.

Introduced here is Ebony, the sales manager at Renaissance, a fictitious apparel company, who is tasked with the meticulous monitoring of sales performance.

Ebony establishes a notification that notifies her of significant fluctuations in the organization’s sales using generative AI. Upon accessing her data platform, she is notified that “sales of footwear have experienced an unusual surge.” Additionally, she receives the following notification: “So far this quarter, footwear sales have surpassed the typical range by 25%.”

Due to the automated notification provided by generative AI, Ebony is able to efficiently analyse the data directly within the platform. By employing inquiries proposed by the AI, such as “How have sales fared in comparison to this period last year?”She can learn more about these changes. Additionally, she is permitted to input her own inquiries, such as “Which products are playing a role in the surge in footwear sales?” By means of this procedure, she gains knowledge that Renaissance’s footwear sales are being propelled by the demand for silver cowboy boots.

By utilising these invaluable insights, Ebony is able to provide her sales team with precise and current information that is crucial for their success.

2) Acquire insights wherever and when necessary

As a result of generative AI’s assistance in segmenting and categorising data, valuable insights that might have otherwise been overlooked can be uncovered. Additionally, you may depend on generative AI to propose supplementary inquiries that aid in comprehending your data. 

Let us revisit our fictitious organisation, Renaissance. Ebony informs Connor, the retail buyer for her organisation, of the novel insights so that he may implement them. Renaissance’s data analytics platform, which is powered by generative AI, expedites the identification of sales trends for silver cowboy boots over the prior quarter from hours to minutes.

Connor further analyses the data by employing a suggested inquiry: “In what ways have sales of silver cowboy boots changed since the previous month?This method illuminates more insights using Renaissance data.  Most importantly, it does so with a visual representation of the trend and straightforward language, making it simple for him to comprehend. He may also ask for further information on sales of silver cowboy boots.

The generative AI solution can identify which of Connor’s retail locations are contributing to the increase, should Connor be interested. Additionally, it may provide notice that the “religious boot retailer Renaissance’s silver cowboy boot inventory fill rate is beginning to decline and may fall below the threshold.” Subsequently, he is capable of expeditiously placing orders for additional boots in order to satisfy consumer demand, particularly in the locations where sales have surged the most.

3) Convert observations to visuals

The advent of generative AI fundamentally transforms the way in which we utilise data. Presenting complex data in formats generated from natural language queries is now possible. These formats comprise data visualisations, charts, and graphs that illuminate all concealed patterns and insights. Subsequently, the discoveries are readily comprehensible to all users, enabling them to instruct others. 

Allison, the marketing manager at Renaissance, exhibits inquisitiveness regarding the recent surge in sales of silver cowboy boots. She consults her analytics platform, which uncovers additional information via generative AI. She discovers, via a series of inquiries, that sales surged subsequent to the boots’ prominent appearance in a recent advertising campaign. The data is presented in a lucid manner, employing visual aids and straightforward language to enable her to discern the precise moment when sales commenced to surge. Allison is not only adept at navigating these insights with ease, but she can also provide her team with the pertinent data for their future marketing endeavours. 

Over time, generative AI models will revolutionise the data analytics domain across all sectors and occupations, including marketing, sales, customer operations, and research and development. Generative AI enables and simplifies this process for all parties involved, enabling businesses to better comprehend their products and services through the use of strategic insights as opposed to speculation and intuition.

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