How Data Analytics Benefits from Generative AI

How Data Analytics Benefits from Generative AI

Generative artificial intelligence (AI) has been around for only a couple of years, but the technology is already boosting the corporate world’s analytical capabilities. First introduced to the world through ChatGPT, generative AI uses advanced algorithms to study vast data sets and produce texts or results based on simple user prompts.

ChatGPT and other generative AI applications may be more popular for their text generation abilities, but the applications have a much wider range of applications. In the data-analytics world, generative AI has made it easier to use analytics tools and improved automation quality, benefits that can be impactful through the entire data analytics life cycle.

In the wake of AI’s growing popularity within the business world, a growing number of companies are now starting to look for evidence of artificial intelligence’s return on income (ROI), especially businesses with rare and pragmatic brands. An Alteryx poll of leading board members chief information officers (CIOs) and board members found that artificial intelligence has already affected goal achievement in 80% of companies.

The poll discovered that using artificial intelligence to develop and synthesize new corporate insights represented the second and third most significant use cases in the corporate world behind content generation. Alteryx researchers looked at the key issues generative AI is currently tackling, how the technology works and where corporate leaders can implement it to maximize their data analytics.

Long before companies such as OpenAI began developing AI tools for corporate and mainstream customers, organizations had already recognized how data and analytics could help them mitigate risks, manage expenses and boost revenue performance. Unfortunately, leveraging data and analytics-driven decisions at scale can be stressful, time consuming and often ineffective due to the limited number of experienced experts in analytics, AI and data science as well as the fact that businesses still use siloed legacy systems.

Furthermore, data volume and complexity continue to grow as corporations struggle to overcome the aforementioned challenges, coupled with the absence of robust governance policies, making analytics and the generation of insights even more difficult. As a result, companies often have poor data quality that isn’t trustworthy enough to be used in decision-making.

Thanks to generative artificial intelligence, corporations now have the chance to address the challenges hindering data-driven decision-making at scale and improve their analytical tools’ efficiency and usability.

Generative AI has made these tools easier to use by allowing the execution of complex analytical tasks using basic English, eliminating the need to learn Python and accelerating the learning curve for analytical tools. The technology has also improved the efficacy of data analytics tools by boosting automation quality through every step of the data-analytics life cycle. As generative AI evolves and becomes more efficient in the coming years, we can expect corporations to continue implementing it in their data analytics. Companies that are focused on data analytics, such as FingerMotion Inc. (NASDAQ: FNGR), are taking the lead in incorporating generative AI in the products that they offer to their clients.

NOTE TO INVESTORS: The latest news and updates relating to FingerMotion Inc. (NASDAQ: FNGR) are available in the company’s newsroom at https://ibn.fm/FNGR

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