Artificial Intelligence

Gartner: How Artificial Intelligence Will Impact Analytics Users?

3 Mins read
AI in analytics

Analytics and artificial intelligence are constantly intersecting, affecting various areas. To take advantage of new opportunities and minimize potential risks, leaders in data and analytics must consider the effects of AI on analytics, data science ecosystems, user behavior, roles, and decision-making.

Spreadsheets remain the primary tool for data analysis due to their simplicity and widespread use. The popularity of web and app-based stand-alone GenAI chatbots allows users to easily and intuitively analyze spreadsheet data for basic tasks. This bridges the gap between traditional data entry and sophisticated analysis without requiring specialized analytics and business intelligence (ABI) and data science and machine learning (DSML) software, training, or difficulties in purchasing licensing.

Users have the ability to analyze data within their business processes without the limitations of traditional analytics software, and they are doing so excessively. This quick implementation of these capabilities is resulting in a rise in data and analytics work being carried out outside of ABI platforms, analytics sandboxes, or security policies. As a result, good governance is also being bypassed, whether intentionally or unintentionally.

Gartner predicts by 2025, 40% of ABI platform users will have circumvented governance processes by sharing analytic content created from spreadsheets loaded to a generative AI-enabled chatbot.

Spreadsheets, often referred to as “the cockroach of analytics tools” — are perennial survivors despite disruptive markets, and they spread when the right conditions arise. With the ability to analyze spreadsheets directly through GenAI chatbots, the use of spreadmarts is expected to grow. This signals a need for closer collaboration between data analysts and IT departments to manage and govern the proliferation of these “generative data silos.”

Gartner predicts that, by 2026, more than 70% of independent software vendors (ISVs) will have embedded GenAI capabilities in their enterprise applications — a major increase from fewer than 1% today. The convenience of AI-enabled natural language query (NLQ) without an ABI platform poses a displacement risk for traditional vendors and investments made by data & analytics (D&A) leaders. Analytics consumers working in this way will reduce their reliance on complex, well-governed analytics software.

Recommendations for Leaders Governing ABI

The new role of AI in analytics requires D&A leaders to think about their D&A ecosystems beyond ABI platforms. They must take the following recommendations into considerations to adapt to the evolving landscape.

Focus on AI training and upskilling: Training modules should be developed for both business analysts and augmented analytics consumers in order to fully utilize the benefits of GenAI. This will ensure that they are able to effectively use these tools for data analysis in a secure manner.

Employ strategic planning for AI-enabled analytics: Leaders in analytics and business intelligence must incorporate the use of NLQ chatbots outside of ABI platforms as a technological catalyst into their strategy and operating model. This will be a crucial component of future data analytics workflows.

Ensure that integration efforts promote composability: ABI platforms must pursue integration with LLMs to remain relevant in a market where users increasingly prefer analytics embedded within their natural workflows. Vendors must describe how their platforms include a large language model (LLM) integration for data retrieval and prompt engineering, while buyers should assess what is available as a plug-in to a third-party application (such as ChatGPT).

Promote collective intelligence through analytics collaboration: Initiatives should be in place to encourage the sharing of analytics insights generated from GenAI chatbots, fostering a culture of collaboration and shared learning. Training must establish adaptive governance mechanisms to avoid hallucinations from AI chatbots and improve interpretability.

Gartner analysts will be discussing AI related best practices for analytics users at the Gartner Data & Analytics Summit, taking place April 24-25 in Mumbai, India.

It is crucial for D&A leaders and their organizations to stay updated on the latest advancements in AI-enabled NLQ and chatbot technology. Otherwise, they may fall behind and face potential violations of data and analytics governance policies, as well as proliferation of content due to the constantly evolving analytics technology and digital landscape.

Author: Mike Fang, Sr. Director Analyst at Gartner

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