The convergence of analytics and artificial intelligence (AI) has profound implications across various domains. As leaders in data and analytics, it is crucial to understand the effects of AI on analytics, data science ecosystems, user behavior, roles, and decision-making. By embracing new opportunities and addressing potential risks, organizations can leverage AI to gain a competitive edge.
Traditionally, spreadsheets have been the go-to tool for data analysis due to their simplicity and widespread use. However, the emergence of web and app-based stand-alone GenAI chatbots has transformed the way users analyze spreadsheet data. These chatbots allow for intuitive and easy analysis, bridging the gap between traditional data entry and sophisticated analysis.
One of the key advantages of GenAI chatbots is that they eliminate the need for specialized analytics and business intelligence (ABI) and data science and machine learning (DSML) software, making data analysis more accessible to a wider audience. Users can now analyze data within their business processes without the limitations imposed by traditional analytics software.
This increased accessibility has led to a surge in data and analytics work being conducted outside of ABI platforms, analytics sandboxes, or security policies. While this quick implementation of AI-driven capabilities offers significant benefits, it also poses governance challenges. Good governance practices may be bypassed, intentionally or unintentionally, resulting in potential risks.
Gartner predicts that by 2025, 40% of ABI platform users will bypass governance processes by using generative AI-enabled chatbots to share analytic content created from spreadsheets. Spreadsheets, often referred to as “the cockroach of analytics tools,” have proven to be resilient despite disruptive market trends. With the ability to analyze spreadsheets directly through chatbots, the use of spreadmarts (generative data silos) is expected to grow.
Looking ahead, Gartner predicts that by 2026, more than 70% of independent software vendors (ISVs) will embed GenAI capabilities in their enterprise applications. This represents a significant increase from the current adoption rate of less than 1%. The convenience of AI-enabled natural language query (NLQ) without the need for an ABI platform poses a risk for traditional vendors and investments made by data and analytics (D&A) leaders.
Recommendations for Leaders Governing Analytics
To navigate the evolving landscape of AI-enabled analytics, D&A leaders should consider the following recommendations:
- Focus on AI training and upskilling: Develop training modules for business analysts and augmented analytics consumers to fully harness the benefits of GenAI. This will facilitate secure and effective utilization of AI tools for data analysis.
- Employ strategic planning for AI-enabled analytics: Incorporate the use of NLQ chatbots outside of ABI platforms as a technological catalyst into the organization’s strategy and operating model. This will be critical to future-proof data analytics workflows.
- Ensure integration efforts promote composability: ABI platforms should integrate with large language models (LLMs) to stay relevant in a market where users prefer embedded analytics in their natural workflows. Buyers should assess available LLM integration options as plug-ins to third-party applications.
- Promote collective intelligence through analytics collaboration: Encourage the sharing of analytics insights generated from GenAI chatbots to foster a culture of collaboration and shared learning. Implement adaptive governance mechanisms to address hallucinations from AI chatbots and improve interpretability.
Gartner analysts will be discussing AI best practices for analytics users at the upcoming Gartner Data & Analytics Summit in Mumbai, India, on April 24-25.
To stay ahead of the evolving analytics technology and digital landscape, it is crucial for D&A leaders and organizations to stay updated on the latest advancements in AI-enabled NLQ and chatbot technology. Failure to do so may result in falling behind and potential violations of data and analytics governance policies.
Author: Mike Fang, Sr. Director Analyst at Gartner
FAQ
What is ABI?
ABI stands for analytics and business intelligence. It includes software and tools that enable organizations to analyze and interpret data to make informed business decisions.
What is DSML?
DSML stands for data science and machine learning. It involves the use of algorithms and statistical models to extract insights and patterns from data.
What are GenAI chatbots?
GenAI chatbots are stand-alone web and app-based tools that utilize artificial intelligence to enable users to analyze spreadsheet data without the need for specialized analytics software.
What are spreadmarts?
Spreadmarts refer to generative data silos created through the analysis of spreadsheets using GenAI chatbots. They enable users to perform data analysis outside of traditional analytics platforms.
The convergence of analytics and artificial intelligence (AI) has profound implications for various industries. The ability to harness AI in analytics can transform data science ecosystems, user behavior, roles, and decision-making processes. By understanding the effects and potential risks of AI, organizations can leverage this technology to gain a competitive edge.
Traditionally, spreadsheets have been the go-to tool for data analysis. However, the emergence of web and app-based GenAI chatbots has revolutionized the way users analyze spreadsheet data. These chatbots provide intuitive and easy analysis, bridging the gap between traditional data entry and sophisticated analysis.
One key advantage of GenAI chatbots is their ability to eliminate the need for specialized analytics and business intelligence (ABI) and data science and machine learning (DSML) software. This makes data analysis more accessible to a wider audience, allowing users to analyze data within their business processes without the limitations imposed by traditional analytics software.
While the increased accessibility of GenAI chatbots offers significant benefits, it also presents governance challenges. Users may bypass good governance practices, intentionally or unintentionally, leading to potential risks. Gartner predicts that by 2025, 40% of ABI platform users will bypass governance processes by using generative AI-enabled chatbots to share analytic content created from spreadsheets. This may lead to the growth of spreadmarts, which are generative data silos.
Looking ahead, Gartner predicts that by 2026, more than 70% of independent software vendors (ISVs) will embed GenAI capabilities in their enterprise applications. This represents a significant increase from the current adoption rate of less than 1%. The convenience of AI-enabled natural language query (NLQ) without the need for an ABI platform poses a risk for traditional vendors and investments made by data and analytics (D&A) leaders.
To navigate the evolving landscape of AI-enabled analytics, D&A leaders should consider some recommendations:
1. Focus on AI training and upskilling: Develop training modules for business analysts and augmented analytics consumers to fully harness the benefits of GenAI chatbots.
2. Employ strategic planning for AI-enabled analytics: Incorporate the use of NLQ chatbots outside of ABI platforms as a technological catalyst into the organization’s strategy and operating model.
3. Ensure integration efforts promote composability: ABI platforms should integrate with large language models (LLMs) to stay relevant in a market where users prefer embedded analytics in their workflows. Buyers should assess available LLM integration options.
4. Promote collective intelligence through analytics collaboration: Encourage the sharing of analytics insights generated from GenAI chatbots to foster a culture of collaboration and shared learning. Implement adaptive governance mechanisms to address hallucinations from AI chatbots and improve interpretability.
To stay ahead in the evolving analytics technology and digital landscape, it is crucial for D&A leaders and organizations to stay updated on the latest advancements in AI-enabled NLQ and chatbot technology. Failure to do so may result in falling behind and potential violations of data and analytics governance policies.
For more information on AI best practices for analytics users, Gartner analysts will be discussing this topic at the upcoming Gartner Data & Analytics Summit in Mumbai, India, on April 24-25.
Related Links:
– Evolving the Conversation from Data Science to Augmented Analytics
– 5 Essential Elements of Machine Learning Model Detect and Patch Misconfigurations
– The Data-Driven Approach to Deciding If a Chatbot Is Right for Your Business
The source of the article is from the blog windowsvistamagazine.es