The End of Dashboard Frustration: AI Powers New Era of Analytics

By PeggySue Werthessen, MicroStrategy

Data-driven decision-making plays a huge role enabling enterprises to achieve their business goals, but despite the widespread adoption of analytics tools, the vast majority of organizations have yet to provide all employees with relevant, timely, and actionable data. Thankfully, organizations can address these obstacles by combining generative AI and business intelligence (BI) capabilities to make analytics more accessible and actionable across organizations.

Enterprises typically face three major hurdles when implementing analytics solutions. First, workflow friction occurs when users must interrupt their regular tasks to access analytics tools. Flipping between windows involves "task switching," a psychological term that describes how the brain behaves when moving from one task to another. A Harvard Business Review study found that task switching costs workers about 4 hours of productivity hours a week (about 10% of their weekly work) as they constantly reorient themselves toggling between applications. That's a significant loss.

Additionally, varying levels of data literacy among employees can limit effective data utilization. Traditional data analytics platforms typically communicate data and insights through dashboards. These are not necessarily intuitive interfaces for most workers, and most employees are not qualified to build dashboards on their own. What's more, dashboard data is often presented without a larger context, so it may be difficult to translate insights into action.

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Finally, concerns about data accuracy and reliability can lead to hesitation in acting on analytics insights. Employees can receive conflicting insights, especially when an organization has disparate data systems. And given the massive amount of data most enterprises have on hand, ensuring the accuracy of that data is a tall order. If people don't trust the data and insights they're given, they may as well not have them at all.

Seamless Integration Through GenAI

Enterprises can tackle the workflow friction challenge by embedding analytics directly into users' existing applications. Most applications these days are delivered on a SaaS basis, which means a web browser is the primary interface for employees' daily workflow. With the assistance of a browser plug-in, keywords can be highlighted to show critical information about any business entity, from customer profiles to product details, making data instantly accessible within the user's natural workflow. There's no need to open another application and lose time on task switching — the data is automatically presented within the natural course of an employee's operations.

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To address varying levels of data expertise, enterprises can take a hybrid approach that combines the natural language capabilities of large language models (LLMs) with the precision of traditional BI tools. In this way, an AI-powered BI assistant can translate natural language queries into precise data analytics operations. Employees will no longer need to know how to form specific, technical queries to get the data they need. Instead, they can simply ask a bot using ordinary text, just as if they were interacting with a human being. The bot forms the query, and the BI platform provides accurate, reliable results, which GenAI provides to the employee in a format that's easy to understand.

The solution addresses the limitations of standalone LLMs, which excel at language processing but often struggle with accurate numerical calculations and, in the absence of trustworthy data, can produce hallucinations. GenAI on its own is not an effective business solution, but by combining AI's interpretative abilities with BI's computational precision, users can interact with data more naturally while maintaining accuracy.

Finally, building employee trust in the data they get from the GenAI bot requires transparency in its AI operations. Users should be able to see how their queries are interpreted and processed. This visibility will help build trust in the system's outputs and enable users to verify the accuracy of results. Additionally, the system must maintain strict data security by minimizing data sharing with AI systems and implementing a no-memory policy for AI interactions.

By following three fundamental principles — pervasiveness, trust, and openness — enterprises can ensure analytics are widely accessible while maintaining data security and governance. And once this hybrid AI + BI solution has been implemented and earned employees' trust, the GenAI bot will continue to get smarter, training on the organization's industry-specific terminology and company acronyms, making interactions even more natural and efficient. This contextual awareness extends to security protocols, ensuring users only access information appropriate for their role.

As organizations continue to seek ways to make data analytics more accessible and actionable, the integration of AI with BI tools represents a significant step forward. Combining these technologies can address long-standing barriers to analytics adoption while maintaining data integrity and security, ushering in a future where data analytics becomes truly pervasive within organizations, and enabling employees at all levels to make data-driven decisions without requiring extensive technical expertise. As AI and BI technologies evolve, this integration may become the standard approach for organizations seeking to maximize the value of their data investments.

About the author:

PeggySue Werthessen is VP, Go-to-Market Strategy at MicroStrategy. Prior to joining MicroStrategy, she held a variety of go-to-market and business analysis positions at companies such as Fidelity Investments, ZoomInfo, and Teradata.