Is AI Just Making Decisions Harder?
How Addressing Fundamental Analytics Challenges Can Help Data Science Take Root
Gartner’s 2021 Reengineering the Decision Survey found 47% of respondents expect [business] decisions to become more complex in the next 18 months.
Business intelligence has historically represented the “traditional” route to operationalized decision-making. Now, business leaders are looking to AI-driven analytics and data science to help them navigate this new decision environment.
Yet the fundamental challenges of data silos and an endless array of analytics tools persist, often exacerbated, when data leaders attempt to incorporate data science and AI tools into traditional analytics workflows across the enterprise.
Often, the new tools do little to solve age-old problems and are too difficult to operate for the average business user. Analytics environments are not meaningfully aligned to business needs—rather, “decision” needs.
According to Gartner, “As businesses become more complex, traditional decision-making practices will become increasingly ineffective. Data and analytics leaders must leverage decision intelligence models to facilitate highly accurate and contextualized decisions.”
- What is Decision Intelligence and how it differs from business intelligence
- How platforms that align to “decision” needs can address the persistent flaws of self-service analytics to help meet the challenges data and business leaders face
- How applying the “right” level of AI and data science—designed with business users in mind and integrated in everyday workflows—can help users confidently address growing complexity.
- Examples of a business user leveraging smart analytics in their day-to-day work?