Seminar D – Pyramid Analytics – Addressing Fundamental Analytics Challenges Using the Power of Platform: How Emerging Technologies Enable “Decision Intelligence”

Ian Macdonald, Principal Technologist, Pyramid Analytics

Business decisions are hard. And in a more complex environment, the way we make them has only become harder. In fact, according to a Gartner 2021 Reengineering the Decision Survey, 47% of respondents expect decisions to become more complex in the next 18 months. Business intelligence, the standard mechanism for decision-makers across the continent, 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 and the evolving Business intelligence tools are outpaced only by the technology that underpins them. Machine learning, artificial intelligence, and their countless applications seem to hold the keys to the future, but establishing systems and processes around these innovations is difficult to do, especially at the enterprise level. Yet the fundamental challenges of data silos and an endless array self-service analytics tools persist and are often exacerbated when data leaders attempt to work in data science and AI tools into traditional analytics workflows across the enterprise. Decisions become even harder. So why do these ambitious initiatives often fail to deliver on their promises?

In many cases, the new tools do little to solve age-old problems. What’s more, they are often too difficult to operate for the average business user. Analytics environments are not meaningfully aligned to business needs or more practically, “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.”

Increasingly, the preferred approach is the consolidation of key data-related competencies—namely, business analytics, data prep, and data science. Unifying these core analytics pillars in a single platform can bring organisations closer to seeing beyond business intelligence to the more actionable, insightful, and future-focused Decision Intelligence. But why does platform matter? And isn’t a best-in-breed approach preferred?

In this session, we’ll examine the challenges data and business leaders face, and how platforms that align to “decision” needs can address the persistent flaws of self-service analytics. We’ll also examine how applying the “right” level of AI and data science designed with business users in mind and integrated in everyday workflows can help them confidently address growing complexity.

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