For “Smart Analytics,” Don’t Put the AI Cart Before the IA Horse

When it Comes to AI and ML Initiatives, Build Capabilities Progressively—and Don’t Leave Your Business Users Behind - By Omri Kohl, Co-founder and CEO at Pyramid Analytics

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Data lies at the heart of every decision in contemporary business practice—no matter who in the organization is making that decision. But today’s employees need analytics to decide correctly, driven by modern AI capabilities.

By the end of 2020, companies that were enhancing their digital infrastructure to bolster remote operations “began to see AI’s potential for producing large-scale data analytics, insights, and automation,” S&P Global found in its 2021 survey of enterprise companies. Now that the subject of artificial intelligence (AI) and machine learning (ML) has become a mainstream topic—from the boardroom to the living room—business leaders feel a natural urgency and competitive instinct to extract value from these technologies as soon as possible.

Unfortunately, the “big shiny object” of AI and ML is a bit of an abstraction. Everyone wants it, but they don’t necessarily know what it means for them or how it will help their companies in practice. As S&P Global describes, “A majority of companies are looking to tap AI for increasingly specific future projects, though AI remains in an experimental phase for many.”

The fundamental problems are that (a) most analytical tools are not matched appropriately to business users’ needs, and (b) companies lack the information architecture (IA) required to make successful AI-driven analytics available for all business users.

Fortunately, solutions to both of these problems are possible with “Smart Analytics”—technologies designed to augment traditional analytics tasks to accommodate individual users, with the help of AI. Here, we explore how modern IA and Smart Analytics can give data leaders and practitioners alike powerful tools, and we uncover how they can answer business questions and gain practical business insights from these tools.

Understanding “Smart Analytics” in the Context of Your Business

Smart Analytics, also defined as “Augmented Analytics” by Gartner and others, represents a combination of strategy and technologies. It gradually exposes advanced analytics to regular business users through the course of their everyday work. Introducing Smart Analytics into their existing workflows in an approachable way “trains” them to use AI-driven analytics every day, even if they don’t have a data science background.

In this way, advanced analytics—long considered a highly technical practice—is becoming a part of everyday business decision-making at all levels of the organization. And as S&P Global found, “empowering and aligning internal decision-makers” is among enterprise leaders’ most significant benefits anticipated or already realized from the use of ML technologies.

The Key to Smart Analytics Success Is Starting With IA

Business leaders’ end game must be to implement advanced analytics with the success of each individual employee in mind. They should make analytics easier for everyone, without sacrificing the sophistication of the results. When done successfully, each user can engage in insights-based communication, collaboration, and decision-making within a shared environment—even if their individual experiences with analytics are different and designed for their individual needs.

Barriers to Smart Analytics Adoption

There are very real hurdles to the adoption of advanced analytics technologies, especially at organizations with sizable data investments and complex business requirements. What leaders often overlook is the foundational IA required to scale analytics capabilities successfully, outside the walls of their technical data practices.

Indeed, preventing silos and overcoming barriers to employee adoption require a foundational IA technology that strengthens users’ trust in underlying data. But many organizations lack the data governance controls for AI and ML projects to make it past the experimental stage or the tools to give everyday users the ability to collaborate with more advanced users.

Governance That Works But Also Complements Data Democratization

Creating a trustworthy data foundation of this kind brings us to the continued role of data stewards. In this modern analytics practice, data stewards must simultaneously encourage the democratization of analytics capabilities and manage the underlying risks associated with expanding data access.

That’s why Smart Analytics initiatives must be anchored to holistic data and analytics applications that are designed to protect the business. Otherwise, newfangled “smart” tools only exacerbate barriers to universal adoption, creating “abilities silos” whereby only a limited number of employees benefit. Those reinforce mistrust in data, especially among those who can’t access or understand a shared analytics environment.

Strategies for Business and User Success

None of these complexities should stop business leaders from this pursuit. As Harvard Business Review describes, “Companies that want to compete in the age of data need to do three things: share data tools, spread data skills, and spread data responsibility.”

The key is to attack the challenge from two angles:

  • First: Prioritize data governance and consider analytics platforms that can integrate data from across the enterprise. Many analytics platforms claim to do this, but few can actually incorporate IA to do this effectively. IA is “a pervasive structural design for data to flow through [which] prevents silos and makes enterprises more agile than ever,” as Forbes describes.
  • Second: With the support of AI, consider gradually integrating advanced analytics into a variety of traditional employee workflows. “Enterprises should consider how the data will be used, reused and shared,” Forbes continues. “The foundation should enable a common set of core data—such as customer, revenue, products—that’s shared throughout the enterprise, which prevents the problem of silos that arise to serve a particular function.”

With these two approaches, organizations can incorporate Smart Analytics capabilities in a way that cooperates with (rather than interrupts) traditional processes and still unifies the organization in a single analytics environment. Simply put, business leaders can bring decision-makers at all levels of the organization “closer” to their analytics.

What’s in It for Users?

Your employees empower your business, so make sure your analytics investments empower them. You’ll know you’re successful when your analytics tools unequivocally answer the question all your data-hungry business users bring to the table: "What's in it for me?"

Give business users the tools and the data they need, and analytics will become a seamless part of employees’ daily lives. Smart analytics can breed confidence in the user base, allowing ingenuity to flourish—safely and securely—as a culture of data-driven problem solving emerges.

In all respects, there’s really no better foundation for long-term business success.

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