Has “augmented analytics” reached peak hype? It certainly feels that way. Countless software vendors in the data and analytics market—traditional business intelligence companies and new entrants alike—have jumped on the augmented analytics bandwagon. Many offer tantalizing new capabilities that promise to add augmented capabilities to everyday analytic workflows with AI, putting advanced analytics techniques in the hands of regular businesspeople, and automating key aspects of the decision-making process.
But do they go far enough? Are they getting organizations closer to solving the data-driven decision-making challenge or driving adoption? Or are they simply plugging holes in the data and analytics stack, bolting on features that herald transformational benefits that, in practice, stand apart from the core analytics experience?
I don’t want to bury the lead, so I’ll go ahead and say it: Pyramid’s approach to augmented analytics is markedly different (hint: integrated). But how so? What’s our “secret sauce”?
Before we dive into the differentiation, let’s be clear about what we mean by “augmented analytics” and why everyone’s talking about it.
According to Gartner, augmented analytics refers to the use of machine learning and artificial intelligence techniques to transform how analytics content is developed, consumed, and shared. This includes augmented data preparation; augmented analytics and business intelligence; and augmented data science and machine learning.
Leading analyst Ventana Research in a recent benchmark report declares, “the use of artificial intelligence (AI) and machine learning (ML), and what is promised in augmented analytics, is limited to one-quarter of organizations but growing and is planned to be addressed in an additional one-third (34%) [emphasis added].”
TechTarget has called ours the “new era of BI,” marking the end of the self-service tools that have defined BI in recent times: “After the era of self-service analytics, it’s now the era of augmented analytics.”
451 Research in a recent Market Intelligence Business Impact Brief advises organizations to “Ensure that machine learning is infused within the entire decision intelligence workflow. This will help support the automation of data management and analysis processes—not merely one or the other since they are complementary.”
Augmented analytics enables organizations to speed up their journey from data to decisions. As data volumes and complexity explode, it’s become an indispensable component of the data and analytics workstream. Machine learning—one of the foundational drivers for augmented analytics—has the potential to reveal tremendous opportunities and perspectives that traditional BI tools fail to uncover.
The appetite for augmented capabilities has clearly grown. However, many traditional analytics and BI vendors have simply retrofitted existing capabilities or acquired technologies to account for customer demands. Far worse, they do all of this and in many cases require additional licenses or limit the application of ML to subsets of data. This has only served to further fragment and complicate the analytics user experience.
The path to a truly data-driven decision doesn’t always start with the data. In fact, it often begins with a question, a problem a businessperson is trying to solve. “What promotions can we offer to maximize or increase sales in North America?” Or “What would happen to overall sales if we reduced our advertising budget in Portugal?”
In a typical decision cycle, you must prepare the data for analysis; you discover and analyze the data; and, in some cases, you build data science models, experiment, test, and then analyze the data and share it with your peers or partners. If you have to do this end to end—manually or with multiple tools—it’s a herculean and time-consuming task. What’s more, moving data through multiple tools impacts governance and security.
Imagine if you could do all these seamlessly through a single platform. And imagine then if this journey is augmented and guided by AI? That is decision intelligence: it enables you to make intelligent decisions faster with augmentation from AI.
It’s precisely this end-to-end aspect that separates Pyramid’s Decision Intelligence Platform from other vendors. Augmented analytics is an inherent and fundamental aspect of the entire analytics experience. Ultimately, it enables technical and non-technical people to use data and quickly make decisions.
In this integrated environment, everyone can experience the transformational benefits of AI because it’s personalized for their needs.
While many vendors offer individual tools today, multiple tools can add unnecessary complexity. Pyramid provides a platform with a unified experience—driven by AI—that brings these capabilities together in one solution, streamlining the entire workflow, personalizing the analytics experience, and encouraging adoption across the organization.
So how does the Decision Intelligence Platform incorporate augmented capabilities? Let’s look at some examples.
Augmented capabilities in the data preparation and discovery phases enable non-technical people to automatically ingest data from data sources, discover tables, columns, column types, and metrics, and define their relationships using AI algorithms. They can also automatically add time dimensions and hierarchies with a few clicks in a no-code environment.
The platform automatically runs heuristics on data to show relevant correlations to uncover patterns across different data that can be used for analysis. The heuristics engine can also make relevant visualizations in reports and dashboards based on the type, size, and density of data that has been analyzed. Analytical operations and calculations based on time hierarchy (such as year-to-date, quarter-to-date, etc.) are automatically generated, and corresponding visualizations are displayed based on the data element.
This AI-guided, no-code environment also delivers automated insights around the data, such as forecasting, Pareto analysis, category analysis, composite views of multiple analyses, and many other complex calculations. It also provides the flexibility to build their discovery and analysis along with various calculations, visualizations, etc.
Natural language queries (NLQ) enable non-technical people to ask questions in simple words and get answers. For example, they can ask for sales forecasting for the next six months. The system will automatically calculate sales across relevant dimensions such as time or region and forecast the numbers with the appropriate visualization. NLQ also enables non-technical people to add, replace, remove, and swap data elements using simple words. They can conduct complex decision tree segment analysis in a few clicks, delivering answers in seconds. Dynamic tooltips can also be made available using augmented analytics.
All these capabilities and more are fully integrated into the platform, don’t require separate tools or modules, and don’t require additional SKUs or licenses. And—perhaps even more crucially—Pyramid applies these augmented capabilities against the full range of data (across all sources), not merely extracts, all with blazingly fast performance.
OK, so Pyramid is different. What’s the upshot? Augmented analytics makes analytics easier for everyone to use and drives higher adoption within your organization. That, in turn, drives faster, more intelligent decisions. This stands in stark contrast to traditional business intelligence tools. No other platform makes analytics more accessible—without sacrificing enterprise needs.
But don’t just take our word for it. In 2022, Pyramid Analytics became the category leader for augmented analytics in Gartner’s annual 2022 Critical Capabilities report. Others, like Ventana Research and 451 Research, applaud Pyramid’s approach to augmented analytics and the ways we are integrating machine learning for automation to foster ease of use and drive speed to decision.
Contact one of our augmented analytics experts today to discover why global data and analytics leaders rely on Pyramid Analytics for their most comprehensive analytics initiatives.