David Menninger's Analyst Perspectives

Alteryx was founded in 1997 and initially focused on analyzing demographic and geographically organized data. In 2006, the company released its eponymous product that established its direction for what the product is today. In 2017, it went public in an IPO on the NYSE. At the time of the IPO, Alteryx was focusing much of its marketing efforts on the data preparation market, particularly to support Tableau. Throughout this time though, Alteryx offered much more than data preparation. As a...

I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research 2023 Augmented Analytics Buyers Guide is the distillation of a year of market and product research by Ventana Research. Drawing on our Benchmark Research, we apply a structured methodology built on evaluation categories that reflect the real-world criteria incorporated in a request for proposal to Analytics and Data vendors supporting...

The 2023 Ventana Research Buyers Guide for Augmented Analytics research enables me to provide observations about how the market has advanced. 

Organizations are continuously combining data from diverse and siloed sources for analytical, artificial intelligence and machine learning projects. As the volume of data grows, it becomes challenging for organizations to manage and keep current to extract valuable insights in a timely manner.

The current market landscape of data and analytics is undergoing rapid evolution, presenting organizations with a wide array of challenges and opportunities. As data sources and warehouses steadily migrate to the cloud, a significant number of organizations still depend on conventional tools. This reliance on legacy systems hinders the seamless accessibility and adoption of analytics and business intelligence within business processes. Organizations are increasingly turning to embedded...

Artificial intelligence (AI) has evolved from a highly specialized niche technology to a worldwide phenomenon. Nearly 9 in 10 organizations use or plan to adopt AI technology. Several factors have contributed to this evolution. First, the amount of data they can collect and store has increased dramatically while the cost of analyzing these large amounts of data has decreased dramatically. Data-driven organizations need to process data in real time which requires AI. In addition, analytics...

Generative AI is a class of artificial intelligence used to generate new, seemingly real content. Broadly speaking, AI has traditionally been used to identify patterns in data and apply those patterns to categorize and predict behaviors. For instance, it can organize customers into groups (or clusters) with similar characteristics, or predict which customers are most likely to respond to certain offers.

Organizations are continuously searching for new business opportunities hidden in their data. They are using various technologies including artificial intelligence and machine learning (AI/ML) to uncover granular insights that can support decision-making. Existing tools and dashboards are effective for observing standard metrics; however, they do not address follow-up questions, such as why things are happening or how those events impact performance. Organizations also struggle to derive...

I’ve previously written about the analytics continuum, which spans a range of capabilities including reporting, visualization, planning, real-time processes, natural language processing, artificial intelligence and machine learning. I’ve also written about the analysis that goes into making intelligent decisions with decision intelligence. In this perspective, I’d like to focus on one end of the analytics continuum, which I’ll label advanced analytics.

Markets have been more volatile than ever. It creates a need for decision makers to utilize technologies such as artificial intelligence and machine learning (AI/ML) to better understand the external factors that impact their business. By identifying these factors, organizations can better plan for changing market environments and seize market opportunities. However, manual modeling is a time-consuming process and results in a limited number of models and tests. Also, updating those models is...