David Menninger's Analyst Perspectives

All too often, software vendors view analytics as the end rather than the beginning of a process. I’m reminded of some of the advanced math classes I’ve taken in which the teaching process focused on a few key aspects of a mathematical proof or solution, leaving the rest of the exercise to be worked out by the students. In other contexts, you may hear people say the numbers speak for themselves.

Topics: Analytics, Business Intelligence, Collaboration, data science, Data Governance, Information Optimization, Machine Learning, digital technology, collaboration for business

We at Ventana Research recently published our research agendas for 2018. Analytics and business intelligence are evolving and so is our research on their use across practice areas. Earlier research has shown that analytics can deliver significant value to organizations; for example, our predictive analytics research shows that 57 percent of organizations reported achieving a competitive advantage and half created new revenue opportunities with predictive analytics. Waves of investment in self-service analytics have propelled the market for analytics tools, significantly empowering line-of-business organizations to create their own analytics and set their own analytic priorities. But organizations are also beginning to recognize some of the limitations of current analytics implementations – for self-service, for example. Our Data Preparation Benchmark Research reveals that fewer than half (42%) of organizations are comfortable allowing business users to work with data not prepared by IT. Our research this year will continue to explore both the successes and challenges organizations face as they continue to use analytics and BI.

Topics: Analytics, Machine Learning, Artificial intelligence, Business Intelligence, Internet of Things, IOT, Collaboration, natural language processing, Natural Language Query

Many organizations continue to struggle with preparing data for use in operational and analytical processes. We see these issues reported in our Data and Analytics in the Cloud benchmark research, where 55 percent of organizations identify data preparation as the most time-consuming task in their analytical processes.  Similarly, in our Next-Generation Predictive Analytics research, 62 percent of companies report that they’re unsatisfied because data needed for access or integration is not readily available. In our Big Data Integration research, 52 percent report spending that in working with big data integration processes, they spend the most time reviewing data for quality and consistency.  And nearly half of companies (48%) report this same issue in our Internet of Things research. We are currently conducting further research into this critical issue with our Data Preparation benchmark research.

Topics: Data Preparation, Analytics, Datawatch, Collaboration, Big Data

Some 3,000 people attended Domo’s recent customer event, called Domopalooza. That’s nearly double the attendance of the previous event, which my colleague Mark Smith covered. Formerly a bit “stealthy,” Domo has started sharing more information, some of which I’ll pass along, as well as observations about product announcements made at the event.

Topics: Business Intelligence, Collaboration, data science, Mobile, Machine Learning Digital Technology, Machine Learning and Cognitive Computing, Mobile Technology, Analytics, Cloud Computing, Big Data

I recently attended SAS Institute’s analyst relations conference. There the company provided updates on its financial performance and its Viya platform and a glimpse into some of its future plans.

Topics: business intelligence, data science, Internet of Things, Data Integration, Data Governance, Data Preparation, Information Optimization, Machine Learning Digital Technology, Big Data, Machine Learning and Cognitive Computing, Mobile Technology, Analytics, Cloud Computing, Collaboration

Big data initially was characterized in terms of “the three V’s,” volume, velocity and variety. Nearly five years ago I wrote about the three V’s as a way to explain why new and different technologies were needed to deal with big data. Since then the industry has tackled many of the technical challenges associated with the three V’s. In 2017 I propose that we focus instead on a different letter, which includes these A’s: analytics, awareness, anticipation and action. I’ll explain why each is important at this stage of big data evolution.

Topics: Business Intelligence, Collaboration, data science, Internet of Things, Data Preparation, Information Optimization, Machine Learning, Analytics

Ventana Research analysts recently published our research agendas for 2017. As we put together these plans we think about the forces that are shaping the markets that we cover and then craft agendas that study these issues to provide insights for our community. I’ve been working in the business intelligence (BI) and analytics market for nearly 25 years, and throughout that time the industry has been trying to make analytics useful to increasingly wider audiences. That focus continues to today. Better search and presentation methods, including visual discovery and natural-language processing, are promising ways to engage more users. We also see organizations supporting their users in specific functional roles with relevant and accessible analytics. My colleagues examine these issues as part of their agendas in the Office of Finance, Sales, Marketing, Customer Experience, Operations and Supply Chain, and Human Capital Management. While their agendas include analytics within specific domains, my own research focuses on a range of analytics issues across domains including cloud computing, mobility, collaboration, data science and the Internet of Things.

Topics: Business Intelligence, Collaboration, data science, Internet of Things, Big Data, Machine Learning and Cognitive Computing, Mobile Technology, Analytics, Cloud Computing

The business intelligence market is bounded on one side by big data and on the other side by data preparation. That is, to maximize their performance in using information, organizations have to collect and analyze ever increasing volumes of data while the tools available are constantly evolving in the big data ecosystem that I have written about. In our benchmark research on big data analytics, half (51%) of organizations said they want to access big data using their existing BI tools. At the same time, as I have noted, end users are demanding self-service access to data preparation capabilities to facilitate their analyses.

Topics: Business Intelligence, Collaboration, Internet of Things, Big Data, Data Integration, Data Preparation, Information Management, Analytics

Topics: Business Analytics, Business Intelligence, Business Performance, Customer & Contact Center, Financial Performance, Information Management, Mobile Business Intelligence, Operational Intelligence, Predictive Analytics, Sales Performance, Social Media, Supply Chain Performance, Workforce Performance, Collaboration

Topics: Business Analytics, Business Intelligence, Business Performance, Customer & Contact Center, Financial Performance, Sales Performance, Social Media, Supply Chain Performance, Workforce Performance, Yellowfin, Mobile, Collaboration