David Menninger's Analyst Perspectives

We are happy to share some insights about Domo drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.

Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning

We are happy to share some insights about Qlik drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.

Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI & Machine Learning

We are happy to share some insights about Information Builders’ WebFOCUS Business Intelligence and Analytics Platform drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.

Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, AI and Machine Learning

We are happy to share some insights about MicroStrategy drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.

Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning

Machine learning is valuable for organizations, but it can be hard to deploy. Our Machine Learning Dynamic Insights research identifies that not having enough skilled resources and difficulty building and maintaining ML systems are pressing challenges organizations face in applying ML. Traditional ML model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. And as the number of ML models grow, their management becomes difficult. By bringing automation to ML, organizations can reduce the time it takes to create production-ready ML models. AutoML can also enable organizations to make data science initiatives more accessible across the organization.

Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, AI and Machine Learning

The amount of data flowing into organizations is growing exponentially, creating a need to process more data more quickly than ever before. Our Data Preparation Benchmark Research shows that accessing and preparing data continues to be the most time-consuming part of making data available for analysis. This can potentially slow down the organizational functions which depend on the analysis results. Trying to get ahead of the backlog with incremental improvements to existing approaches and traditional technologies alone can be frustrating.

Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, Information Management (IM), data lakes

Process-mining software isn’t exactly new, but it’s also not widely known in the software technology market. The discipline has been around for at least a decade, but is generating more interest these days with both specialist vendors and major enterprise software vendors offering process-mining products and services. We assert that through 2022, 1 in 4 organizations will look to streamline their operations by exploring process mining.

Topics: business intelligence, Analytics, Digital Technology, AI and Machine Learning, robotic automation

Organizations are accelerating their digital transformation and looking for innovative ways to engage with customers in this new digital era of data management. The goal is to understand how to manage the growing volume of data in real time, across all sources and platforms, and use it to inform, streamline and transform internal operations. Over the years, the adoption of cloud computing has gained momentum with more and more organizations trying to make use of applications, data, analytics and self-service business intelligence (BI) tools running on top of cloud-computing infrastructure in order to improve efficiency. However, cloud adoption means living with a mix of on-premises and multiple cloud-based systems in a hybrid computing environment. The challenge is to ensure that processes, applications and data can still be integrated across cloud and on-premises systems. Our research shows that organizations still have a significant requirement for on-premises data management but also have a growing requirement for cloud-based capabilities.

Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things, Data, natural language processing, data lakes, AI & Machine Learning

Every organization performing analytics with multiple employees needs to collaborate. They should be collaborating in the analytics process and in communicating the results of those analyses. As I continue my evaluation of analytics and data vendors, I have to admit some disappointment at the level of collaborative capabilities some analytics vendors provide. To be fair, the level of capabilities vary widely, but I expected collaborative capabilities to be more uniformly available as a standard feature in analytics technologies by now. I had anticipated that three-quarters of analytics vendors would include collaboration capabilities. More than half the vendors I have evaluated support some comments and discussion in their products, only a few have incorporated social recognition and wall posting as part of their collaborative capabilities. So, what impact does a lack of analytics collaboration have on organizations undergoing digital transformation?

Topics: business intelligence, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, collaborative computing

Ventana Research has been evaluating analytics and business intelligence (BI) software for a long time—almost 20 years. Our methodology for these assessments is referred to as a Value Index. We use weightings derived from our benchmark research about how you, as buyers of these technologies, value and evaluate vendors. You can view our 2019 Value Index results here. I am in the process of completing the 2020 evaluation now.

Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management (IM), natural language processing, Conversational Computing, AI and Machine Learning, collaborative computing, software evaluation