Analyst Perspectives

Data and analytics have become increasingly important to all aspects of business. The modern data and analytics stack includes many components, which creates challenges for enterprises and software providers alike. As my colleague Matt Aslett points out, a better term might be modern data and analytics smorgasbord. There are arguments for and against using an assortment of tools versus a consolidated platform. For example, purchasing, integrating and deploying a variety of tools can be complex....

Artificial intelligence seems poised to change everything, although naturally a great deal of attention tends to be paid to the cool things it makes possible. AI can also make the humdrum less tedious and even transform the dullest of back-office operations into something more meaningful. For example, AI can take accounts receivable automation to the next level. 

Interest in artificial intelligence (AI) is exploding driven in large part by the widespread interest in generative AI. ISG’s AI Buyer Behavior Survey reported that more than 6 in 10 participants have at least one AI application in production. However, despite the ease with which individuals can use AI as a result of natural language processing, creating and managing AI models is still a challenge. First, there is a shortage of skills. Second, the process itself involves many parts, each of...

Cloud computing has had an enormous impact on the analytics and data industry in recent decades, with the on-demand provisioning of computational resources providing new opportunities for enterprises to lower costs and increase efficiency. Two-thirds of participants in Ventana Research’s Data Lakes Dynamic Insightsresearch are using a cloud-based environment as the primary data platform for analytics. 

After a year of near-constant AI chatter, the broad strokes of how the technology will roll out in business over the next three to five years are coming into place. It’s almost trite but worth repeating that artificial intelligence will drive a substantial boost in productivity as it’s adopted. Rather than making large swathes of jobs obsolete, it will take the robotic work out of those job descriptions, enabling people to focus on tasks with a greater economic return. 

The first wave of discussions around artificial intelligence (AI) in the contact center was focused on providing software buyers with a general understanding of what the technology could do. Now the conversations are becoming more specific, focused and direct. Buyers are more aware of the spectrum of available use cases and appear to be exploring how to map new tools to the particular business problems they face. Contact center buyers are approaching new technology deployments (or enhancements...

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Contact centers have long collected feedback from customers, usually through short surveys. It is very common for an agent or an automated system to ask for an assessment of the interaction that just occurred, hoping to get the customer's candid, instant view of whether they were satisfied. For the most part, what's learned in those short engagements is very narrow. It can be used for a customer satisfaction snapshot, and it can be used to find out if a particular agent is running into trouble....

Today’s contact center agents find themselves handling increasingly more complex interactions due to changes in consumer demand, advances in self-service and the proliferation of digital contact channels. This added complexity requires continuous agent support for successful customer experience outcomes. Intelligent software can reduce agent workload and improve customer interactions by picking up customer cues.

MicroStrategy recently held their annual user conference, MicroStrategy World 2019. This year's conference brought 2,100 customer attendees plus partners to the Phoenix Convention Center in Phoenix, AZ. The big news of the event was the introduction of MicroStrategy HyperIntelligence™, a platform tool designed to directly inject analytics into business applications.