More than a year ago I wrote from personal experience about the challenges our firm encountered with Salesforce’s cloud computing systems and customer service and if we can trust them for business in the cloud. That perspective covered a range of issues that the behemoth cloud computing applications and platform company is facing regarding its service and technology. While Salesforce has shifted its customers like us and others to a different cloud computing environment, as it did in moving us off its #NA14 cloud computing instance, core challenges of its customer experience and billing processes persist. After the last customer experience incident, I contacted its executives about the need for operational improvement; it was clear in the dialogue that they saw some but not all of our experience as issues important to improving its customer processes. It was good to get some immediate attention, but my hope was to speak for all SMB companies in pointing out the importance of effective communications and escalating notifications and interactions when those customer moments go sour, so the customer isn’t forced to turn to social media to be heard. This was an area where Salesforce had significant room for improvement in customer engagement, reflecting a pattern we see in our benchmark research, which finds the most common challenges in almost half of organizations are integration of channels of engagement and managing activities as silos.
Topics: Big Data, Sales, Office of Finance, Analytics, Cloud Computing, Collaboration, Product Information Management, Sales Performance Management, Digital Commerce, Sales and Operations Planning, Machine Learning and Cognitive Computing, Sales Enablement and Execution, Machine Learning Digital Technology, Sales Planning and Analytics
This year various types of organizations are embracing machine learning like it is going out of style – or maybe it would be better to say coming into style. And now with a little investigation on LinkedIn finds over half million professionals with machine learning in their job title. Machine learning is the application of specific data science algorithms that become more accurate as the system records more outcomes and processes more data. This improvement is referred to as “learning,” hence the name. There are good reasons machine learning is growing so rapidly, but there are pitfalls to avoid as well.
Topics: Big Data, Data Science, Analytics, Business Intelligence, Cloud Computing, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
Informatica Asserts Its Commitment to the Cloud and Machine Learning
Informatica reintroduced itself to the world at its recent customer conference, Informatica World, in San Francisco. The company took advantage of the event to showcase its new branding in an effort to change the way customers think about the company. Informatica has been providing information services in the cloud for more than a decade. Even though cloud revenue comprises a minority of Informatica’s business, in absolute terms, the revenue is significant, and company executives want the public to recognize Informatica as a leader in cloud-based data management services for enterprises. Presenters also made notable product announcements, discussed below, including the application of machine learning to the data management process.
Topics: Big Data, Data Science, Analytics, Business Intelligence, Cloud Computing, Data Governance, Data Integration, Data Preparation, Information Optimization, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
Supercharge Sales Analytics with Digital Technologies
Our firm regularly explores the impacts of new technologies on business. Analytics is foremost among recently emerging technologies, which our benchmark research consistently confirms. In our research on next-generation sales analytics, fourth-fifths (82%) of participating organizations cited analytics as the most important technology trend for sales; however, several other technologies also are adding power and flexibility to the use of sales analytics.
Topics: Big Data, Sales, Mobile Technology, Office of Finance, Analytics, Cloud Computing, Collaboration, Product Information Management, Sales Performance Management, Digital Commerce, Machine Learning and Cognitive Computing, Sales Enablement and Execution, Machine Learning Digital Technology, Sales Planning and Analytics
New Generation of Sales Analytics Can Optimize Revenue Potential
I have been following advances in sales analytics since the 1990s. Over the last five years, however, I have seen evolution, not innovation. In most cases the information that analytics provides is too complicated and not contextualized enough for sales people who are not analytics experts to understand, let alone take action on. As I pointed out in my 2017 research agenda on sales, analytics is essential for planning that improves the impacts of sales efforts and meets the goals of the organization.
Topics: Big Data, Sales, Mobile Technology, Office of Finance, Analytics, Cloud Computing, Collaboration, Product Information Management, Sales Performance Management, Digital Commerce, Sales and Operations Planning, Machine Learning and Cognitive Computing, Sales Enablement and Execution, Machine Learning Digital Technology, Sales Planning and Analytics
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: Data Science, Mobile, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
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: Big Data, Data Science, Mobile Technology, business intelligence, Analytics, Cloud Computing, Collaboration, Data Governance, Data Integration, Data Preparation, Internet of Things, Information Optimization, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
Research Agenda Will Track Big Data and Information Management in 2017
Big data has become an integral part of information management. Nearly all organizations have some need to access big data sources and produce actionable information for decision-makers. Recognizing this connection, we merged these two topics when we put together our recently published research agendas for 2017. As we plan our research, we focus on current technologies and how they can be used to improve an organization’s performance. We then share those results with our readers.
Topics: Big Data, Data Science, Analytics, Data Governance, Data Integration, Data Preparation, Information Management, Internet of Things, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
Research Agenda Will Guide Business Analytics in 2017
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: Big Data, Data Science, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Internet of Things, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
