Recently Hortonworks announced some significant additions to its products at the DataWorks Summit. These additions reflect the fact that the big data market continues to evolve, as I have previously written.
This is my second analyst perspective based on our IoT Benchmark Research. In the first, I discussed the business focus of IoT applications and some of the challenges organizations are facing. Now I’ll share some of the findings about technologies used in IoT applications and the impact those technologies appear to have on the success of users’ projects.
If we look at the focus of technology vendors for analytics and business intelligence or business applications providers deploying these capabilities in the last five years, we see that they have elevated the importance on the value of visualization and dashboards. These promotions might be understandable, but will they make business and the people using them more intelligent?
Topics: Big Data, data science, Mobile, Machine Learning, Analytics, Business Intelligence, Cloud Computing, Collaboration, Information Optimization, digital technology, Machine Learning and Cognitive Computing
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.