Representative End User Clients
Representative Automation Clients
Representative Software Clients
Two pieces of research, one already published and one up next....
ARC's analytics and business intelligence buyers guide published recently. The intent is to give buyers a significant head start as they begin the selection and procurement of describe and discover solutions. The guide includes likely selection criteria from all areas of the procurement decision:
Almost every BI or analytics implementation is unique in some way - the exact functionality that the users need, odd data sources that need to be integrated, or dashboards that need to be embedded in business processes, for example. It's realistic to say that the ARC buyers guide is just a starting point - most buyers will also need to add a number of selection criteria that are unique to their needs if they are going to run a solid selection process.
Moving on, my next major project at ARC moves the focus squarely back onto the Industrial Internet of Things (IIoT). Machine learning is a concept that's been around for decades, but with the exception of a few niche use cases, has largely been collecting dust on the shelf. Big data - and by extension the Industrial Internet of Things - will change that. The masses of device level data that will be generated will breathe new life into machine learning technologies and approaches. This research project will focus on established and emerging machine learning vendors, covering:
If you think you're a machine learning vendor that has something particularly unique for asset intensive industries, drop me a note and we can discuss.
ARC's analytics and business intelligence buyers guide published recently. The intent is to give buyers a significant head start as they begin the selection and procurement of describe and discover solutions. The guide includes likely selection criteria from all areas of the procurement decision:
- Vendor maturity and stability
- Platform requirements
- Technical support
- Installation and deployment options and requirements
- Implementation and end user functionality
- Total cost of ownership
Almost every BI or analytics implementation is unique in some way - the exact functionality that the users need, odd data sources that need to be integrated, or dashboards that need to be embedded in business processes, for example. It's realistic to say that the ARC buyers guide is just a starting point - most buyers will also need to add a number of selection criteria that are unique to their needs if they are going to run a solid selection process.
Moving on, my next major project at ARC moves the focus squarely back onto the Industrial Internet of Things (IIoT). Machine learning is a concept that's been around for decades, but with the exception of a few niche use cases, has largely been collecting dust on the shelf. Big data - and by extension the Industrial Internet of Things - will change that. The masses of device level data that will be generated will breathe new life into machine learning technologies and approaches. This research project will focus on established and emerging machine learning vendors, covering:
- What machine learning is
- How machine learning applies to asset intensive industries
- Different machine learning approaches
- A review of the interesting vendors in this space.
If you think you're a machine learning vendor that has something particularly unique for asset intensive industries, drop me a note and we can discuss.