I was pleased to see SAS announce a new division dedicated to Internet of Things (IoT). SAS is well known for its capabilities in enterprise level analytics. Their expertise applied in real world use cases will be very useful in advancing IoT as companies struggle through how to monetize the data generated from billions of connected devices.
In the announcement, SAS cited an example as a reason for the company to create a division dedicated to IoT :
“A modern locomotive emits a stream of more than a billion data points per second as it rolls down the tracks. The data tells the operator about the health and location of the equipment and, if handled properly, can help the rail company operate more efficiently and save money. The business opportunities in that locomotive’s data – like the data streaming from billions of other connected devices around the world – are the reason analytics leader SAS has created a new global division devoted to the Internet of Things (IoT).”
Customers are trying to solve specific problem with IoT technologies e.g. gaining energy efficiency, early fault detection or remote diagnosis and maintenance of equipment. Decisions are generally driven by the need to reduce Operational Expenditure (OPEX) and save on Capital Expenditure (CAPEX). With all the data generated from connected IoT devices, having strong analytics and visualization capabilities can help in making accurate decisions and taking timely action thus achieving these critical business objectives. To harness the power of IoT and achieve reductions in OPEX and/or CAPEX, you need to effectively address data collection, analytics, visualization and control.
Below are some recommendations for you to design your IoT analytics solution :
- Identify the Problem and Set Your IoT Goals – Target how much you want to save or make – be reasonable and don’t expect overnight wonders
- Ensure Smart Data Collection – Edge Computing is being reinvented; select the right platform
- Select the Right IoT Platform – from over 300 platforms, selection is hard. Get the right internal or external talent to help with this as vendor lock-in and wrong technology selection can become a long term problem.
- Prepare for Real-time and Historical Analytics – have the right CPU or GPU infrastructure – on-premises or in the cloud
- Actionable Visualization – make it simple to pin-point problems; seeing is believing (many times). And then act upon what you see.
Every business is different and there is no set formula with data analytics in IoT. Do what’s best for you. Identify the specific problem that you want to solve and build your IoT Analytics solution around it. SAS has an opportunity to do this well and we wish them best with their new IoT division!
Chief Executive Officer, Dihuni
Dihuni offers IoT Consulting services and products and we are always anxious to learn. We would love to hear from you about your IoT Analytics project and experience. Please e-mail us at firstname.lastname@example.org or call us. This blog is part of our series related to Digital Transformation/IoT/AI etc that we publish on Dihuni.com to benefit our visitors. To contribute original articles that can help advance Digital Transformation, please contact us.