In short, Internet of Things devices are allowing anyone to create new attack vectors with zero accountability for companies to make the devices secure. Houston, we have a problem!
New IoT devices are showing a huge weakness in IT departments’ networks and software. This is scaring the heck out of corporate information technology departments and new security practices take time, money and qualified people to manage.
Big companies are stepping up. This week at IBM Corp.’s World of Watson show, the company is putting its weight behind big data to drive into the IoT marketplace. So are lots of competitors such as Amazon Web Services, Microsoft Azure, Oracle Corp. and a slew of new startups.
In hopes of making more sense of the IoT landscape, Wikibon Research published a recent reporting breaking down the key segments and how to deal with IoT data.
Segments of the IoT market
Personal IoT
Smart phones, smart cars, entertainment, location, travel, wearables, etc.
Home IoT
Smart meters, security and access control, smart appliances, health & safety, etc.
Government IoT
Federal, state & local government: criminal justice, internal security, traffic management, emergency response, etc.
Health IoT
Smart equipment, home health, electronic medical records, provider and payor applications, etc.
Small Business IoT
Asset management, smart retail, customer management, inventory, security, health & safety, etc.
Office IoT
Building management: security and access control, equipment management, utilities management, maintenance, etc.
Industrial IoT
Plant operations, equipment maintenance (predictive and preventive), vehicle and fleet management (aircraft, trucks, shipping, trains, etc.), warehouse operations and inventory management, site health & safety, security and access control, etc.
Action items
It will be very difficult to justify Big Data projects if all the data is extracted and sent to clouds. The cost and elapsed time for data transmission is likely to be prohibitive, and the results delayed and reduced in value. The optimum strategy is to use distributed data analytics in data center, and extract small amounts of data for further analysis in the cloud. Individual projects can also turn on specific filters to save additional data at the edge for processing and transmission of results.
Digital business goals require greater cooperation and coordination of modern IT and operations technologies and organizations. To extract the maximum value from edge data, it will need to be streamed, reduced and processed locally. Edge projects should focus primarily on giving value to the edge business processes. Extracts of data can be processed and delivered to upstream parties who pay for it, subject to careful review. In short, big data projects should be designed with distributed edge analytics combined with hybrid cloud services.