The Internet of Things is vast. Vast in the variety of devices attached, in the amount of data generated, and in the potential for great business intelligence to improve how we consume and produce things. Who wouldn’t want to be able to turn off the lights at home while away, or leave it to their refrigerator to make sure they know when milk, butter and other staples need to be replenished? But there’s more to the IoT than lifestyle enhancement. It also includes a corporate side, enabling organizations to collect and analyze data from sensors on manufacturing equipment, pipelines, weather stations, smart meters, delivery trucks and other types of machinery.
IoT analytics applications can help companies understand the Internet of Things data at their disposal, with an eye toward reducing maintenance costs, avoiding equipment failures and improving business operations. In addition, retailers, restaurant chains and makers of consumer goods can use data from smartphones, wearable technologies and in-home devices to do targeted marketing and promotions — the business side of the IoT’s futuristic world of connected consumer gear.
Prior to the emergence of the IoT, taking a step back to analyze all of the information provided by the assortment of devices it can encompass was exceedingly difficult, if not outright impossible. What is the business value of the data generated by the IoT what some call big data? And what do we need to do to realize that value?
Some of the challenges still ahead preventing us from enjoying the full value of IoT analytics include the fact that all these devices are independent, and there’s no way for anybody to aggregate that data together and no standards yet.
Big data is a bit of a misnomer. Certainly, the volume of information coming from the Web, modern call centers and other data sources can be enormous. But the main benefit of all that data isn’t in its size. It’s not even in the business insights you can get by analyzing individual data sets in search of interesting patterns and relationships. To get true business intelligence from big data analytics applications, user organizations and BI and analytics vendors alike must focus on integrating and analyzing a broad mix of information — in short, wide data.
Future business success lies in ensuring that the data in both big data and mainstream enterprise systems can be analyzed in a coherent and coordinated fashion.
In addition, most users want evolutionary advances in technology, not revolutionary ones. That means intelligently incorporating the latest technologies into existing IT ecosystems to gain new business value as quickly and as smoothly as possible.
The Internet of Things (IoT) also needs be taken into account. Sensors and other tracking devices are proliferating in products and industrial equipment, and they can send the operational data they capture back to corporate systems via the Internet. But many people have a mind-set that the IoT solely provides better command and control of machinery, as in remote sensors monitoring oil pipelines or gathering maintenance-related information from trucks, tractors and other vehicles.
Although such uses are important, even bigger issues are at stake. Looking for trends in massive amounts of sensor data can help users better identify and understand quality control issues, geographical differences in equipment performance and other critical factors for long-term planning. The information generated by the IoT is structured and, over time, likely will dwarf the data collected from the Web. Once again, a narrow focus on unstructured data will lead to organizations missing out on a valuable form of business information.
The next generation of BI and analytics technologies must address the fact that the breadth and complexity of the data flowing into corporate systems is more important than the data volume is. The big data era isn’t just about bigness. Width matters, too, and BI and analytics managers should make sure the vendors they work with understand that fact.
CaptiveXS Puts IoT Security Analytics Where They Are Needed
Local IoT analytics with fast, reliable action to deal with the different devices connecting to a business network is the approach used by RaGaPa with its CaptiveXS solution, which offers the following capabilities:
- Automatic classification of devices into categories (camera, appliance, sensor, light, wearable, and so on)
- Out-of-the-box ranking of IoT devices as high, medium, or low risk
- Alerts for first-time connections of devices
- Alerts for unusual activity, such as a device downloading greater volumes of data than normal, or accessing sites or network addresses never accessed before
- Easy to read views of device threats, showing their location on an on-screen map
- Continually updated statistics on data upload and download per device
- Detailed information on devices and users, such as device owner name and email address
- At-a-glance analytics overview of Total Connected, Total Authenticated, Total Quarantined, Total ByPassed devices
Actions from Analytics for Effective IoT Security
Knowing about the threats is good, but taking the right action is even better. Besides IoT security analytics, CaptiveXS provides business users with options for automatic and user-initiated actions, including:
- Quarantining of misbehaving or hacked devices from the cloud
- ByPass authentication for trusted devices, notably devices used internally by the business owning the network
- Additional user-defined policies to be applied per device type, including blocking and capping of data throughput and bandwidth
Well suited to small and medium sized businesses (SMBs) as well as larger entities with distributed branches and outlets, CaptiveXS provides the right analytics and actions, in the right place, at the right time to make sure IoT security remains immediate and effective.