Opinion Category

Is your network IoT ready?

By Manish Bhardwaj


The Internet of Things (IoT) has been a popular buzz phrase for the past 18 months, but the scale of devices to which it applies has been fairly limited. However, developments in network technology that will support the IoT as it comes of age, along with emerging use cases that will propel the mass deployment of IoT devices across the enterprise and in many vertical markets, are finally driving broader scale IoT deployments.

IoT devices can drive significant improvement in business processes by delivering a level of knowledge about workflows that has never been available before. For example, by equipping IoT devices with location technology, a hospital can reduce the time required to transport critical equipment where it is needed most, saving not only costs, but, more importantly, lives. IoT devices will also enable machine-to-machine (M2M) processes, offloading labor-intensive procedures so companies can redirect their human resources to more crucial tasks.


Working on Wi-Fi


IoT devices typically connect to Wi-Fi networks directly or utilize Bluetooth tethering to a Wi-Fi device such as a tablet or Smartphone for transport. With the pervasiveness of enterprise Wi-Fi networks, Wi-Fi will be the primary transport option. Bluetooth, or Bluetooth Low Energy (Bluetooth LE), specifically, will be utilized as the edge connectivity when power requirements are constrained.

As Bluetooth-based IoT devices are more widely deployed, Bluetooth LE technology will be integrated into some Wi-Fi access points to provide enhanced connectivity and management options. While several other wireless technologies have been considered for IoT devices, most manufacturers recognize the importance of limiting the breadth of connectivity options to allow for a richer experience and to promote interoperability.

The growth in IoT devices will introduce some significant challenges for businesses that embrace the technology. For example, while some IoT devices connect to application servers within the enterprise, the bulk of these devices today leverage Cloud-based application servers and analytics systems that reside outside the enterprise, introducing security concerns. Enterprises will need to secure these devices to prevent them from becoming entry points for hackers to break into the enterprise.

Also, because of the varying lifecycles of IoT devices – such as a refrigerator having a much longer lifecycle than a Smartphone, as one example – ongoing support of firmware updates will vary dramatically, leading to a diverse set of capabilities across the IoT ecosystem.

Some devices will receive firmware updates often and others will lack firmware updates or retain connectivity technology that will become antiquated due to their long product lifecycles. Enterprises will need to ensure that their networks support and provide security for legacy IoT devices that use yesterday’s generation of wireless technology and that do not maintain security updates in the firmware over time.


IoT Infrastructure


As enterprises begin building a network infrastructure to support the Internet of Things, they must address the following challenges:

Device density on networks will soar. Previously, the number of devices on a network was tied directly to the number of people on the network. With the IoT, devices will be associated with machines and processes, as well as people. Enterprises must design their networks to support exploding device densities beyond what #GenMobile drove in the past year, upgrading their Wi-Fi networks to 802.11ac and ensuring that their edge access switches are properly engineered to support the increased device density.

IoT devices will introduce new potential security vulnerabilities. Enterprises will increasingly need to design their networks with firewalls that isolate the devices to prevent them from becoming a jumping-off point for hackers into the enterprise. With the number and variety of operating systems the devices will utilize, businesses will find it difficult to patch all the new security vulnerabilities the devices could introduce. Enterprises should consider segregating select IoT devices onto parallel networks, creating additional layers of security to protect sensitive data and applications.

Some IoT devices will be sensitive to real-time operations or will require significant bandwidth for operation. To support this class of devices, enterprises need to make sure their infrastructure has the intelligence to automatically identify IoT devices and applications on the network and, subsequently, prioritize or police traffic-based on administrator-configured policies.

With the escalating number of IoT devices, the management of these devices, including both configuration and monitoring, will be critical. Enterprises must enhance their network infrastructure to perform inventory of devices, configure device settings, monitor operations and the health of IoT devices, and manage device firmware where applicable.

As the IoT comes of age, enterprises need to look for network solutions that provide the connectivity and management capabilities that IoT devices will require as they become more pervasive, as well as secure their infrastructure against the new threats these devices can potentially introduce.

* The author is senior marketing manager for the Middle East and Turkey at Aruba, a Hewlett Packard Enterprise company



Stop ‘Garbage In, Garbage Out’: Ensure Data Accuracy

Standfirst: Is the famous saying still relevant? After all, GIGO became a popular term sometime in the 1960s.

By Emma Isichei





Today, with Smarter systems and processes, there is less ‘garbage out’. Unfortunately, there is still a lot of ‘garbage in’: the data entering a business is all too often ‘bad’, wrong or incomplete. Data accuracy remains a challenge, especially as the quality and accuracy of data are of the utmost importance in this data-driven economy. With poor ‘data in’, we get errors, unsatisfied customers and underwhelming customer service and experiences. In addition, we also lose money and productivity.

There are several reasons why there is still so much ‘garbage in’: wrong choices, poor data quality and human error. The good news is that all these causes can be resolved.


Fighting the cost of poor data quality at the source


Almost two years ago, Gartner estimated that, on average, poor-quality data costs organizations a whopping $14.2 million annually. That is huge and more than enough reason to take action.

But how do you start? Where do you begin? The most obvious place to start is where the data that you and your customers need enters the organization and flows into the processes that turn them into intelligence, knowledge and valuable ‘output’. Data comes in across a broad variety of sources and channels. Let’s look at a few of them and, more importantly, ways to have better data and information coming in through them.


Here are some ways to beat ‘garbage in’ and improve data accuracy in capture:

Automation: Manual data entry is prone to errors and, overall, human errors are among the main reasons for data quality issues. It’s important to automate where it makes sense and avoid the ‘bad data in’ where human mistakes lead to financial losses and slow processes, essentially also resulting in missed revenues and hidden costs. Automating manual processes regarding data input is all the more important today as data is a key asset in the digital business.

Document capture excellence: A lot of data still comes from paper sources, often standing in the way of digital transformation. But paper will not go away any time soon, for so many reasons. So, let’s just make digitization better! To ensure the accuracy of the information that sits in paper documents and thereby enhance efficiency, it’s important to choose document scanners that enable the best image quality, with integrated image processing, among other features.

Professional capture software: Hardware alone is not enough: the capture software plays a critical role too. With Kodak Capture Pro software, for instance, you can be sure that you can avoid costly errors and reduce human errors to the maximum extent possible, while enhancing efficiency and data quality.

Processes: Processes are key, regardless of the data source or channel. And, even if you use the best document scanners, capture software and other data capturing sources, the processing and routing of the information across various connected systems needs to be fast, correct and streamlined.

Close to the source: Capturing data close to the source with the best capture solutions and streamlining information processes means a world of difference for your business efficiency. And it helps you save a lot of money. This goes for paper documents, where the need to capture close to the point of entry is known to reduce errors and speed up processes, but it also holds true for many other ways of information and data capture, where we want to shorten the time between capture or ‘data in’ and output or ‘data out’. While data is a core asset, speed is a key differentiator in an age where the right information needs to be available in the best and most rapid way without any loss of data quality.


** The author is the worldwide category director, Capture Solutions at Kodak Alaris


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