Telecoms: Make SLAs Your Best Sales Rep

Just as wireless and mobile communications redefined traditional networking, so are the Internet of Things and IoT analytics redefining the traditional concept of the service level agreement, or SLA.

This comes at a time when the telecom industry is splitting into more and more specialized segments, with network providers serving mobile net operators and MNOs serving mobile virtual net operators and pretty much everyone servicing widening ranges of business customers.

For many of these companies the SLA has been regarded as a necessary evil: necessary to secure the business contract; evil because of the very specific consequences of failure to provide the agreed service levels.

Surgical Precision

But no more. Thanks to the ability of IoT analytics to deliver operational metrics with surgical precision, increasing numbers of telecom operators are now able to build customer-appropriate KPIs into their business-to-business service agreements.

This carries the potential to make remake the SLA into more of a competitive differentiator than ever before, and it stands to benefit the many mobile operators who are making new headway into business markets. Global analysts Ovum have noted that businesses in retail, hospitality and healthcare are now looking for “bundles that offer vertically integrated software, including e-commerce options to allow for real-time sales transactions or service delivery for their end customers.”

The same is true for other industries. Ripe for customer-level service notifications are insurance, energy, and commercial transportation, as well as the many suppliers that will be selling and supplying autonomous automobiles.

IoT analytics will be critical because businesses will require widely varying levels of information and support. To optimize fuel, maintenance and routing efficiency a trucking fleet will want to know as much as possible about the trucks, drivers and the roads they’ll be using, as well as their own customers. This way, they’ll be able to make route changes requested as the last minute by customers, or they’ll be able to proactively alert a customer to a late delivery. This is a far better way to maintain brand loyalty than having to hear about the problem from an irate customer.

A more telling example of the customer value of IoT analytics is in a healthcare application such as critical patient monitoring, where patient sensors will communicate heartbeat, blood pressure and other vital signs. Other applications include predicting clinical evens such as epilepsy or sepsis, or even predicting patient falls.

IoT Analytics In Patient Monitoring

In patient monitoring applications biosensors send event data to a medical device gateway for authentication, encryption, and noise filtration, then for translation into medical-standard, observation-quality data. This data then goes to an analytics engine that can issue immediate alerts and predictions.

The medical data is also stored in a HIPPA-compliant, standards-based data warehouse to be used as contextual information for ongoing analysis. This information then informs other analytical functions and models to produce additional intelligence about the patient.

For this healthcare application, a provider’s SLA could customer-specific KPIs such as “no more than eight percent of devices must be have reporting periods longer than five minutes.”

It’s important to note that to achieve this level of detail at ultra-high speed will require multiple different handoffs among multiple members of the network ecosystem, from network provider to mobile operator to virtual mobile operator to customer, and possibly with others in between. Each handshake will feature an SLA to govern the specifics of that particular handoff, thus allowing the final action – the timely and accurate monitoring of the patient – to succeed.

Robust Analytics Frameworks

An effective service-provider ecosystem will likely have IoT analytics in place at each juncture to create the end-to-end process orchestration. The analytics themselves will be continuously learning and updating their specific contextual models as they receive new feedback from the sensors, or from their ecosystem partners.

This will be true no matter who the end customer is, whether in healthcare, energy distribution, supply chain or other industry. In each market, and markets for IoT analytics are growing by the day, the details of operation will be different. That’s why the components and performance of IoT analytics frameworks will have to be extremely robust, combining ultra-high-speed analytics engines with wide-ranging modeling capabilities and easily accessible data stores or data lakes.

To succeed at delivering the optimum, end-to-end service to the customer – and the patient – an analytics framework will be tasked with smoothing out data flows across a number of obstacles. Analytics will be called on to correlate network data from operational support systems (OSS) with customer-level data from business support systems, or BSS. They’ll need to be able to predict, and even prevent, potential ecosystem breakdowns. And they’ll be called on to simplify the growing complexities of IoT-enabled business processes.

If the analytics frameworks and engines work correctly, the analytics chain will make it possible for all levels of providers to serve their customers with greater assurance than ever before. And analytics will make it possible for the ecosystem providers to produce SLAs with greater specificity, and greater marketing thrust, than ever thought possible.

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