IoT: success is not in the data but in the actions

Telecoms.com periodically invites expert third parties to share their views on the industry’s most pressing issues. In this piece by Alexander Khaytin, Chief Operating Officer at Yandex Data Factory, looks at what telecoms operators must do to ensure they seize the data-driven opportunities IoT brings.

Last month, Juniper Research revealed that the number of IoT (Internet of Things) devices are expected to hit 38.5 billion units in 2020, up from 13.4 billion in 2015. This surge in connected devices will also lead to a spending increase: IDC expects IoT spending to reach $1.3 trillion globally by 2019. With traditional services like messaging in decline for many operators, could IoT service now be the golden goose?

Right place, right time, right technology

Increasing numbers of connected devices, as well as a demand for different IoT services from both businesses and consumers, places telecom operators in a strong starting position within the IoT market. A position they can certainly capitalise if they act fast enough, and are smart with their approach.

To seize the opportunities presented by IoT – market growth and increased revenue – operators must expand the range of services they offer to meet demand. However, if operators are not careful from the outset, many may find themselves in a very narrow and low-margin segment of the market – a “pipe” for carrying and transferring encrypted data between devices.

With no access to the data being transmitted, the operator is sadly left in a thankless role of developing and supporting the infrastructure for third-party data transfer. To avoid falling into such a reservation, operators need to plan for the development and realisation of intelligent, marginal services. The longer an operator waits, the fewer chances for a successful entry into the IoT market.

That said, operators have significant advantages against other IoT-hopefuls – an existing, well-developed infrastructure for data acquisition, storage and processing, integrated information systems that provide customer support, and billing systems for all of their subscribers. However, to really benefit, operators must look to offer unique, intelligent services that not only transfer data between IoT services, but also process this data and reduce network load.

For example, an intelligent service, based on computer vision technology, can help to process and store data from a connected security camera by transferring only a single frame. But if motion is detected or an object discovered within the frame, it can immediately increase the flow of data. Such solutions usually require collaboration with equipment suppliers, but operators have accumulated enough relevant experience – sales practices, promotion of devices (modems, telephones, etc.) and broad customer base – to give themselves a head start.

Yet, when it comes to distributing such services, operators often lack understanding of advanced data processing techniques, such as machine learning, not to speak of experience. Overcoming this is needed not only to enter the IoT market of intelligent services, but also to improve an operator’s own internal processes.

Moving to decision making

By itself, an abundance of data that each operator surely possesses, does not lead directly to increased efficiency or further development.

There is already a well-developed market of analytical solutions for operators. However, most of these solutions are based on the rules and processes that significantly hinder progress, often incorporating a “manual” data analysis phase performed by human experts. Such analysis limits the number of factors considered, is time intensive and requires a high level of expertise.

Instead, more advanced but bespoke solutions, like machine learning, can help to gradually replace the expert decisions with computer models. Free from restrictions on the volume of data being processed and the number of factors taken into account, these models can be successfully applied to various internal processes by the operator.

A behavioural analytics solution can successfully deliver targeting for cross-sell offers, assessing and managing customer experience and churn prediction. The ability to predict loads and failure in infrastructure, can also allow operators to reduce costs and increase their efficiency.

On the way to implementing new technologies there is a lot of obstacles. Yet, mobile operators are well positioned in order to start with this, compared to many others. At the same time, they cannot rely solely on past experience. New technologies require a change in mind-set, strategy and approaches towards solving problems. Moving attention from data collection and data transfer, to decision making on the basis of data is maybe the most important step.

Alexander Khaytin is a Chief Operating Officer of Yandex Data Factory. He defines operational priorities and implementation procedures for each Yandex Data Factory project. He oversees projects from concept to completion, alongside contributing to Yandex Data Factory’s partnership, sales and technology strategies. Prior to joining Yandex in 2014, Alexander spent over a decade providing consulting and strategic analysis services for businesses in telecom, construction, energy, retail and finance industries. As a partner at a system integrator, Korus Consulting, from 2011 to 2014, he ran projects for some of Russia’s leading brands, including state-owned hi-tech corporation Rostech, Moscow City Telephone Network, mobile broadband services provider and smartphone manufacturer Yota, Bank Saint Petersburg and Russia’s largest e-payment system Yandex.Money.