The way we live and interact with everyday things are being radically personalized by the convergence of Big Data, advanced analytics and the Internet of Things.
The biggest question is how organizations, governments, individuals would leverage the vast amount of machine to machine data that is being emitted by the connected devices and how to make best use of that information.For example: a blade in a gas turbine used to generate electricity creates 520 Gigabytes of data per day. And there are 20 blades in each turbine. An airplane on a transatlantic flight produces several terabytes of data, which can be used to improve safety, streamline maintenance operations and decrease fuel consumption. The amount of data aggregated over weeks, months and years, are astonishing. This type of data automatically generated by the machines and equipments are going to become a fantastic natural resource. The application of predictive analytics on such Big Data has the potential to make companies smarter, more progressive and provide them a competitive advantage.
Companies are using advance statistical modeling techniques to analyze the sensor data and provide real time insights on event correlations, root cause analysis, forecast potential risks and simulate possible scenarios. Machine learning techniques can help us extract and discover patterns and insights from these vast mounds of data. Advanced analytics is poised to become an integral part of business processes to trigger intelligent decisions in real time and changing the way we live our lives, the way we conduct our businesses or the way government machinery functions. Gartner predicts that by 2017, more than 50 percent of analytics implementations will make use of event data streams generated from instrumented machines, applications, and/or individuals. We are already seeing application of advanced analytics on M2M data to drive very specific actions that can help create better customer experience improve operational efficiencies and create better living conditions for citizens.
Energy Efficiency: In our homes, we will start to see energy efficiencies as a result of various sensors embedded in our devices. By understanding the usage patterns of the various home appliances, utility companies can remotely step down or up the energy transmitted to the households and thus helping to optimize energy consumption and the utility costs.
Predictive Healthcare: Machine-to-machine devices, including blood pressure monitors, glucose meters, and electronic weighing scales, have integrated sensors that check patients' conditions at home, thereby saving a trip to the doctor's office or hospital. An M2M monitor can also alert a physician when it detects a potential health problem in a patient. Rather than waiting for a person to end up in the emergency room or hospital, which gets very expensive, the physician can be notified when the patient is in an adverse condition and proactively schedule a visit to the doctor.
Insurance Pricing: The combination of M2M and Big Data can help auto insurance companies make informed decisions about their customers, based on their driving behaviors.
Enterprise network management: Analyze the machine log data coming from various network devices in real time to predict which network devices have a higher propensity to fail and identify potential network outages in advance and hence initiate pro-active remediation actions to enhance customer service levels and experience
Anticipating the next purchase: By monitoring the usage of devices and products by customers and provide pro-active alerts and triggers to the sales team on the right time to contact the customer for a product upgrade/ refresh. This can be very effective to build 1-2-1 relationship with the customer and help cross-sell and up-sell to the customer.
Public safety and management: Analyze the traffic and video signals to discover reasons why traffic congestion happens on certain roads at certain times and help avoid traffic jams. This also helps to predict any security risks by analyzing video images.
We are also seeing promise on M2M related analytics efforts in India
* One of Indias largest automobile companies partnered with an Indian telecom operator for powering its electric cars with machine-to-machine (M2M) communication services, with which users can remotely lock their car, control air conditioning as well as get emergency boost charge for their vehicle. Using an app users can know how much battery is left, schedule pre-heating or pre-cooling, lock/unlock car door, find the nearest charging station as well as get an emergency boost charge to go an extra 8-10 kms.
The India auto, telecom and healthcare sector have already started using M2M a sa competitive advantage. There are various reasons for the present uptake in M2M systems which are likely to continue over the next decade. Some of the key ones are;
* The miniaturization of sensors,
* The plummeting cost of instrumenting an asset with sensors
* Changing regulatory requirements continuous advancement of data science and analytics,
* The emergence of ecosystems and innovations for processing big data effectively.
However, to reap the benefits of M2M business have to put in place a sound strategy by developing a portfolio of pilots to deliver actionable insights from M2M data. They have to develop real-time data analytics infrastructure to support the data velocity and insight needs of digital-physical projects. Additionally, companies will have to put in place a governance strategy to act on real time feedback loops to enhance decisions and proactively address potential data privacy issues as new pilots and projects are developed. Last but not the least; organizations have to start planning for known technology disruptions they will face in the coming years.
Like any other technology with high growth potential, M2M will bring its own set of challenges to the organizations. The deluge of data created through M2M is likely to overwhelm organizations. Organizations will therefore need to find ways to collect and analyze this large volume of machine-generated data to drive bottom-line business benefits. Also organizations will have to carefully manage the data privacy and data security risk associated with the machine data to help mitigate any business risks. Besides, organizations need to carefully invest in analytical tools and talent to help monetize the mountains of machine and device data to improve the business performance.
M2M-based analytics will not only be a high growth opportunity by itself but will also be a key differentiator for organizations in driving actionable business insights to improve business performance and remain competitive and relevant in the coming interconnected, machine dominated marketplace.
- By Michael Svilar, Managing Director and Global Lead for Advance Analytics, Accenture Analytics &
Arnab Chakraborty, Managing Director for Advanced Analytics, Accenture Analytics.
Disclaimer: The views expressed here are solely those of the authors and do not in any way represent the views of the The Financial Express, or any other entity of the Indian Express Group.