The Internet of Things is here
The future is here and looking at us square in the eye. Yes, we are talking about a world in which our personal and professional lives are more digital than ever before and connected in a social world by first and second degrees. We follow topics of interest, start communities on the Internet at the drop of a hat and have interests that drive business trends and consumer markets based on community interests.
The big question that the commercial, consumer and business industries face is, are they ready for the onslaught of data? Other questions follow such as do they know how they will secure the right data segments and how will they do it? Who can govern the data over a period of time? And do they know which backup strategies will be needed for this infrastructure to evolve? These are all questions for categorizing and strategizing the Internet of Things.
At this point in time, the foundational step for the Internet of Things is as an evolving model of machine learning and artificial intelligence mixed into algorithms for managing and monitoring systems, users and applications. Several entities that make up the Internet of Things will require answers to questions not only for today, but for tomorrow as well:
- Artificial intelligence
- Data analytics
- Data architecture
- Data formats
- Data lifecycle
- Governance models
- Machine-to-machine conversations
- Platforms: cloud and mobile
The questions that we plan to ask in the future also bring a perspective on skills. The next generation of the workforce is the millennial generation—a Google-and-collaboration-based group of innovators and thinkers. This characterization means that, for those of us who are innovating the future today, we need to create a platform that is not focused on technology but focused on data.
These opportunities open a world of new frontiers and innovation that is very exciting, and a lot of this newness exists in terms of platforms and possibilities. As we look into the new world enabled by data, the algorithms we can execute in this realm are all here and more are being looked into. They are all very flexible and not mathematical in nature; and they have started creating an effect on the world in some way, shape and form.
The programming world today is drawn into providing solutions to create and enable platforms. We see the emergence of technologies that are coming with scalability, security and support as mandated by the requirements of data. For example, think about the interconnected things in our lives today. We can control our home heating, cooling and lighting from remote applications. This capability is fed with information on everyday usage of power and resources from our smart thermostats and grid meters.
Device-based connectivity and conversations are driven by data that is transmitted between all the devices and include encryption, transmission and decision support. Another example is the electric cars that are sold today by several manufacturers. These automobiles come with rechargeable batteries. Moreover, the batteries can start drawing a charge from nearby charging stations that can indicate how long the charging will take. The repeat charges can be transmitted to the manufacturer with encrypted vehicle identification numbers (VINs) and battery numbers for statistical and mathematical purposes.
Another example is the rising use of personal, wearable health devices provided by vendors such as Android, Apple, Fitbit and Nike. People can wear these devices when exercising and during normal activities such as walking, sleeping and eating. These devices upload statistics based on your profile, age, and fitness and health goals to your smartphone, tablet and laptop for you to use and update.
As we can see, a lot of data is produced and shared with the producers of these products based on their requirements for further usage. The new world will have more data from all areas including some of the examples discussed here. The challenges are how to collect the data, sort through the noise and acquire the signal in a streaming process and then collect the entire set of signals for algorithmic analysis on a time-period basis in a 24-hour cycle.
Stay tuned for another installment in this multipart series discussing these challenges and their associated solutions, algorithms and statistics. It also looks at how IBM is applying IBM Watson, how Apple and Google are experimenting with Apache Hadoop in their back-end systems and how Qualcomm and Cisco are looking at the future. The world we live in is becoming increasingly flat, and the geographic limits are now more complex than ever before. Where do we go next?
Editor’s note: This article is offered for publication in association with the Big Data Seminar 2017, 16–17 November 2017, in New York City, at the Hotel Pennsylvania, and sponsored by Data Management Forum. Additional information is available in the Big Data Seminar flyer.