More and more we are seeing innovative technology that is the result of combining prior technologies and ideas. The book The Second Machine Age, includes a great discussion about this, and cites the example of Waze, a driving app that is basically Google or Apple Maps on steroids.
The secret to Waze is tapping into the power of the network to gain an enriched signature that you cannot get simply through very good and/or accurate street maps and GPS alone. Waze users provide additional data by their own information (location, speed, etc.) along with additional contributed information that, collectively, paint a much more granular, real-time signal as to what is happening at that specific point in time.
In other words, Apple Maps might give the shortest route between your house and your office, and it might even give you some traffic information (which will continue to evolve, for sure), but Waze is going to provide significant depth based on its own users’ contributing rich data that can augment any routing at a given point in time. Nothing about this process is really all that new or groundbreaking, but the combination of technologies enables capabilities that did not previously exist.
Examples abound of innovation largely taking the form of combining existing technologies in new ways. Machine learning is evolving, but the technology of machines reporting their status is not new. Consider thermostats. Thermostats have been around for a long time, and communications technology to take a digital signal and move it to a receiving process is also not new. But combine them in Google’s Nest Learning Thermostat and consumers are captivated with new technology that allows automatic updates to the household temperature as their lives and seasons change. The Nest Thermostat innovation doesn’t stop there. Google also purchased Big Ass Fans, so now the fans and the Nest Thermostat “talk” to each other to optimize conditions in a person’s home, including the energy required to run these objects.
And that’s just the consumer world. Look at what’s happening with smart cities. In Chicago, there is a project called the Array of Things, an open source initiative that ultimately will extend to many cities around the world. The idea is that a number of sensors – none of which is new in and of itself – will be built into a hardened module, or “node.” Each node contains multiple sensors, the combination of sensors varying depending on location and what researchers wish to study (e.g., sound intensity, light, temperature, humidity, barometric pressure, vibration, air quality, etc.).
The city will then deploy nodes all over Chicago on public poles. The granular and diverse set of data gathered will allow greater insight into conditions around the city, including the ability to do forensic analysis to determine cause and effect relationships ranging from air quality to traffic conditions and more. Again, they have combined existing ideas in a way that will generate new insights.
Another example in the smart-cities space is Trendit. The company uses carrier logs, takes network events – all completely anonymous and sterilized for privacy – and combines them with pre-knowledge about a geography to build a dynamic statistical model. This model provides incredible accuracy in a very rich, very real-time signature of the population and the dynamics of that population, and can then also be modeled for investigative and predictive analytics as well.
In the world of the Internet of Things, what used to be impossible to determine, or relegated to models based on minimal valuable information, can now be exposed via rich and plentiful data. So, for example, if there is a threat to the population based on a virus or a hazmat incident, the city can gain insight about the population virtually instantly to make decisions about remediation and deployment of resources. Trendit’s innovation is not in the creation of the data. That was there. It is in the utilization of that data. The data Trendit uses is structured. However, like many other IoT applications, Trendit requires rapid access to vast amounts of data as well as robust, versatile, and rapid query abilities to turn raw, previously available data into insight. In this case, those insights can accomplish quite a bit, up to and including saving lives.
One last example shows the combination of innovation for smart cities with innovation in smart cars. Most would agree that street lights are nothing new. However, Eluminocity, a German company founded in 2014, developed a street-light concept in cooperation with automotive industry and market leaders to implement a state-of-the-art urban electric vehicle (EV) infrastructure. And they are working closely with BMW to ensure that the innovation in the smart city infrastructure can be seamlessly linked to the innovation in smart cars. The company concluded they could integrate EV charging stations for eMobility solutions with LED based light fixtures on light poles. These could then be deployed street-wide in neighborhoods and cities of all sizes. This could eliminate the argument about the scarcity of electric charging stations.
Moreover, the idea can extend to smart parking and network connectivity that can basically let the light poles serve as information points that “talk” to cars in close proximity – a function of more seamless infrastructure that may have seemed like the stuff of movies just a few years ago, but now is clearly within reach. What’s more, if you step back and look at how these technologies are progressing, it appears to be a mathematical certainty that this is a world we will see sooner rather than later.
Smart cities will become more and more a reality. The Array of Things, Trendit, Eluminosity, and BMW are innovative, to be certain, but the basic building blocks of their innovation are largely prior innovations enhanced to achieve what was previously beyond reach. There will certainly be more to come – more in smart cities, mHealth, the Industrial IoT, smart grids, and more in places where the lines are blurred between the focus areas. The possibilities, and the combination of possibilities, are endless.
Don DeLoach is CEO and president of Infobright and a member of the Data Informed Board of Advisers. Don has more than 30 years of software industry experience, with demonstrated success building software companies with extensive sales, marketing, and international experience. Don joined Infobright after serving as CEO of Aleri, the complex event processing company, which was acquired by Sybase in February 2010.
Prior to Aleri, Don served as President and CEO of YOUcentric, a CRM software company, where he led the growth of the company’s revenue from $2.8M to $25M in three years, before being acquired by JD Edwards. Don also spent five years in senior sales management culminating in the role of Vice President of North American Geographic Sales, Telesales, Channels, and Field Marketing. He has also served as a Director at Broadbeam Corporation and Apropos Inc.
Mr. Benny Saban, Former Director & CEO of Trendit, led the company to a successful IPO in London. Mr. Saban is a seasoned executive bringing with him over 20 years of experience in bringing technologies to market. Mr. Saban is an expert in big data analytics products and financial services technologies. Before joining Trendit, Mr. Saban was VP, Product, at Amdocs, building two new product lines: Big Data and Analytics as well as a Mobile Financial Services platform. Prior to Amdocs, Mr. Saban served as VP. Product, Gemalto and Trivnet, designing Trivnet’s state-of-the-art Financial Services platform. Mr. Saban also led two major product lines while working at Orad Hi-Tec Systems (currently part of Avid Technologies).
Subscribe to Data Informed for the latest information and news on big data and analytics for the enterprise.