Combining IoT Data From Different Entities Will Take Us To The Unexpected Innovation Journeys
Before opening the door for the modern age with his radical invention, one German entrepreneur first bankrupted manufacturing small mirrors with supposedly healing powers which he planned to sell to religious pilgrims.
His next venture which led to the Enlightenment era, the scientific revolution knowledge-based economy is one of the famous examples of combinatorial innovation. Gutenberg has invented printing press not by creating new technology from the scratch but combining borrowed technology from the totally different field, namely wine screw-press for grapes.
He was a craftsman with many skills. If the bubonic plague did not curtailed his target customers (pilgrims) he will probably succeed with the healing mirrors project.
Gutenberg was a blacksmith, goldsmith, gem polisher, and also dexterous with investors.
The movable type, the ink, the paper, and the press itself, already existed before. His radical idea relied in his ability to devise new uses for an older technology.
Many important innovations and scientific discoveries were conceived like this. By borrowing concepts, ideas, technology or simple mechanical parts, from the different areas of expertise, industries, cultures, departments, disciplines and combining them in completely new whole for the new purpose and use. As Arthur Koestler simple explained in his book The Act of Creation:
All decisive events in the history of scientific thought can be described in terms of mental cross-fertilization between different disciplines.
A Few Examples of innovations
Let’s take Henry Ford’s assembly line. Developed for the Model T and began operation on October 7, 1913, produced cars quicker than paint of the day could dry. Pioneering the modern automated assembly line technology, it had an immense influence on the world.
But the concept and idea for the assembly line actually came from one Chicago slaughterhouse. It was by introduced by William “Pa” Klann, Ford Motor Company engineer whose attention caught the efficiency of one person removing the same piece over and over without himself moving.
Regarding programmable computers and data processing industry, punch cards remained crucial for data input, output, and storage, until the 1970s.
But punch card is the early 1800s invention by French weaver Joseph Marie Jacquard who developed the first punch cards to weave complex silk patterns with mechanical looms.
Several decades later Charles Babbage — considered to be the father of modern computing — designed “Analytical engine” which anticipated the basic structure of all contemporary computers were “programs” were to be inputted via punch cards
Modern fitted kitchen built after a unified concept now is a standard in every household. It was created in 1926 by Austrian architect Margarete Schütte-Lihotzky to be practical and built at low cost (Frankfurt kitchen, see image below). She wanted to improve women’s status and to relieve and enable them to pursue other interests.
But this contemporary centerpiece of domestic architecture and family life was inspired as a matter of fact by the Taylorism, 19th industrial optimization concepts and extremely space-constrained railway dining car kitchens.
Austrian architect saw ideal model in the train kitchens because, even though these were very small, two people could prepare and serve the meals for about 100 guests, and then wash and store the dishes.
It is well known how Apple — considered the most innovative company on the globe-borrowed and combined technology either created by other companies or public sector.
But it is lesser known that brilliant design undoubtedly was also borrowed from other source. Namely from products designed in late 50’ and 60’ by German company Braun and its industrial designer Dieter Rams.
Data and Innovation
There is no point reminding the vast magnitude and influence data already has and will have in all aspects of our life that unquestionably come with enormous opportunities it will create in the following years. There are many well know examples of the power of data-driven innovation, improvements, advancement and discoveries.
In the recent Harvard Business Review article “Are the Most Innovative Companies Just the Ones With the Most Data?”, the ubiquity of one improvement powered by data is worth mentioning:
During 2016, self-driving cars by major car manufacturers improved by roughly a third. But, Google collected far more data per car to feed a more advanced machine learning system, and its cars improved by 400%
Google’s self-driving cars are getting better through the analysis of billions of feedback data. Enormously valuable, this kind of data fuels innovation as a raw material fed into machine learning tools. And the more of this data “material” is provided, the better you get.
“Cross-fertilization” and combining IoT data
Application of IoT data proved valuable across the various industries, including the public sector as well. Improving processes and operations, customizing products, adjusting and enhancing services, from building automation and smart factories to transportation and wearables, vehicle manufacturing, agriculture and farming to retail sales and digital services.
State, city governments and municipalities start collecting data from our streets, highways, roadside, buildings and other infrastructure to enhance their services, reduce costs, and improve safety, communication, and interaction.
The question that naturally arises is what can we expect when the data from totally different diverse sources, entities, areas or applications will be used and combined in the powerful machine learning enginee, similar to what great inventors did in the past and are still doing.
Actually, exactly that is the future of IoT according to ABI Research. Services which will enable using or sharing and exchanging data with other entities and third-parties is the next evolutionary step of internet things technology.
A similar opinion gave McKinsey researches:
To maximize the value of IoT data and extract as much as 60% of its potential we need to learn how to integrate and analyze data from many heterogeneous IoT systems simultaneously.
As the number and diversity of data sources and applications grow, the array of new opportunities will emerge.
Possible data “cross- fertilization” scenarios
The first opportunity that comes to mind is that co-creation of collected data from several IoT systems developed for operational processes can significantly increase the creation of sustainable value.
Cars which are moving sensor platform are perfect for collecting tertiary data of various kinds, which combined with geolocation and other data points can be used as intelligence for other stakeholders or participants in the overall traffic system.
Telcos which are collecting environmental data from their network stations coverage could sell data to whether institutions or public traffic departments enabling them to create more hyperlocal and accurate forecasts.
For the insurers sensor-driven pricing is the future, providing more accurate and quantifiable assessments based on real-time conditions of a client’s health, a business’s standing, or real-time usage-based information.
Banks can customize their financial products on the grounds of a real-time capital exploitation view.
The whole specter of mutual benefits arises. Entities could have entirely new revenue streams of data much-needed for other sectors, that would otherwise just go to waste.
For example, the data on the quality of wastewater from production may be useless for the enterprise, but make a substantial impact on the operation of the neighboring hydropower plant.
Transportation entities can have additional revenues by monetizing the traffic data they collect. They could sell it to third-party navigation companies like Google Maps or TomTom to improve their route-planning capabilities.
Actually, there are even now examples of utilizing data from 3rd parties.
McKinsey gives one:
Given the access to information and transactional gateways for businesses such as charging-infrastructure providers, mobility-service players, and vehicle manufacturers, charging-station operators optimize their pricing using available data about customer habits and market trends.
Access to IoT data from other entities will build a more complete picture of how products and services are used. Therefore, we will see more subtle, refined design, workflows and strategies.
Another interesting example is given in E&Y article Every business must monetize its IoT data to survive.
One renowned delivery company is reducing maintenance time and costs, fuel consumption and accidents, using data from sensors. The next logical step was to build its business around the data, which included, collecting data from competitors, and publishing information about the attractiveness of business areas, based on the patterns of parcels delivered.
You can take the journey now with Datapace
Companies, public sector and other stakeholders and entities are starting to realize the huge potential that exist in the multitude of different IoT opportunities interoperability and shared IoT data.
We at Datapace gave our best to create infrastructure where verified streaming data is accessible and affordable for mutual benefits. A decentralized marketplace where anyone have the opportunity to to improve business, operations, research or any kind of other endeavors, acquiring exchanging and sharing the most diverse kind of data. Or just simply monetize data and get paid.
To make the environment for exchange and sharing the most diverse kind of data secure, we implemented blockchain technology, which also enables micropayments, smart contract and fast transactions.
To maximize opportunities we provide access to global network of sensors with hardware verification program and partnerships that ensures data accuracy and validity.
By creating Datapace, a technology allowing us to reimagine our future we are helping innovations, discoveries and creations thrive.
Datapace
Blockchain powered data marketplace with technical and policy-based data verification, and access to the global network of sensors.
P.S.
Searching for his unique style, Vang Gogh closely copied and studied Japanese cheap color woodblock prints…actually 660 of them.
He tacked them to the walls of his studio and used them for inspiration.
To his brother Theo he wrote “All my work is based to some extent on Japanese art”.
