The Electrified Third “Data” Rail — How Data is Powering the Fourth Industrial Revolution
The world is collectively embarking on a collision course with the Fourth Industrial Revolution, powered by a new source of ‘energy’, a new ‘fuel source’: data. Unlike the previous Industrial Revolutions which were powered by steam, electricity, and combinations of automation or mechanization or computerization, the next wave of revolution will be accomplished via data, the translation of data into information, and ultimately contextualizing that information to turn it into knowledge. The Fourth Industrial Revolution, also called Internet 4.0, will be solely powered by the explosion of data which will ultimately feed into technologies and constructs such as Internet of Things (IoT), AI & Machine Learning, and Blockchain — all with the intent of making useful sense of the deluge of data.
The data tracks the have been laid in the previous wave of digitization (Third Industrial Revolution) still remain, but the Fourth Industrial Revolution will be electrified and truly come to fruition by the proverbial ‘third rail’: turning data into a powerful commodity — as powerful as steam, electricity, coal, and oil in the traditional economy.
The Data Dump
Right now, the world is generating a literal mountain, a Mount Everest, of data every day. Best estimates indicate that the collective ‘we’ produce 2.5 quintillion bytes of data every 24 hours. Moreover, the rate of data generation is truly exponential in nature with 90% of the world’s data produced in the last 2 years alone. Furthermore, with the proliferation of IoT, more and more connected devices will come online and begin generating their own data. In 2006, there were 2 billion connected devices — by 2020 the number of connected devices will reach 200 billion. Making sense of all of this data is akin to drinking from the ocean (since not even the ‘drinking from a firehose’ analogy suffices here!). Currently, there exists an asymmetric relationship between data production and our collective ability consume that data in order to create meaningful sense of it — in the business world or otherwise.
Economically, all signals point to the importance of data, understanding data, rationalizing data, and organizing data — this is why Data Scientists these days get paid the big bucks. Data Scientists are equivalent to the Engineers and Computer Scientists of the previous Industrial Revolutions. By 2023, all signs point to the big data analytics market being worth in excess of US $275 billion with a 12% compound annual growth rate. Data is big business, and as the Fourth Industrial Revolution is concerned, it will really be the only line of business that will matter.
Klaus Schwab, Founder & Executive Chairman of the World Economic Forum, writes extensively about The Fourth Industrial Revolution in a book by the same title. The Fourth Industrial Revolution will be the rewiring and fusing of the physical, digital, and biological worlds. A more encompassing definition of the Fourth Industrial Revolution is given by Bernard Marr of Forbes:
“The Fourth Industrial Revolution describes the exponential changes to the way we live, work and relate to one another due to the adoption of cyber-physical systems, the Internet of Things and the Internet of Systems. As we implement smart technologies in our factories and workplaces, connected machines will interact, visualize the entire production chain and make decisions autonomously. This revolution is expected to impact all disciplines, industries, and economies. While in some ways it’s an extension of the computerization of the 3rd Industrial Revolution (Digital Revolution), due to the velocity, scope and systems impact of the changes of the fourth revolution, it is being considered a distinct era. The Fourth Industrial Revolution is disrupting almost every industry in every country and creating massive change in a non-linear way at unprecedented speed.”
Data: The Next Precious Resource
Each previous Industrial Revolution has been powered by precious resources, as well as leaps in human ingenuity to convert natural-world inputs into mechanized, electrified, automated, or computerized outputs. The Fourth Industrial Revolution is different. For the first time, it isn’t the manipulation of natural-world inputs that will be of concern — it will be the manipulation of intangible resources — the manipulation of cold hard data.
So how should businesses even attempt to wade through the data quagmire that is most likely already upon their doorstep? The first thing to do — is to have a firm and intimate relationship with the data that your organization is generating, both now and in the immediate future. Why? Because data is the foundational backbone of any business strategy these days. Period. Whether you realize it or not.
The Data Lifecycle consists of 6 stages — mapping the full journey of a data point.
- Data Creation or Capture
- Data Storage, Organization, or Processing
- Data Use or Analysis
- Data Publishing or Sharing
- Data Back-Up or Maintenance
- Data Destruction or Re-Use
Definitively, it’s the first two stages of the data lifecycle that need to be better understood by businesses as they navigate changing strategic landscapes and new and emergent technologies.
Data Creation or Capture
- What it means?
The data creation or capture phase consists of all the possible permutations of generating data or the acquisition of data via data capture mechanisms.
- Tools & Technologies?
Technologies that generate data are too numerous to list out — and the act of data generation in the data lifecycle should be self-evident. However, the act of data capture or data acquisition via real-world inputs forms the sensoring or end-point network layer in the Internet of Things. In the Internet of Things world, data is captured via sensing real-world parameters like temperature, pressure, indications, on/off, speed, humidity, altitude, moisture, vibrations — basically anything that can be measured by a digital sensor today. As 5G and Narrow-Band IoT are around the corner, the IoT end-point layer will explode since the act of data capture and data acquisition will cost less (low hardware costs), sensing hardware will last longer (low power requirements), all driven by sub-GHz frequencies of communications (longer communication pathways).
Data Storage, Organization, or Processing
- What it means?
The data storage, organization, and processing phase references the means of achieving both short-term and long-term storage, across both cloud-based data storage or local data-center methods, real-time data processing, and how to store and organize data for future use or analysis. There are a number of ways of storing data and it’s important to recognize that the way that data is stored will affect the way it can be potentially used or analyzed later. On a spectrum between distributed ledger and machine learning outcomes, we find the following database types: 1) Blockchain, 2) Flat File, 3) Relational, 4) Graph, 5) Full Text, and 6) Data Lake.
- Tools & Technologies?
It’s interesting to note that the way data is stored, organized, and processed will ultimately pave the way for how it can be ultimately consumed and used. It’s also interesting to note that Blockchain constructs are at fundamental odds with the construct of a Data Lake which promotes consumption by deep machine learning algorithms powered via neural networks. This example is meant to show that data storage, organization, and processing is a very truly important consideration that will either be in alignment with certain tools and technologies, or not.
Data: The Basic Building-Block of Future Business Strategy
When building an outcome-based business strategy, here are a list of questions that can help begin to frame your thinking about the relationship your business has with data today, and how that relationship with data with data will change into the future. In many respects it is best to view data as a commodity, and understand how data inputs will be turned into information, and knowledge outputs by your business. These questions will likely spurn a whole set of new questions — and that’s the point — this is not to be seen as an exhaustive list, but meant to be a starting point. A starting point to better understand the most precious resource of the Fourth Industrial Revolution: data!
- Is the data valuable?
- Is there a willingness-to-pay for the value extracted from the data?
- Is the data rare, precious, niche, or expensive to obtain?
- Is there an intrinsic value that is extracted by ensuring the data’s wholeness or accuracy?
- Is there a requirement for data permanency?
- Is real-time access to data important?
- Is there a requirement to check and validate the data, and by whom?
- Is sharing of data amongst business ecosystem participants important?
- Is there a requirement of how data can be creation, and by whom?