The Growing Amount of Data: The Fourth Industrial Revolution
By Erik Rolland
Most of us grab our mobile phone in the morning and carry it around with us all day. Many of us also use a mobile tracker, like a Fitbit or Apple Watch, for exercise or sleep. Most modern cars have some type of trackable computer. Even your purchases on Amazon are tracked individually from the warehouse to your home.
The result is that we and many of the things around us are never lost (thanks to GPS and sensors everywhere), and we are almost always connected. The byproduct of our connectivity, however, is mountains of data — data that can be processed for a greater understanding and for value creation. In 2017, the Economist magazine declared that data is the new oil: Data itself becomes the key source of value creation and economic growth.
This means that resources will be controlled by those who understand data, increasing the economic importance of data and knowledge as opposed to natural or physical resources. This will reshape economies and continue the shift further away from tangible resources, such as production facilities, toward intangible resources that are derived from data and information-related products and services.
The abundance of data is the foundation for the “Fourth Industrial Revolution.”
Data and technologies are blurring the lines between the physical, digital and biological systems. Driven by both human and machine intelligence, data is independently contributing to unskilled and skilled labor tasks, threatening to replace the human in many cases.
The Value of Data
Prior to the 1990s, we suffered from a lack of data, but the opposite is true today — we have too much data. We often lack time and resources to store, process and analyze data, as well as to make better decisions with it or learn from what we have.
Despite the challenges, the promise remains one of value creation. Your Facebook profile can tell us with a great certainty your psychographic profile. That is, we can quickly understand your political views, who your friends are and what interests you have. Your demographic profile, age, geographic coordinates, education and income are already known by your browser and your computing devices, and such data is constantly being updated and revised.
This information is often beneficial to us as citizens and consumers: our health data can be analyzed and aggregated, and our consumer-related needs can be better understood and addressed. The real promise is for humanity to advance and for organizations to more effectively and efficiently serve their customers.
The challenges of the Fourth Industrial Revolution are not only technical ones. As we become increasingly more efficient in analytics through artificial intelligence and machine learning, we in turn create major behavioral and socio-economic challenges. For example, we may replace or reduce human involvement in many jobs. While that may provide some benefits, such as replacing a car driver with an automated and improved driving system, it can also pose challenges to many professions, even highly skilled ones such as accountants, physicians and lawyers.
For example, in the financial industry it will mean massive job losses, with a predicted decrease of over 200,000 jobs by 2025. Accounting firms are scrambling to provide new value-added services beyond taxes, and the list goes on and on. The social impact will likely be uneven in the sense that higher-income consumers will be more likely to deploy data-driven technologies, and this may lead to wider wealth-gaps in society.
The Future of Big Data - and Work
There is no doubt that big data drives major changes to the future of work; many jobs will change, and some will disappear. It also enables improvements and new job creation in many areas.
On a daily basis, we all make decisions. The success of most organizations, like of individuals, depends on the ability to make good decisions. By augmenting physical data with knowledge collected from other sources (such as behavioral data), we can imagine a future in which our decisions may be much better supported with all kinds of available data, information and knowledge. The key to good decision-making will then be to combine data and information into formats that are usable on the spot. This will include the combination of machine intelligence, human intelligence and tools to allow the human decision-maker to interact with big data and information generated from it.
The future workplace will need experts from all fields to help create the tools that allow human and machine intelligence to come together and make decisions that are far superior to what we expect in 2018.
At the College of Business Administration, we realize that the digital enterprise behind today’s organizations run on complex data essential for timely decision-making and insight into consumers, operations and markets. Our upcoming Center for Innovative Analytics aims at preparing the next generation workforce with skills that leverage the interaction between human and machine intelligence through advanced visualization technology and innovative analytics. This initiative for analytics will serve as a nexus of interdisciplinary experiential learning and innovation that will engage in applied research to address the challenges of organizations and society that are grounded in big data.
We are building on Cal Poly Pomona’s 80 years of leadership in polytechnic education and hands-on experiences. Preparing our students for the future of work means that we are educating them for careers that are just emerging, and for challenges that have yet to be discovered. Our values of community engagement, inclusivity, and social and environmental responsibility take on great importance to ensure that as society moves forward, we lead the change in a positive direction where we do not unintentionally become a society of haves and have-nots.
Erik Rolland is dean of the College of Business Administration at Cal Poly Pomona. His research embodies a range of management and engineering areas, electronic commerce, service science, and modeling of complex technology and management problems.