“Big data has passed the early adopter stage,” asserts Carol Jon. “The early adopters have taken the big data ball and ran with it. Everyone else has to climb aboard the big data ship or it’s going to start leaving them behind.” What Jon really means is enough companies have now implemented business projects that take advantage of collecting and analyzing big data that evidence now supports the conclusion that such projects offer a good return on investment. Stating that “big data has passed the early adopter stage” leaves the mistaken impression that big data is something new. Jamie Davies (@) reminds us, “Big data as a concept has in fact been around longer than computer technology, which would surprise a number of people.” He’s correct, of course. Since the beginning of recorded history, humans have collected and analyzed data. Until relatively recently, however, that collection and analysis process was done manually. The big data era is not so much characterized by the amount of data being collected — although it’s exponentially larger than data collected in the past — as it is about how that data is analyzed. The big data era is characterized by advanced analytics that allow computers to pore through massive amounts of data in a reasonably short amount of time to discover insights and hidden relationships. In other words, advanced analytics is what makes big data come alive.
“The promise of big data, and data analytics more generically,” Davies explains, “is to provide intelligence, insight and predictability but only now are we getting to a stage where technology is advanced enough to capitalise on the vast amount of information which we have available to us.” Although data collection is as old as recorded history, Patricio Davila, Sara Diamond, and Steve Szigeti, all from OCAD University, explain what is new is that data is now considered a commodity as precious as oil or gold. “We can describe data as one of the remarkable new materials of the 21st century,” they write, “as important to our future as water.” They add, “Data are measurements of other things: physical phenomena (such as weather patterns) or virtual phenomena (such as telecommunications packets). Every time we search for an online movie, view a video on our mobile device, tweet a comment about a news article, upload a photo to Instagram or are directed to a new location in Pokemon Go, we are producing and responding to data.”
As database size increases, artificial intelligence systems (aka cognitive computing systems) become increasingly necessary to analyze structured and unstructured data. The staff at Information Management put together a list of a dozen ways that big data analytics are going to develop and affect society. They include:
- Empowering individuals. “The growing influence of individuals will transform existing societies and industries. Digitation will force providers to extend their existing business models to be more customer-centric, embracing the increasing power of the individual.”
- Decentralized collaboration. “Many people and all kinds of things will be linked to the Internet, resulting in innovation. Each component will act autonomously, and a new ecosystem will be built where relationships will change dramatically.”
- Hyperconnected society. “Big data analytics will fuel innovation. Products after shipment will become ever-evolving things with growing functionality and performance. This in turn will boost customer value and promote the transfer of business models.”
- Smarter society. “The physical-digital convergence will broaden in scope. More flexible and effective use of technology will create new value, improve social issues, and lead to a smarter society.”
The IM staff also identified eight technology trends that will be driven by machine learning, artificial intelligence, and engineering innovation. They are:
- Immersive interaction (i.e., virtual and augmented reality). “Devices and technology to enrich one’s perception of reality is emerging. By enabling people to naturally perceive and utilize more information, new computer interaction can potentially transform human behavior and enhance the scope of their actions.”
- Precision life science. “DNA analysis has become readily available and obtaining continuous biological information will be easy through the use of sensors. Analytical research utilizing large volumes of shared data will enable a better understanding of people’s lives leading to positive changes.”
- Human/machine collaboration. “Advanced machine learning algorithms will enable computers to understand time and be aware of context. Hence, the roles of computers will expand. The coexistence of humans and computers will advance through an evolution where people will take charge of work to realize overall optimization.”
- Autonomous mobility. “Next-generation mobility centered around connected cars will innovate the transportation of people and things. Cities will develop as a fluid system through real-time, mutual sharing of information, including the transportation of people, the operator’s condition, and the external situation.”
- Ambient commerce. “Service tailored to the preference, affiliation, and condition of individual customers will emerge. The continuum of customer point of contact, from discover, purchase, payment and receiving of products, will become seamless, enabling customers to have a stress-free experience.”
- Distributed mesh computing. “New distributed architecture has appeared to adapt to cloud-native applications and big data processing. ‘Block chain,’ the peer-to-per bitcoin platform, is expanding its application to diverse systems, not limiting its use to virtual currency.”
- Cyber-physical security. “The advent of Internet of Things has extended the impact of cyberattacks to the physical world, requiring every device to have security measures. To cope with the growing scope of cyberattacks, joint defense, such as immediate sharing of threat information, is required.”
- Engineering innovation. “The application of new technologies to manufacturing, such as virtual reality, sensors, 3D printers, and robots, will lead to sophisticated digital manufacturing. The application of design methods that make products evolve through the repetition of high-speed verification will expand.”
Laura Segarra and Hamed Almalki, doctoral students at the University of Central Florida, report, “Our research indicates that businesses stand to gain from the wealth of data they collect both internally and from their customers.” They add, “While the exact techniques for applying big data analytics will greatly depend on the business’s goals, objectives, its usage is virtually unlimited. We believe innovation will differentiate between great usage of big data and poor usage of it.” Davies concludes, “Big data has entered the business world; true AI and automated, data-driven decisions may not be too far behind. Data is driving the direction of businesses through a better understanding of the customer, increasing the security of an organization or gaining a better understanding of the risk associated with any business decision. Big data is no longer a theory, but an accomplished business strategy.” It is no coincidence that cognitive computing has emerged at time when oceans of data is being collected for analysis. Cognitive computing and big data have a symbiotic relationship that companies can exploit to make themselves more efficient and relevant. Some companies are reluctant to become early adopters of new technologies; but, as Jon noted, big data projects are beyond the early adopter stage.
 Carol Jon, “5 Ways Big Data Will Change Every Industry,” DZone, 27 August 2016.
 Jamie Davies, “What is the promise of big data? Computers will be better than humans,” Business Cloud News, 6 June 2016.
 Patricio Davila, Sara Diamond, and Steve Szigeti, “There’s no Big Data without intelligent interface,” The Globe and Mail, 22 August 2016.
 Staff, “Future of Big Data: 12 Society & Tech Trends to Expect,” Information Management, July 2016.
 Laura Segarra and Hamed Almalki, “Big data helps firms improve efficiency and customer relationships,” London School of Economics Business Review, 19 July 2016.