5 Technologies That Will Disrupt Your Industry

The extent to which technology benefits our organizations can't be overstated. This applies not only to processes and procedures intended to streamline and improve upon internal operations, but also to the goal of providing products, services and experiences that meet the expectations of clients whose lives are becoming more and more dependent on digital solutions.

The potential is there, and the organizations that understand this have pioneered many technological innovations, fundamentally reshaping the way we do business in the process.

The phenomenon of digital technologies catalyzing a fundamental shift in conventional thinking in business, technology, industry, or culture has become known as digital disruption.

That's no surprise, considering the staggering examples of digital disruption that are already impacting our society. For instance, Spotify and Netflix have famously disrupted the traditional broadcasting industry by instantly delivering digital content tailored specifically to their users' preferences, provoking a fundamental shift in the way that society views the consumption of this kind of content.

Airbnb has revolutionized the way many of us plan trips and arrange lodging, disrupting the hospitality industry in the process. These are prime examples of companies whose innovations in the digital landscape have led to serious disruptions in the traditional approaches to their respective industries.

With new and intelligent technology being released at a dizzying rate, and the fact that over half of all organizations today are living on the edge of serious business disruption, it's critical today more than ever to ensure that technology alignment isn't just supporting the business model, but that it's propelling the organization to massive innovation and improvement.

Robotic Process Automation

For many of us, the concept of automation conjures up images of nuts, bolts, gears, and large machines working diligently on assembly lines; however, the reality is that the potential for automation extends far beyond industrial application.

In this day and age, organizations of all types are utilizing various forms of automation to increase efficiency, saving time, and reducing costs in the process. With that said, as beneficial as automation can be, it doesn't come without challenges of its own - its implementation can prove to be a daunting undertaking.

Traditionally, when automating processes, engineers have had to identify the repetitive tasks that could benefit from automation, design and build the required machines or software, and then tediously program them to achieve the goal at hand.

RPA utilizes software bots that are able to gather data on the ways that we interact with various applications and then emulate that behavior with minimal human involvement. While not every business process is ideal for RPA, most high volume, repetitive digital tasks that follow a consistent set of rules are perfect candidates.

For example, organizations across many industries have implemented RPA solutions to assist with functions such as data entry, billing, employee onboarding, and inventory management. We're just scratching the surface of what RPA is capable of, and, as the technology continues to evolve, we can be sure that it will play a big role in the disruption of many processes that are widely accepted as standard today.

Machine Learning

We've identified Robotic Process Automation as a key potential source of digital disruption, but how can technology actually learn? That's a big question with a multi-faceted answer, but one of the main drivers of this type of digital innovation is the concept of Machine Learning.

To better understand the cycle of Machine Learning, it's helpful to take a look inward and think about how we learn as human beings.

When I learned how to hit a golf ball, my first step was to gain an understanding of the elements involved. In order to accomplish this, I began to collect data. I held the driver and felt the weight of it in my hands, I assessed my surroundings, gauging the direction and speed of the wind, I looked at the ball and compared its size to the head of my club. Only then was I able to draw a conclusion about how to proceed and take an actual swing at the ball.

Can you guess how that went? Much to the amusement of my companions, I missed the ball entirely. I had to reevaluate my data, consider the reasons for my failure, and use that new information to adapt my process in the hopes that my next swing might be more successful.

My second swing wasn't much better than the first, but as the day progressed and I continued to take swing after swing, I was able to continually evaluate my degree of success and use that information to adapt my strategy, training myself in the process.

By the end of the day, using this cycle of gathering data, drawing actionable conclusions, and gaining further data and insight by continually evaluating the success or failure of my results, I was able to successfully adjust my process and consistently hit the ball. Hey, I said I could hit it, not that I could hit it well! This is a simple example that does a good job of illustrating the basic cycle of Machine Learning.

Software works the same way. Collecting large amounts of data helps create data models that enable machines to come to actionable conclusions regarding the meaning of the data. By continually providing feedback to the system regarding the accuracy of conclusions, the technology is trained to recognize relevant patterns and make decisions with a high degree of accuracy. This is the essence of Machine Learning, and its application is already playing an important role in the disruption of existing industries.

Natural Language Processing

As we examine the various technologies that are contributing to business disruption, a trend is emerging: data is the name of the game. Before technology can learn, automate, or make predictions, it must first be provided with relevant data. The more, the better!

One of the challenges that we face in this regard is getting that data from point A to point B. Since computers and humans don't speak the same language, this can often be a tedious process that requires human beings to structure the data in a way that computers can understand before importing it into the system.

Luckily, the tech community has been working hard to bridge that gap. One of the most exciting prospects on this frontier is the concept of Natural Language Processing (NLP). Humans read and write in a variety of extremely complex natural languages that have evolved over thousands of years; however, these languages were developed with the sole objective of enabling communication between human beings. Verbal or written communication with technology wasn't even a consideration until the 20th century, and that's where NLP comes in.

One obvious application for NLP that most of us are familiar with is the advent of the virtual assistant. Virtual assistants like Apple's Siri or Amazon's Alexa are able to act on verbal commands, interpreting meaning of the spoken words and performing the requested task, and this application of NLP is just the tip of the iceberg.

There is an incredible amount of unstructured data that is continually being produced by humans for human consumption, and NLP is opening the floodgates, allowing our technology to interface with that data in new and disruptive ways.

Augmented Analytics

While Machine Learning and Natural Language Processing are already providing many benefits to us on their own, they are predicted to rapidly join forces in the years to come, drastically increasing the potential for businesses to understand the meaning of large amounts of data that once might have been thought too vast and unstructured to even consider.

We've discussed how Machine Learning uses data to come to conclusions based on training and the recognition of patterns, and, as Natural Language Processing continues to provide our technology with a gateway to more and more of our data, we will be able to train our systems to identify important business trends and even make predictions, providing business leaders with important insight that will allow them to make decisions that lead to more desirable outcomes.

The reason augmented analytics is a powerful disruptor is it's ability to create valuable, real-time insight from emerging data sources. Traditional analytics in the form of reports and dashboards are rigid, and manually structured. Augmented analytics turns the table on what users will do with data.

Now, data visualizations are auto-generated. Modern analytics platforms enable rapid and accurate forecasting and on-the-fly decision making. Previously inaccessible or obscure data is now part of advanced auto-prescriptive functionality.

One of the best applications for augmented analytics is machine learning model development. Now, business problems that have a massive number of data points or changing relationships with datasets are approachable like never before. Users are no longer burdened by trying to manually find patterns in data and correlating endless combinations of variables.

Blockchain

World governments generally measure their strength by their ability to project power across human domains (check out this interesting article on measuring power). For centuries, these areas of operation were limited to sea and land, and, in the 20th century, expanded to include the domains of air and space.

The 21st century has brought a new domain to the forefront: Cyberspace.

The digital world is a real, tangible place where we connect, learn, share ideas, seek entertainment, and engage in commerce. This reality has led to important advancements that enable us to engage the digital landscape in more transparent, secure ways.

One such advancement is the emergence of Blockchain technology. Blockchain technology has led to the rise of cryptocurrencies such as Bitcoin and Ethereum, and has the potential for many other applications, but how does it work?

Once we understand the basic concept, it's easy to visualize this chronological series of groups of data as a chain of blocks, but you might be wondering what sets Blockchain technology apart. After all, our society is already successfully using various types of ledgers to record transactional data, so what does Blockchain bring to the table?

The answer lies in the cryptographic and decentralized nature of the technology. Traditionally, ledgers have been maintained in a central location, like a book or a spreadsheet, and the data contained within these ledgers is often not encoded in any way. This can work when we trust the way the ledgers are maintained, but it puts the data in an unnecessarily vulnerable position.

With traditional ledgers, it can often take just one malicious party to access the data and permanently alter it.

Blockchain technology solves this problem by cryptographically encoding all data within the Blockchain and distributing the entire chain across a large network of computers. Multiple computers on the Blockchain's network must verify and agree upon any changes to the encrypted transactional data before a new block can be added to the chain.

The high level of trust that Blockchain users have in the security of their data has led to the widespread adoption of cryptocurrencies as a safe alternative to traditional currency and has already resulted in a degree of disruption to traditional transactional models.

As we discover new applications for Blockchain technology and further integrate it into our lives, its potential to digitally disrupt current models demands the attention of business leaders who wish to stay ahead of the curve.