Editor's note: We originally posted this article in June 2016, and one year later these technologies still have huge potential disrupting a wide range of industries. We've updated this with additional resources to help with gain a deeper understanding of these emerging technologies as the opportunities to apply them as part of your organization's day-to-day operations increases.
As you take off for your summer vacation, we have 5 technologies in the early stages of their S-curves that you should consider through the last half of 2017 and into 2018—virtual/augmented reality (VR/AR), Internet of Things (IoT), big data/predictive analytics, machine learning/AI, and blockchain.
1. Virtual/augmented reality
When Facebook purchased Oculus Rift in 2014 for nearly $2 billion, it solidified the fact that virtual reality and augmented reality will play a major role in our digital future. Today, technologies like Oculus Rift, Samsung Gear, HTC Vive have created a valuable VR market. This has led to a $4.5 billion valuation for Magic Leap, an augmented reality solution that doesn’t even exist yet.
For businesses looking to update their innovation guide with virtual reality, consider training/educational applications. For example, first responders can use VR to experience “real world” training without actually putting anyone in danger.
Or, an electrical company could use augmented reality to set up a central virtual assistance hub, allowing a “human cloud” of the most experienced technicians that could help junior field technicians to overcome challenges via the ability to see the issues directly and overlay data for the field technician right over the broken equipment parts.
Key takeaway for AR/VR: This technology isn’t just a gadget for consumers and video games—it’s quickly becoming a valuable business tool.
Further reading: How AR and VR will influence the business world
2. Internet of things
The Internet of Things is enabling businesses to use off-the-shelf or custom hardware to achieve large scale data collection within various business processes. However, the value of IoT sensors hasn’t been realized by most businesses just yet.
Amazon is already using the Internet of Things to drive business value by developing the Dash Button. The connectivity of the button has limitless potential as developers can program their own functions into the AWS version of the technology via a simple application programing interface (API). With this kind of technology, companies can take their new product and service ideas and push them to act more human—need a service technician, press the “easy” button.
Key takeaway for IoT: The price-to-benefit ratio in IoT has dropped to a point where businesses should be putting sensors into everything they’re doing. If you don’t, someone else will reinvent your process to be a “smart” process enabled with real-time sensors.
Further reading: Ignoring Your Unstructured Data Will Hurt Where It Counts
3. Big Data and predictive analytics
Predictive analytics often gets put into the same conversation as artificial intelligence, but you can do predictive analytics without AI. Predictive analytics started out simply as statistical analysis of data, but most organizations aren’t realizing true value in this area. New methods of
“teasing out” answers and questions from your data go beyond standard statistical analysis.
So much money has been spent on big data to collect information about customers, supply chains, and other processes; but businesses haven’t found the right ways to leverage this data with predictive analytics.
Key takeaway for big data and predictive analytics: Predictive analytics is about execution right now—putting the data and analysis back into your processes instead of isolating them.
Further reading: Why Predicting Trends Doesn’t Help Prepare For The Future
4. Machine learning and Artificial Intelligence
Machine learning is a newer subset of the long standing discipline of artificial intelligence. However, for almost the entirety of artificial intelligence’s existence, we’ve talked about building and applying rules to machine processes to get them to “think”. Now, machine learning is working backwards by providing machines with a certain set of circumstances and allowing them to infer rules based on patterns and algorithms (with a starting set of human training assumptions).
Fraud protection is one example of machine learning making a significant difference. Companies like MasterCard have already implemented machine learning to track patterns across various users and spot fraud faster and more effectively than with human interaction alone.
Key takeaway for machine learning and artificial intelligence: You need to think about your ill-defined process and data problems for potential machine learning applications.
Further reading: Opportunities for machine-learning startups: An investor perspective
Find out where great products come from with our End-to-End Product Development Guide.
Many CIOs are probably at least somewhat familiar with augmented reality, IoT, and artificial intelligence. But blockchain is likely a new concept.
Blockchain is a way to create a public keychain that continuously grows with new data records and secures itself through constant validation across users. The original and clearest use case for blockchain is the financial services industry, where blockchain was created as a means to distribute virtual currencies while maintaining integrity.
Although use cases for this technology are endless, financial services companies have invested over $1 billion already. Another potential use case is in real estate or the buying and selling of collectibles—any transaction where the purchaser and seller must be validated in real time.
Key takeaway for blockchain: Start thinking about generalized use cases for your business—what is a long-term, written down transaction that blockchain could be the backbone of? The key is to innovate before someone else beats you to it.
Further reading: Ethereum Project
Preparing for a New Era of Digital Transformation
Each of these growing technology trends has its own individual S curve and it’s important to start investing before they really take off. However, there’s a challenge that every CIO must address—the fact that their feedback loops overlap each other.
You can’t think about these technologies in silos. As IoT devices collect more data, big data platforms will be necessary to making sense of everything you’ve collected. And from there, machine learning and AI can analyze trends and pass the data on to humans who can use predictive analytics to understand how people interact with technology.
Understanding how people interact with these technologies will be essential to determining how to create a VR or AR product with a user experience that dominates the market. If you get your design team into the process, they’ll be able to help you formulate the proper UX.
While you’re on summer vacation, start thinking about these technology trends, read up on them and develop an understanding of which ones will be most important for your own digital disruption plans. If you don’t start investing in these opportunities now, you can be sure someone will beat you to the cutting edge.
Succeeding in digital transformation isn’t just about understanding these specific technologies—it’s about creating an innovation center built for long-term development as new waves of cutting edge technology emerge.
If you liked this article, listen to guests Scott Harper, Dialexa’s co-founder and CEO, and Chris Garrick, Sr Partner here at Dialexa, on Custom Made, where we discuss how to drive innovation within an enterprise organization:
Listen to all episodes of Custom Made for insights and perspectives from industry disruptors and technology leaders.
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