Innovation Enablers: Eight Game-Changing Digital Technologies for Enterprise Transformation (Part 3 of 3)

 In AI, ML and Data

Carrying on from where we left off, the final post of this series covers three enabling technology trends (Machine Intelligence, Blockchain, and Containers) that are broadly applicable to various aspects of digital transformation across different industries.  The full report, which includes additional material on how businesses can leverage these trend to drive their digital transformation, is available for download here.

Trend 6: Machine Intelligence

“I’m sorry Dave, I’m afraid I can’t do that.” That line, spoken by HAL 9000 in2001: A Space Odyssey, offered a 1960s glimpse of what we imagined machine intelligence would be like in the future – the future in this case being 15 years ago! But while we may not have flying cars or robots that control us, machine intelligence is in fact here. From optimizing an Uber driver’s route (and maximizing revenue) to recognizing faces in photographs, software is taking on the responsibility for complex processes that require “learning” of new information from multiple data sources and past outcomes, often needing to perform this learning in real-time.

Until now, advanced machine intelligence and deep learning techniques were largely limited to the realm of scientists with advanced degrees and enterprises with vast resources. With the availability of platforms such as Watson (IBM),Azure ML (Microsoft), TensorFlow (Google), and various open-source options, these techniques are now becoming available to every enterprise. Of course, enterprises will still need to model their problems, come up with the right training scenarios for the algorithms, and train their workforce in using these tools. However, harnessing the power of advanced learning algorithms at scale has become incredibly accessible with these platforms. 2016 will see major adoption of machine learning algorithms and platforms in driving operational efficiencies, predicting failures, customizing user experience and in discovering new revenue channels.

2015 was a breakthrough year in terms of machine intelligence advances both in terms of democratizing it through the aforementioned cloud services and strides made in vertical solutions towards machine autonomy. In 2016 we expect to see a much stronger impetus towards unsupervised and reinforcement learning techniques to build on that.

Watch out in many more places for automated customer service chatbots that can understand human questions and provide the right answer. Autonomous vehicles are one of the biggest success stories of this technology domain, and we will see early adoption in the industrial applications for these in 2016. One of the biggest impacts deep learning technologies will have is in the cybersecurity domain. It is the next big hope to be able to detect previously unseen malware or stealthy and persistent threats by connecting the dots on seemingly unconnected network or system events and user behaviors. Machine intelligence will also power major advances in genomics, personalized medicine, and drug discovery.

Trend 7: Blockchain

Blockchain, the underlying technology that powers Bitcoin, has been independently recognized for its broader potential for some time now. Blockchain is a distributed, transparent, and auditable ledger that does not depend on any central trusted entity but instead establishes trust, immutability, and integrity through cryptographically powered distributed consensus models.

Blockchain offers a distributed trust and/or consensus model that eliminates dependencies on central entities. Multiple players have adopted and extended the blockchain platform for uses beyond Bitcoin. Key constraints that plagued Bitcoin – such as anonymity, proof of work (mining) overheads, and permission-less participation – are eliminated by some of these platforms. These emerging platforms are geared for more nuanced use cases, including: support for a closed, permissioned ecosystem; trusted special nodes; and alternative consensus models that do not require mining as a proof of work.

The second half of 2015 saw some breakout platforms and alliances emerge to take this forward in a wide range of domains including finance, IoT, and supply chain.  Examples include Ripple (international payments), Counterparty (financial contracts), and Ethereum (smart contracts). We will see a sharp rise in companies building domain-specific solutions using blockchain in 2016, and some of the early players will cement their position in the various domains. In late 2015, the Linux Foundation partnered with a large group of technology (IBM, Intel, etc.) and financial services (J. P. Morgan, Wells Fargo, etc.) players to build an enterprise-grade blockchain that will be open source. This is a very positive development towards establishing a large-scale developer community as well as enterprise adoption in the future. We will see early results of that in 2016.

In 2016 we expect to see the emerging platforms mature and sign on early adopters. Processes that requires central trusted third parties for trust and clearance and that want to eliminate cross border delays in processing or multiple intermediaries will be redesigned using blockchain technology. We expect to see significant disruptive innovation, especially but not limited to the Financial, Government, and Commerce domains.

Trend 8: Containers

I’ve looked at clouds from both sides now,
from up and down, and still somehow
it’s cloud illusions I recall.
I really don’t know clouds at all.

Joni Mitchell, Both Sides Now

For the past decade, “cloud” has been part of every organization’s technology road map. It’s been great to include in conversations, and we finally seem to be tapping into the full capabilities of what the cloud can do.

In today’s cloud infrastructures, a “container” is a packaged runtime environment for an application along with its dependencies and configuration files. Containers enable us to move applications from one environment to another without breaking, and in turn allow greater agility between the development and operations teams. The past two years have seen an incredible growth in the container ecosystem, from core technology innovation to rich suites of tools for orchestration, monitoring, and management. Such advances will be a key driver for widespread enterprise adoption of containers in 2016.

While there has been a great interest in containers among enterprises, and many tests of the technology, their actual adoption in production environments has been quite low. Security concerns have been the biggest reason for this reluctance. Several recent developments have addressed those issues, leading us to predict a much higher adoption rate for containers in 2016.

Since multiple containers run atop the same OS (either directly or inside a VM) at user level, the isolation between applications is not sufficient for creating a secure multi-tenant environment. Support for user namespaces in Docker and other containers has alleviated this issue substantially. Trustworthiness of images has been another concern, which should go away with systems like CoreOS introducing signed images that can be verified before execution.

Another major concern is that IT security has no visibility inside the container itself. New startups like TwistLock are addressing that problem and also enabling the enterprise IT security team to extend their security and access control postures to the containers and provide the much needed audit trails.

Another reason creating a drag on operations is that persistent data storage has to be external to the container, which in turns implies that it is disconnected during migration. New tools like Flocker have come up to address this and enable stateful services inside containers.

Enterprises need to start evaluating and testing containers now, as adopting this technology will require a significant change in the skills, operations, and workflows that are historically slow to change due to enterprise complexity.  Containers and the ecosystem around them should be considered as a catalyst for change in the enterprise, to improve processes, reduce infrastructure complexity, and speed up innovation.

Conclusion

So there you have it: eight technology trends broken out across three posts that we think are going to make the biggest impact in 2016. Every enterprise, regardless of scale or domain, should look at leveraging one or more of these in driving its digital transformation strategy. The best way to proceed is pick the biggest opportunities you see to apply digital transformation, and then ask the kind of questions we have included here to determine which digital technologies will help you address those opportunities. Then start from the end-user perspective and solve for creating the ideal digital experience for them. Innovate in small intervals, get feedback from end users, and repeat. These are exciting times for innovation, and we hope this document guides your enterprise digital transformation journey in 2016 and beyond.

Image Credits: furiouscinema.com

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