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Five predictions for the internet of things in 2017

The internet of things continues to prove its relevance in organizations’ digital transformation strategy. Companies big and small invested in it in 2016 and are realizing opportunities in extending IoT uses out into the field to enhance customer relationships and driving business growth. Throughout 2016, technology providers of all sizes started to truly realize that alignment is vital to relieve fragmentation and that they don’t need to “own” every piece of an IoT solution to add significant value and succeed.

2017 will continue to bring more complexity to the market, but companies will start to find their uniqueness, allowing us to focus on the real problems at hand together.

Below are five predictions for what 2017 holds for IoT.

1. Measuring business impact and security fears will be the greatest inhibitors of IoT projects and solutions

There’s no doubt that IoT has huge potential for business impact, but end users need to get comfortable with the anticipated ROI in order to move beyond maker projects and proofs-of-concepts to real investment. Without understanding business value and potential ROI, IoT adoption will stifle and slow. There’s a bit of a catch-22 in these early days because companies that have successfully deployed an IoT solution typically don’t want to share metrics of their success with competitors. We’re seeing progress on this front with more and more companies willing to document measurable benefits in case studies, but it will take time for this to become the norm.

Once customers are confident enough in the business value and how to calculate it, the second biggest inhibitor becomes security fears. 2016 brought the largest DDoS attack ever delivered by a botnet made up of IoT devices, which shut down 1,600 websites in the U.S. A month later, a major attack on Dyn led to a massive internet outage across the U.S and parts of Western Europe. According to analysts and industry experts, this is just the beginning. Most of the hacks to date have been conducted through consumer devices that had limited to no security measures applied — products that promoted ease of use and instant gratification over security. That said, as the business value and associated attack surface grows for IoT so will the interest of attackers.

Throughout 2017, hackers will continue to exploit IoT device vulnerabilities to launch broad-scale attacks. To help fight this battle, the fractured IoT market needs to come together to develop security measures that render devices less vulnerable by default and promote best practices in deployment while also recognizing the importance of retaining usability. After all, for IoT solutions to be successful the business value needs to be far greater than the complexities involved with deploying and maintaining them.

2. Consolidation of IoT platforms through broad-scale collaboration

It’s no secret that the IoT market is fragmented. Currently, there are over 400 platforms, which is confusing to customers and slows down the process of research and development. The market needs to consolidate so that it’s easier for customers to leverage preferred technology and they don’t feel like the rug might get pulled out from under them with a failed platform that built its entire data integration foundation in a silo. We have seen a distinct trend in 2016 that companies have gotten it out of their system that they must “own” everything to be successful. In fact, many have realized that it’s simply not possible to cover all facets of an IoT solution well. In order for the industry to scale, we need to come together on a more common foundation so we can focus our differentiation where it matters in areas such as analytics, advanced security, vertical expertise and services.

A key factor that will accelerate interoperability efforts is open source collaboration. Open source platforms are increasingly being used in industrial, smart cities and utility industry projects. I think historically conservative industries will continue to get more comfortable with open source in 2017. We will see a leading open source platform project emerge that provides a center of gravity for data integration. This will mitigate between the fragmentation in connectivity standards. As more open source tools are developed and mature, they’ll become a vital part of the research and development process, too.

3. Increased focus on use cases within verticals

A lot of the IoT hype comes from the consumer segments — connected baby monitors, refrigerators and toilets. We have been focused on commercial and industrial sectors from the broader markets have started to realize that’s where the real ROI is. In 2017, the market will have deep conversations on use case development within industrial verticals. By creating and sharing blueprints and solution architectures for these use cases, we’ll learn from each other and make more progress quicker. The consolidation I mention above will get us closer to the utopia of an open and flexible horizontal IoT platform to which specialized tools and vertical domain knowledge can be applied to address targeted use cases. Meanwhile, proprietary platform and service providers focused on highly specific use cases will see traction and jacks-of-all-trades will be masters of none.

4. Vendors will focus on certifications to help advance IoT growth

Becoming a certified expert in one of the countless IoT platforms doesn’t mean a whole lot. However, as the industry works towards a de facto standard for data integration (and leaders that apply this foundation with their own differentiation emerge), the stage will be set for industry-specific certifications to take hold in an effort to keep the bar high and ensure that certifications hold weight. These certifications will help prepare top talent with the required IoT skill sets and will cover industry-standard tools, specific platforms that apply them and domain expertise in security, analytics or specific industries and use cases.

As a result, large companies and innovative start-ups will begin to invest heavily in low- or no-cost training certifications. This trend is already being seen in IBM’s Watson IoT Academy and PTC University’s ThingWorx Certification — and enterprises should be ready to keep track of what their IoT vendors are doing in the area of certifications.

5. Artificial intelligence will increasingly be used to mine the data coming from IoT devices

As IoT gets distributed across the edge and cloud, the insights will be boosted by the use of AI deployed via containers. AI has already been making a mark through aiding real-time decision-making. In AI’s future, developing more natural language capabilities will help to further realize the potential of a connected IoT world, as natural language-based data descriptions will provide a universal way to understand data among various types of devices. This approach will not only break down silos, but also allow people to communicate with IoT directly through voice or text.

This will come with challenges, especially culturally in the workforce. In countries where traditional industries dominate, such as manufacturing in Brazil, our Future Workforce Study found that 41% of workers said they worried a robot might take their job. Some companies are using the opportunity to retrain employees and teach them new skill sets. For example, data scientists will start training machines to go beyond reviewing large data pools for insights and answers. This will help machines to develop the knowledge to read between layers of data. AI will be able to interpret data differently, break it down more succinctly and identify and share nuances otherwise overlooked.

Do you agree with these? What other predictions do you have for IoT in the New Year?

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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Well! thanks for interesting post. Now almost any asset can be turned into a data stream. With pervasive cloud-connectivity, one can also move past the limitations of "batch" or "offline" applications, and the store-extract-report-analyze mindset. Instead, we can soon expect to see data turned into analysis in near real-time.
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