Data and Trending Technologies: Data’s Role

COL04x - image - EDI am excited to begin my role as a regular contributor to The focus of my new column will be the role of data in trending technologies. I am calling the quarterly column ‘Data and Trending Technologies’.

In this first column of the series, I’d like to take a look at key technology predictions for 2017 from the big guys like Gartner, Forrester, Forbes, etc., their mid-year assessments, and how data played a role in these technologies. As you might have seen, at the end of last year and beginning of this year, many analysts and publications came up with their 2017 technology predictions. I looked at some key publications and tabulated them below for easy reference. Each row is a technology category, each column is a key publication, and the cells reflect some key observations made by the author.


Tech / Publication Gartner Forrester Forbes Inc.Com Deloitte
IOT related Intelligent Things IoT Mobile will continue to rise as the primary shopping platform Inevitable architecture Large scale IoT projects slow but small projects explode; Smart home products consolidate
AI / Machine Learning related Applied AI and Advanced Machine Learning AI Smart devices will actually become smart Funding will skew towards Artificial Intelligence (AI), Machine learning, Natural Language Processing (NLP) Machine Intelligence AI based bots move to the mainstream
Blockchain related Blockchains and distributed ledgers Blockchain technology will start to show usefulness Construction industry will tap into the IoT industry Blockchain – Trust economy
AR / VR / Mixed Reality related Virtual and Augmented Reality Augmented and Virtual reality We’ll get used to the idea of being in two places at the same time Shift to social is essential to succeeding in the online marketplace Mixed Reality Non-gaming applications for AR/VR grow faster than games
Cloud Computing related Mesh App and Service architecture Cloud Computing Spending on software goes up and spending on hardware goes down
Data Analytics related Digital Twins Unstructured data will start to give up its secrets Business intelligence will be democratized Dark analytics
Miscellaneous Adaptive security architecture More organizations will discover their digital twin More about autonomous connecting to network Everything As a Service Autonomous driving slows but assisted driving soars


As you can see, there is more of a convergence than divergence in these predictions. If I were into conspiracy, I would say they probably colluded in coming up with these predictions. All kidding aside and taking these technology predictions at their face value, I plan to analyze these technology trends from a data perspective, and how much of a role data can play in their adoption. I’ll be looking at these technologies along the following dimensions:

  • Impact on corporations versus consumers
  • Adoption versus hype
  • Specific use cases
  • Accelerators and inhibitors influencing adoption

In this first article, I’d like us to get grounded on five of these technologies, what they entail, and a mid-year assessment.

Internet Of Things

Even though much of IoT news focuses on consumer applications like wearable technologies, Mckinsey reminds us that 70% of IoT’s value will be in business-to-business applications, reaching nearly $5 trillion in value over the next 10 years in manufacturing, agriculture, and healthcare environments. Data of course plays a crucial role in IoT, as sensor and usage data can help with predictive analytics in a major way. As an example, McKinsey notes in this article that only 1% of data from an oilrig’s 30,000 sensors is actually used – and not for optimization or prediction. One can only imagine the significant impact data can play as the percentage of usage increases.

Pitchbook states that venture capital investments in IoT have been steadily growing. See the image below.


Artificial Intelligence / Machine Learning

AI and machine learning has taken 2017 by storm. With investments from big guys like Google and Facebook and tweets / posts from visionaries like Elon Musk and Mark Zuckerberg, AI is here to stay. The image below about VC investments in AI is just one example. Even though AI technologies have been in existence for many decades, big data analytics is enabling and accelerating AI adoption. This MIT Sloan review article goes on to say that “the ability to access large volumes of data with agility and ready access is leading to a rapid evolution in the application of AI and machine-learning applications.”


Blockchain Technologies

In my opinion, blockchain technologies have sneaked upon us suddenly without much notice. As per this HBR article, blockchain technologies enable a digital transformation of these age-old entities like contracts, transactions, and their records. The same article summarizes blockchain in a neat way. “The technology at the heart of bitcoin and other virtual currencies, blockchain is an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way. The ledger itself can also be programmed to trigger transactions automatically.” Given that blockchain is all about enabling digital transformation in contracts, transactions, and their ilk, data plays a crucial role in enabling blockchain. Financial services and Insurance companies are at the forefront of adopting this technology.

Augmented Reality / Virtual reality / Mixed Reality

This is probably one area where consumer adoption outpaces corporate adoption. Of these, AR likely has more corporate impact than the others. In this article citing Zion Market Research, AR is expected to grow to $134 billion by 2021.  The expected segments for adoption are: Aerospace & Defense, Industrial, E-Commerce, and Retail. I am not sure about the impact of data in this particular technology. I will dig more into it.

Cloud Computing

Forbes did a roundup of cloud computing forecasts recently in this column, finding that 74% of Tech CFOs say cloud computing will have the most measurable impact on their business in 2017. Another statistic states that enterprise cloud computing spending is growing at a 16% CAGR between 2016 and 2026.


We know that cloud computing involves data in a significant way. But one impediment is that enterprises are reluctant to move data to a public cloud as it is seen a corporate asset and are afraid to lose control over it. We’ll see how the technology evolves to accommodate this concern.


Now that we have set a foundation for this column, I’ll dig more into each of these technologies and how data plays a key role in their adoption going forward.



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Ramesh Dontha

Ramesh Dontha

Ramesh Dontha is Managing Partner at Digital Transformation Pro (www.DigitalTransformationPro.Com), a management consulting company focusing on Data Strategy, Data Governance, Data Quality and related Data management practices. For more than 15 years, Ramesh has put together successful strategies and implementation plans to meet/exceed business objectives and deliver business value . His personal passion is to demystify the intricacies of data governance and data management and make them applicable to business strategies and objectives. Ramesh can either be reached on LinkedIn or via email: rkdontha AT

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