runner's hurdle
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There is no getting away from the Internet of Things (IoT), in fact, businesses that are looking for a competitive edge are rushing to invest. However, not everyone is convinced, ongoing discussions about IoT security and data management have some enterprises dragging their heels. Meanwhile, new research from Microsoft indicates that within 2 years 30% of leading organization’s revenues will be the result of IoT adoption.

Holding Back on IoT

The findings are contained in the report called IoT Signals, which surveyed over 3,000 IoT decision makers. Leaving aside the findings that are directly related to IoT deployments, the research shows, that, like many other cutting edge technologies such as augmented artificial intelligence (AI), advanced analytics and blockchain, there is a large skills gap that could compromise competitiveness if it is not addressed rapidly. The underlying message appears to be that if your organization hasn’t invested by now, it really should. Some of the other findings are worth a look too. The report indicates that:

  • 97% of IoT adopters are still concerned about security.
  • 38% said technical challenges are holding them back.
  • 47% said lack of skilled workers compounds everything.

The result is that nearly one-third (30%) of projects fail before even getting off the ground.

Related Article: 6 Security Issues That Will Dominate IoT in 2019

IoT Hurdles

Given how widespread IoT devices are already, this is a major problem as effective IoT management is as important as a deployment strategy. According to the recently released, Worldwide Global DataSphere IoT Device and Data Forecast, 2019-2023 (subscription required) from independent research company IDC, the number of devices connected to the internet, including the machines, sensors and cameras that make up the IoT, continues to grow at a steady pace.

It is predicted that there will be 41.6 billion connected IoT devices, or "things," generating 79.4 zettabytes (ZB) of data in 2025. (To put this in perspective, in 2009, the entire World Wide Web was estimated to contain close to 500 exabytes, or half a zettabyte.

Add into the mix the fact that as the number of connected IoT devices grows, the amount of data generated by these devices will also grow. Some of this data is small and bursty, indicating a single metric of a machine's health, while large amounts of data can be generated by video surveillance cameras using computer vision to analyze crowds of people, for example. There is an obvious direct relationship between all the "things" and the data these things create.

Data management is, as a result, also becoming a problem. A Deloitte Insights report from the end of last year explained: 

“Since nearly every major company is actively looking for data science talent, the demand has rapidly outpaced the supply of people with required skills. (Based on current demand and supply dynamics, the United States alone is projected to face a shortfall of some 250,000 data scientists by 2024.)

The skills gap and longer hiring times can cause project delays and higher costs, hindering enterprises’ data analytics efforts. But a number of recent trends may change how companies acquire and apply data science capabilities, presenting savvy companies with some options for alleviating the talent bottleneck.”

Related Article:  7 Big Problems with the Internet of Things

Solving IoT Problems

There are other solutions to the problem too and most of them can be found in the cloud. Tendü Yoğurtçu, CTO at Syncsort, pointed out that as enterprises are looking to centralize multiple disparate data assets into one repository for analytics at scale (building the data lake), more and more are turning to a hybrid cloud environment, primarily because of the cloud's cost savings, ease of setup and ease of integration with future technologies.

Enterprises can easily scale up and down compute power based on demand, and there is no hardware or software to install, configure, upgrade or manage. Therefore, they populate cloud data warehouses with data from mainframe, traditional data warehouses and IoT.

The challenge here is to prepare an artificial intelligence (AI) data pipeline with trusted data for analytics, as machine learning (ML) models need to be trained with data, but using bad data to train the models impacts the future recommendations and predictions those models produce.

But poor data quality is public enemy No. 1 to the widespread, profitable use of machine learning. The phrase garbage-in, garbage-out carries a special warning and bad data can have a multiplier effect with machine learning — first in the historical data used to train the predictive model and second in the new data used by that model to make future decisions. It's important to use a data quality product with a rich set of algorithms to perform both exact matching and fuzzy matching, which is key to the high-quality data delivered to the AI pipelines.

Security Concerns

The final hurdle is security and for that too there are solutions. Ana Bera is a cybersecurity expert and the co-founder of She said that while there are certainly some security issues related to IoT, it is important for businesses to start investing in smart technologies as early as now for more streamlined operations. “You see, security was also a primary concern when the internet itself was first starting to take over the world. However, the pros definitely outweighed the cons and in just a couple of years, cloud storage became the safest way to back up your data. I see the same thing happening with IoT,” she said.

“While data security is definitely something that we should all be concerned about, business owners have several options when it comes to strengthening the cybersecurity of their office network.”

Needless to say, investing in IoT also means shelling out funds to have the right cybersecurity framework in place and having a reliable IT team on board to regularly check if there are easy entry points for hackers within the network.