IOT Trends for 2018

Published on Jan 22, 2018

Updated on Oct 16, 2019

We spoke with a number of IT leaders and industry experts about what to expect from IoT in the coming year and what could be the latest trends for IOT which will dominate 2018.

Following are the Internet of things trends to watch out for in 2018.

1.The IOT industry will bring a changed awareness around security and risk:

Security concerns will be high on the list. We have reached a point in the evolution of IoT when we need to re-think the types of security we are putting in place. Have we truly addressed the unique security challenges of IoT, or have we just patched existing security models into IoT with the hope that it is sufficient?

IOT presents a different kind of risk. Businesses need to understand that sensors and machine-to-machine communications are also stored in the cloud. In particular, facilities implementing devices connected to the IoT need to think about communication and the security protocols between devices: sensor-to-sensor communication, sensor-to-gateway communication, and updating and maintaining all on-premise equipment to better secure their data.

Tom Smith is a research analyst for DZone.com and he queried these IT professionals to get their insights on predictions for 2018. Here’s what IOT experts shared their thoughts on IoT trends for 2018.

IoT security will continue to dominate as a major concern, and I would expect the rise of several IoT-driven platforms to rise to the surface in an attempt to address and manage this. Says Lucas Vogel, Founder, Endpoint Systems

My hope is that there will be some adopted regulations around IoT security and compliance, otherwise, there will undoubtedly be more frequent and massive attacks. The fully-connected home will move closer to being a reality, and there will be unique solutions that address actual needs instead of just being “internet-connected”. Says Mike Kail, CTO, CYBRIC

2. Businesses will need to embrace the implementation of edge and cloud computing:

Edge computing, also known as fog computing, will continue to rise. The ability to run software at the edge is turning out to be one of the most promising accelerators of IoT adoption, given the cost savings and the ability to quickly achieve largescale systems.

3. Connectivity Management:

Another exciting new area involves the management of whole IoT systems or solutions. Device management and connectivity management has been around for several years already, but now that the pieces of IoT systems are coming together to form whole enterprise-scale solutions, management of these solutions has become higher up on the “tech wish list” for organizations.

4. IOT vs IIOT:

In addition, the separation between consumer IoT and Industrial IoT is becoming clearer all the time. One key distinction that is now apparent is that consumer IoT can often focus on greenfield installations but IIoT must enable brownfield installations. The investments in systems and equipment that were made by industrial firms over the last decades will continue to be in place and will need to be incorporated into IIoT solutions.

We’re seeing a trend towards a lot more IIoT use cases. As we move into 2018, we will see a much higher adoption of industrial IoT where sensors are making a big impact in the manufacturing, automotive, aerospace and engineering sectors. Other areas where we expect greater uptake of IoT systems include shipping, retail, agriculture, and healthcare. This expansion will trigger a need to hire many more IoT professionals and will likely see the rise of many new types of IoT specific roles within companies.

Many verticals still have business operations that involve manual observation of equipment status, inventory levels, and other key metrics. Where there is currently manual observation, there may be a great opportunity for a high-ROI project involving IoT. Some verticals that have a lot of manual observations are Oil & Gas, Energy Distribution, Supply Chain, and Telecommunications. The repeating theme is high-value infrastructure that is spread out geographically.

Thanks Kilton Hopkins, IoT Program Director forNortheastern University-Silicon Valley and the CEO of IOTRACKS, for providing your inputs to this article.

Let’s rewind the clock for a moment. Thousands of years ago, humans had a simple way of keeping tabs on things—literally. They carved marks into clay tablets to track grain harvests or seal trade agreements. These ancient scribes kickstarted what would later become one of humanity’s greatest pursuits: organizing and understanding data. The journey of data began to take shape.

Now, here’s the kicker—we’ve gone from storing the data on clay to storing the data on the cloud, but one age-old problem still nags at us: How healthy is that data? Can we trust it?

Think about it. Records from centuries ago survived and still make sense today because someone cared enough to store them and keep them in good shape. That’s essentially what data observability does for our modern world. It’s like having a health monitor for your data systems, ensuring they’re reliable, accurate, and ready for action. And here are the times when data observability actually had more than a few wins in the real world and this is how it works

How Data Observability Works

Data observability involves monitoring, analyzing, and ensuring the health of your data systems in real-time. Here’s how it functions:

Data Monitoring: Continuously tracks metrics like data volume, freshness, and schema consistency to spot anomalies early.
Automated data Alerts: Notify teams of irregularities, such as unexpected data spikes or pipeline failures, before they escalate.
Root Cause Analysis: Pinpoints the source of issues using lineage tracking, making problem-solving faster and more efficient.
Proactive Maintenance: Predicts potential failures by analyzing historical trends, helping enterprises stay ahead of disruptions.
Collaboration Tools: Bridges gaps between data engineering, analytics, and operations teams with a shared understanding of system health.

Real-World Wins with Data Observability

1. Preventing Retail Chaos

A global retailer was struggling with the complexities of scaling data operations across diverse regions, Faced with a vast and complex system, manual oversight became unsustainable. Rakuten provided data observability solutions by leveraging real-time monitoring and integrating ITSM solutions with a unified data health dashboard, the retailer was able to prevent costly downtime and ensure seamless data operations. The result? Enhanced data lineage tracking and reduced operational overhead.

2. Fixing Silent Pipeline Failures

Monte Carlo’s data observability solutions have saved organizations from silent data pipeline failures. For example, a Salesforce password expiry caused updates to stop in the salesforce_accounts_created table. Monte Carlo flagged the issue, allowing the team to resolve it before it caught the executive attention. Similarly, an authorization issue with Google Ads integrations was detected and fixed, avoiding significant data loss.

3. Forbes Optimizes Performance

To ensure its website performs optimally, Forbes turned to Datadog for data observability. Previously, siloed data and limited access slowed down troubleshooting. With Datadog, Forbes unified observability across teams, reducing homepage load times by 37% and maintaining operational efficiency during high-traffic events like Black Friday.

4. Lenovo Maintains Uptime

Lenovo leveraged observability, provided by Splunk, to monitor its infrastructure during critical periods. Despite a 300% increase in web traffic on Black Friday, Lenovo maintained 100% uptime and reduced mean time to resolution (MTTR) by 83%, ensuring a flawless user experience.

Why Every Enterprise Needs Data Observability Today

1. Prevent Costly Downtime

Data downtime can cost enterprises up to $9,000 per minute. Imagine a retail giant facing data pipeline failures during peak sales—inventory mismatches lead to missed opportunities and unhappy customers. Data observability proactively detects anomalies, like sudden drops in data volume, preventing disruptions before they escalate.

2. Boost Confidence in Data

Poor data quality costs the U.S. economy $3.1 trillion annually. For enterprises, accurate, observable data ensures reliable decision-making and better AI outcomes. For instance, an insurance company can avoid processing errors by identifying schema changes or inconsistencies in real-time.

3. Enhance Collaboration

When data pipelines fail, teams often waste hours diagnosing issues. Data observability simplifies this by providing clear insights into pipeline health, enabling seamless collaboration across data engineering, data analytics, and data operations teams. This reduces finger-pointing and accelerates problem-solving.

4. Stay Agile Amid Complexity

As enterprises scale, data sources multiply, making Data pipeline monitoring and data pipeline management more complex. Data observability acts as a compass, pinpointing where and why issues occur, allowing organizations to adapt quickly without compromising operational efficiency.

The Bigger Picture:

Are you relying on broken roads in your data metropolis, or are you ready to embrace a system that keeps your operations smooth and your outcomes predictable?

Just as humanity evolved from carving records on clay tablets to storing data in the cloud, the way we manage and interpret data must evolve too. Data observability is not just a tool for keeping your data clean; it’s a strategic necessity to future-proof your business in a world where insights are the cornerstone of success.

At Mantra Labs, we understand this deeply. With our partnership with Rakuten, we empower enterprises with advanced data observability solutions tailored to their unique challenges. Let us help you turn your data into an invaluable asset that ensures smooth operations and drives impactful outcomes.

IOT Trends for 2018

1.The IOT industry will bring a changed awareness around security and risk: