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Top 6 Technology Trends to Watch in 2018

Sridhar Iyengar

Making predictions is always a gamble. But given the way 2017 played out and the way 2018 is shaping up, odds are that the technology trends discussed below will play a significant role in your IT department this year.

Growing use of artificial intelligence, machine learning with data analytics, and business intelligence

Business applications continue to churn out large volumes of data, and users are trying to mine that data to determine patterns and predict user behavior. In ecommerce, users want to know customers' buying patterns, which will help market products better. Website designers want to understand how visitors move through their sites in order to improve conversion rates. And companies want to analyze their sales data to correlate marketing dollars spent with sales dollars generated.

Business intelligence and data analytics activities are becoming easier to perform, and that's driving their adoption in mainstream businesses that are seeking to make better, faster decisions.

Rise of AI-powered chatbots in customer service and support

Over the past few years, chatbots — the automated, human-like chat responders — have been more of an experiment, with limited adoption. Now, chatbots are becoming more mainstream as people see the benefits of those experiments, especially in customer service and support.

AI-powered chatbots are learning how to respond to customers and predict what they want

Unlike human customer service and support reps, chatbots don't have the physical and mental inconsistencies that can degrade service levels. Moreover, AI-powered chatbots are learning how to respond to customers and predict what they want. Based on customer history or questions customers ask during a chat session, AI-powered chatbots can ask users what they need and even ask leading questions, all to improve the support experience.

Use of natural language processing as a new form of human-computer interface

"Star Trek" fans aren't the only ones who've been waiting for this prediction to manifest. Business users, too, are eager to have computers understand natural language.

Take a sales manager who wants to generate a quarterly report. If the manager has to ask for it from an analytics specialist, the manager has to explain what she's looking for and hope the specialist accurately translates her request into something the computer can process in order to generate the information she wants. Natural language processing bypasses the analytics specialist and lets the manager work with a computer directly via speech. In response, the computer may generate a visual or auditory response, depending on the manager's preference.

Tightening of data protection laws

Everything is heading toward digitization. Every business process, every technology, everything done with information — from storing, transmitting and processing it — it's all in digital form. Now, a lot of countries are recognizing that their citizens' personal data needs to be protected.

In addition, they're recognizing that users have to opt-in to these digital relationships, and they have to know the reason their personal data is being provided to a data process or data consumer and know what the consumer will do with their data.

Tighter data protection laws are designed to secure their citizens' privacy as well as prevent data abuse and outright criminal activity such as fraud or theft. The most recent example of this is the European Union's General Data Protection Regulation (GDPR). While some countries like India are also coming up with data protection frameworks, others will enhance their existing framework.

Continuation of cloud adoption in mid-sized and larger enterprises

Cloud is a mindset. Governments and larger enterprises have been slower to adopt that mindset, preferring a private cloud/private data center strategy as a starting point. Now, the biggest barriers to their cloud adoption — security and data privacy risks — are well understood and processes and mechanisms have been put in place to mitigate them. Enterprises now also recognize that most cloud companies invest heavily in the security of their cloud infrastructure, platforms and cloud applications. And they recognize that, in most cases, the security teams of the cloud companies are much larger and much more experienced than their own.

Overall, the larger enterprises are finally becoming comfortable and confident with cloud security and the cloud itself. Governments are also taking the steps to put citizen-facing, non-sensitive data and applications on the cloud.

Use of blockchain in enterprise security for identity management

Blockchain provides a distributed, secure and unique system of records, so you can have a strongly encrypted authentication mechanism that prevents malicious users from breaking in. This makes it a great choice in terms of enterprise security, especially for an identity access management system, which manages user logins and authentication.

In 2018, we'll likely start seeing blockchain adoption in areas such as banking, financial services and healthcare.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

Top 6 Technology Trends to Watch in 2018

Sridhar Iyengar

Making predictions is always a gamble. But given the way 2017 played out and the way 2018 is shaping up, odds are that the technology trends discussed below will play a significant role in your IT department this year.

Growing use of artificial intelligence, machine learning with data analytics, and business intelligence

Business applications continue to churn out large volumes of data, and users are trying to mine that data to determine patterns and predict user behavior. In ecommerce, users want to know customers' buying patterns, which will help market products better. Website designers want to understand how visitors move through their sites in order to improve conversion rates. And companies want to analyze their sales data to correlate marketing dollars spent with sales dollars generated.

Business intelligence and data analytics activities are becoming easier to perform, and that's driving their adoption in mainstream businesses that are seeking to make better, faster decisions.

Rise of AI-powered chatbots in customer service and support

Over the past few years, chatbots — the automated, human-like chat responders — have been more of an experiment, with limited adoption. Now, chatbots are becoming more mainstream as people see the benefits of those experiments, especially in customer service and support.

AI-powered chatbots are learning how to respond to customers and predict what they want

Unlike human customer service and support reps, chatbots don't have the physical and mental inconsistencies that can degrade service levels. Moreover, AI-powered chatbots are learning how to respond to customers and predict what they want. Based on customer history or questions customers ask during a chat session, AI-powered chatbots can ask users what they need and even ask leading questions, all to improve the support experience.

Use of natural language processing as a new form of human-computer interface

"Star Trek" fans aren't the only ones who've been waiting for this prediction to manifest. Business users, too, are eager to have computers understand natural language.

Take a sales manager who wants to generate a quarterly report. If the manager has to ask for it from an analytics specialist, the manager has to explain what she's looking for and hope the specialist accurately translates her request into something the computer can process in order to generate the information she wants. Natural language processing bypasses the analytics specialist and lets the manager work with a computer directly via speech. In response, the computer may generate a visual or auditory response, depending on the manager's preference.

Tightening of data protection laws

Everything is heading toward digitization. Every business process, every technology, everything done with information — from storing, transmitting and processing it — it's all in digital form. Now, a lot of countries are recognizing that their citizens' personal data needs to be protected.

In addition, they're recognizing that users have to opt-in to these digital relationships, and they have to know the reason their personal data is being provided to a data process or data consumer and know what the consumer will do with their data.

Tighter data protection laws are designed to secure their citizens' privacy as well as prevent data abuse and outright criminal activity such as fraud or theft. The most recent example of this is the European Union's General Data Protection Regulation (GDPR). While some countries like India are also coming up with data protection frameworks, others will enhance their existing framework.

Continuation of cloud adoption in mid-sized and larger enterprises

Cloud is a mindset. Governments and larger enterprises have been slower to adopt that mindset, preferring a private cloud/private data center strategy as a starting point. Now, the biggest barriers to their cloud adoption — security and data privacy risks — are well understood and processes and mechanisms have been put in place to mitigate them. Enterprises now also recognize that most cloud companies invest heavily in the security of their cloud infrastructure, platforms and cloud applications. And they recognize that, in most cases, the security teams of the cloud companies are much larger and much more experienced than their own.

Overall, the larger enterprises are finally becoming comfortable and confident with cloud security and the cloud itself. Governments are also taking the steps to put citizen-facing, non-sensitive data and applications on the cloud.

Use of blockchain in enterprise security for identity management

Blockchain provides a distributed, secure and unique system of records, so you can have a strongly encrypted authentication mechanism that prevents malicious users from breaking in. This makes it a great choice in terms of enterprise security, especially for an identity access management system, which manages user logins and authentication.

In 2018, we'll likely start seeing blockchain adoption in areas such as banking, financial services and healthcare.

Hot Topics

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...