By Sridhar Iyengar, Vice President, ManageEngine
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 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. Unlike human customer service and support reps, chatbots don’t have the physical and mental inconsistencies that can degrade service levels. More, 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 towards 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. More, 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. Most recent example of this is Europe’s General Data Protection Regulation (GDPR). While some countries like India are also coming up with data protection frameworks, others will enhance their current data protection framework.
Continuation of cloud adoption in mid-sized & larger enterprises. Cloud is a mindset. And governments and larger enterprises have been slower to adopt that mindset, preferring to 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 putting out 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 identity access management system, which manages user logins and authentication. In 2018, we’ll like start seeing blockchain adoption in areas such as banking, financial services, and health care.