Top Disruptive Technologies for the Enterprise in 2020
By Suhale Kapoor, Executive Vice President & Co-Founder, Absolutdata
In an increasingly digitized world, disruption by way of technological innovation is inevitable. Given the rapid pace at which emerging technologies are coming to the fore, it is not only essential for organizations to stay up-to-date with the latest developments, but to carefully examine which of these are relevant for their particular business. Additionally, there is a need to prepare the workforce with training to upskill/reskill in order to meet the changes head-on with experienced personnel.Only when this is achieved can enterprises chart out an effective roadmap for their implementation in order to strategize better and make the most of new opportunities.
While there are a number of new and advanced technologies on the rise, including Big Data, IoT and Blockchain, Artificial Intelligence is proving to be the starting point that a majority of enterprises are aiming for today. There have been several use-cases of its successful application across industries, sectors and functions within an organization as well, with its capabilities being further developed over time.
As per research by IDC, worldwide data is expected to grow by 61% between now and 2025, touching 175 zettabytes within the same period. Consequently, the shift of AI into the mainstream is primarily attributable to the fact that it enables enterprises to derive meaningful, actionable insights from increasingly large and complex data in real-time.How well businesses enhance their strategies to achievetheir goals will be largely determined by their ability to adopt, implement and modify an amalgamation of various technologies, eventually working together in perfect sync.
Let us look into some of the top technologies that have been instrumental in transforming businesses so far, and are likely to continue to expand in their application to improve capabilities in 2020:
The increasing amount of big data that enterprises have to deal with today – from collection to analysis to interpretation – makes it nearly impossible to cover every conceivable permutation and combination manually. Augmented analytics is stepping in to ensure crucial insights aren’t missed, while also unearthing hidden patterns and removing human bias.Its widespread implementationwill allow valuable data to be more widely accessible not just for data and analytics experts, but for key decision-makers across business functions.
Natural language processing (NLP)
NLP isbecoming a necessary element for companies looking to improve their data analytics capabilities by enhancing visualized dashboards and reports within their BI systems. In several cases, it is facilitating interactions via Q&A/chat mediums to get real-time answers and useful visualizations in response to data-specific questions.It is predicted that natural-language generation and artificial intelligence will be standard features of 90% of advanced business intelligence platforms including those which are backed by cloud platforms. Its increasing useacross the market indicates that, by bringing in improved efficiency and insights, NLP will be instrumental in optimizing data exploration in the years to come.
Machine Learning experience
Gartner predicts that, by 2020, over 40% of all data science tasks will be automated. A significant proportion of this automation can be attributed to the increasing capabilities of machine learning technology. Its tools can augment the roles of even the most skilled analysts by instantly extracting hard-to-identify insights, thereby simultaneously improving an enterprise’s reaction-time to various events. In addition to general machine learning, it is crucial for today’s data scientists to have experience with automation technology like experimental analysis, data scaling and quantitative analysis for scalable business impact.
Increasing power of real-time analytics
The implementation of, and reliance on real-time analyticsis increasingly gaining momentum in marketing, particularly for customer interactions across multiple channels. This is enabling enterprises to effectively target new audiences, improve rates of retention, defect win-back and enhance engagement for first-time and active customers. As a larger number of marketers continue to look for real-time insights, we are likely to see AI-driven marketing activities increase manifold in 2020.
In 2020, it is expected that close to 90% of large organizations will be using DaaS to generate some form of revenue. Given that it is highly accessible, this technology also has the potential for seamless, real-time inter-department data sharing withinorganizations. Its growing implementation is highlighted through the emergence of a number of players such as map data providers and product catalog vendors. Companies working with data that others could utilize can monetize it by selling based on the nature and/or size of data.
Hyper-automation uses a combination of various ML, automation tools and packaged software to work simultaneously and in perfect sync. These include robotic process automation (RPA), intelligent business management software and AI, to take the automation of human roles and organizational processes to the next level. The key role of hyper-automation is essentially automation through replication. It requires a mix of devices to support this process to recreate exactly where the human employee is involved with a project, after which it can carry out the decision-making process independently.
Edge computing is essential to measuring the impact and scope of IoT innovations in the creation and development of smart spaces. It studies the landscape to ensure that key applications and services are within reach of those who are using them. Considering that the number of smart devices at the edge of the network may be over 20X more than in traditional IT jobs by 2023, edge computing is going to be instrumental in laying the foundation as well as for the consistent development of future IoT infrastructures.
Integration of AI with other emerging technologies
Incorporating AI with other technologies is what will facilitate enterprises to unleash its full potential for a positive impact.For instance, smart vehicles may not function optimally without AI and IoT working in sync. Here, AI models are what the vehicle’s decision-making is based on, while the sensors that collect the data in real-time are activated and monitored via IoT.Similarly, there are multiple use-cases where the integration of AI and other technologies can facilitate future innovation for improved accuracy and security, optimized processes, smarter devices and greater scalability.
Technology, today, is evolving more rapidly than ever before. Whether they are kick-starting their journey with emerging technologies, refining what they already have, or exploring them further, enterprises need to keep abreast of the latest industry trends in order to not just survive, but thrive. The key to unlocking their potential lies in identifying patterns relevant to their specific business context, which will be instrumental in helping them embrace new opportunities while optimizing what exists, thereby facilitating scalable impact for the long run.
(The author is Executive Vice President & Co-Founder, Absolutdata)