It’s time of the year again. As we welcome 2018, a lot of conversations are happening in the business world speculating about the technologies that will be trending high and will dominate in the coming year. There’s always something new in store for us every year.
As someone passionately involved in the AI, IoT, Data Analytics space, I am fortunate to be working with some of the brightest minds and forward-thinking companies around the world. Based on my conversations with them, here are some of the promising trends of data analytics which I think will shape the future of analytics in 2018 –
Augmented analytics will become popular
Augmented analytics uses machine learning and NLP to automate data preparation and present data in a simplified manner. The coming year will see Augmented Analytics being used widely to aid human intelligence to go beyond opinion and bias in order to bring better outcomes.
Augmented Analytics allows data scientists, citizen data scientists, and the IT community, in general, to focus on strategic issues and make better business decisions, and improve service offerings, pricing, and other aspects of the business.
AI and Machine learning methods will be more widely adopted in the industry
AI and Machine learning will be playing a major role in the coming years as these technologies are helping to simplify our lives and work processes. The explosive growth of big data has resulted in real-time approaches to addressing various business issues including customer experiences for most businesses. The close relationship between big data analytics and different forms of artificial intelligence, such as predictive analytics, machine learning and, deep learning will help organizations to drastically improve their customer experiences.
Another technique called AutoML is fast gaining popularity. Simply put, AutoML is a technique of developing machine learning by automating workflows using deep-learning and statistical techniques. This technique is being widely adopted in the industry today because it helps democratize AI tools by allowing business users to develop machine learning models without a deep programming background. It will also reduce the time that data scientists take to create models and without any programming. The coming years will see more AutoML packages being created for commercial usage and integration of AutoML within larger machine learning platforms.
The growing adoption of Big Data for meaningful outputs
The last few years have seen technological progress being made which has resulted in cheaper computers with more processing power than ever before. The price of storage devices has also dropped drastically. With companies like Amazon, Microsoft and many others offering storage, servers, and apps on the cloud at very reasonable rates, it is possible to process huge volumes of data from various sources to derive meaningful insights that will help businesses grow. Businesses can now use data to accurately predict the needs of their customers. This data could be made available in various formats; text, image, and video. Big Data is predicted to be the most powerful technology that gives answers to many consumer-centric questions that companies are trying to answer these days.
Edge Computing and Cloud will see an explosive growth
Edge computing is a distributed information technology (IT) architecture in which computer services are moved closer to the source of data or to the periphery of the network. Edge computing helps overcome connectivity and latency challenges as the distance traveled by the data is reduced. Edge computing has become popular due to an increase in the use of mobile computing, the decrease in cost of computer hardware, and a drastic increase in the use of IoT-enabled devices. Many of the leading companies, such as Cisco and HPE are looking forward to leveraging this technology for their benefit.
Many of the smart devices, such as smart drones, wearable technologies, and autonomous vehicles will benefit with the use of Edge Computing as they require real-time response and processing to work efficiently. Though there is a speculation that edge computing will eventually replace cloud computing, cloud computing finds its usefulness in many areas. For example, for centralized storage and big data analytics applications that are not as sensitive to a timeout response. I feel that in many cases, the two technologies could have a symbiotic relationship.
Predictive Analytics will be used for solving difficult problems
Even though predictive analytics is not new and is being used in the industry for quite some time, more and more organizations are now turning towards this technology to increase their bottom line and competitive advantage.
Owing to the availability of interactive software, many organizations and business analysts are using predictive analytics to precisely predict future behaviors to improve the organization’s profitability. Predictive analytics is finding major uses in the areas of fraud detection, reducing market risks, optimizing marketing campaigns, and improving business operations.
I feel that The year 2018 will see a convergence between big data and others technologies. There will be a big surge in the application of Machine learning in day-to-day life. Artificial Intelligence, IoT and cloud-first strategy for big data analytics will be more widely adopted and allow these smart technological capabilities to be incorporated in various enterprise solutions and services – this will allow people and organizations to leverage their benefits. I am excited to be a part of this fascinating evolution.