Its 7 am of Feb 27th, 2029, Richard just woke up and said good morning google, and here is the response.
“Hey, Richard, a very good morning. Its 7 am, you slept for 8 hours and 12 minutes with 6 hours 20 mins of deep sleep. Your health records indicate you will have a healthy morning. You saw 3 dreams which are ready for you to review. What would you like to eat at breakfast? Based on today’s health report and weekly breakfast schedule, I suggest you theses 3 options”. And so on.
This is how a future day will look like. The impact of improvements in artificial intelligence, growing popularity of IoT and new tool & technology to create/consume data will change the way we see the world today. It will serve to every common person in their day to day life for decision making based on real-time data and analytics. Now, think about the big players and organization in this industry. This industry leader will rely heavily on augmented analytics, deep learning, RAP, and prescriptive analytics to leverage the Machine Learning/AI techniques to transform how analytics content is developed, consumed and shared.
Augment Analytics is the third wave of disruption in the data and analytics market. Similar to the second wave of self-service BI disrupting the first wave of traditional BI; augmented analytics uses machine-learning automation to supplement human intelligence and contextual awareness across the entire analytics life-cycle. By 2025, Augment Analytics will automatically prepare and cleanse data, perform feature engineering, find key insights and hidden patterns. Automation expedites investigation across millions of variable combinations that would be too time-consuming for a human to do manually. Often new discoveries are exposed in the process. Furthermore, artificial intelligence algorithms interpret results and present unbiased alternatives along with actionable recommendations. (Underwood, 2017)
Deep Learning is a class of machine learning which rely on large data to imitate functions of the human brain, aiming to solve the complex problem the way humans do. “These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics” LeCun, Hilton, &Bengio, 2015). With deep learning technology, Decision making will become more insightful and accurate by 2025. The organization has to spend less time on a data feed, aggregation, and review while more time can be spent on processing, analyzing, and acting upon the data. Deep learning has already revolutionized the fields of computer vision, robotics, gaming, and natural language processing. It is rapidly making strides in genomics, medical diagnosis, and computational chemistry. (Xia et al., 2018)
Robotic Process Automation: The concept of RPA is that the software observe different functions of computer system and find repeated function in business process. This intelligent software doesn’t require system analyst to define automated process, the ML systems observe what people are regularly doing and then the systems can automate the tasks. With the advancement in Artificial Intelligence, RPA is only going to get better. “By using RPA to eliminate manual and highly error-prone tasks from the human “to do” list, Organizations have the opportunity to improve efficiency and increase accuracy at a lower cost while still freeing professionals to focus on the activities that humans do best: strategy, analysis, and decision making” (TUCKER, 2017)
Prescriptive Analytics: We are entering the decade where many of the tasks will be replaced by machines and humans watch from the sidelines. That time is not really far. In fact, it’s already here. It’s called prescriptive analytics. The prescriptive analysis uses data from the past to identify trends and make guesses about the future. Prescriptive analytics take predictive tools a step further by recommending actionable steps for business users based on insights. Apart from providing information, prescriptive analytics will also tell you what to do with that information. Such kind of analytics will be highly used in the next decade when a small, big organization which consumes unstructured data and need to analyze texts, images, and videos. ("Future of Business Intelligence," n.d.).
Throughout history, humans have both shaped and adapted to new technologies. The capabilities of the decision support system and Business intelligence will continue to evolve with continuous technology advancement. As per current assumptions, we will see the effect of third wave of disruption in next 5-10 years in the world of analytics. The goal of the decision support system has always been to create value for the business. We will see the modern advances in BI as we continue to progress through the era of big data, deep learning. The future of business intelligence is likely to be much more automated and aggressively utilized, with fewer bottlenecks in terms of interface limitations and the free flow of data. Future BI trends are all part of a quickly evolving model that is essential to the progression of modern businesses. (Conard, A.)