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.)
