Future impact of data science on business analytics

1. THE FUTURE IMPACT OF DATA SCIENCE
ON BUSINESS ANALYTICS

2. INTRODUCTION
The field of Data Science is in a transitional mode in terms of how the latest data
technologies are being used to solve business problems for a strategic advantage.
In the near future, Data Scientists will conduct their business very differently. As
Big Data, algorithm economics, IoT, and Cloud continue to become mainstream
across global enterprises, businesses will continue to adapt the latest competitive
strategies to stay ahead of the curve. The two most striking features of this
transition are increased automation of data processes and delivery of
instantaneous analytics solutions.

3. The Forbes article Mckinsey’s 2016 Analytics Study Defines The Future Of Machine Learing offers an analysis of
the potential of Machine Learning (ML) in improving the current state of Predictive Analytics. Forbes reports that
the McKinsey study has identified 120 ML use cases across 12 industry sectors and surveyed over 600 industry
experts about the potential impact of Machine Learning in Business Analytics. Business Analytics is probably the
number one application area where the future Data Science and future Data Scientists will play a key role.
Data Science Trend in 2018 touches on Big Data, AI, Blockchain, Serverless Computing. Edge Computing, and
Digital Twins, which broadly sums up Gartner’s “Intelligent Digital Mesh” – the confluence of the physical and
digital worlds. One point is evident: the adoption of Artificial Intelligence and associated technologies in global
businesses will be widespread.
Features of future AI-Powered Data Science:
Domain specialization of Analytics platforms: The next-generation of Analytics will rely heavily on
domain specialization, thus delivering solutions for target industry sectors. Data Science is Changing and
Data Scientists Will Need to Change Too-Here’s Why and How? from Data Science Central describes
Advanced Analytics platforms with access to third-party GIS and consumer data. The current market trends
in Business Analytics indicate that the platform strategy will soon shift from being a “one-stop, general
purpose” platform to a domain-specific solution geared to industry sectors such as ecommerce, finance,
HR, manufacturing and so on.

4. Automation of Analytics processes: More than 40 percent of Data Science tasks will become automated
by 2020. Significant Analytics processes like Data Preparation or Data Modeling will become automated in
most cases. Automation tools like SPSS and Xpanse Analytics are already in wide use. The learning
algorithms of ML-powered, AI solutions will provide quicker and better results over time. The McKinsey
article what’s now and next in analytics, AI, and Automation provides a clear vision of the digitized future,
where advanced digitization of business processes will be a differentiator between businesses that survive
and those that perish.
The Middle Layer of the Analytics stack will absorb the Data Science: The Data Science smarts will be
hidden in the middle layer of the Analytics platforms, as is evident in many VC-funded startup Analytics
solutions.
Multi-skilled Data Scientists will be required: In addition to being highly skilled in their fields, future
Data Scientists will be knowledgeable in industry domains to succeed in their jobs. Without the adequate
domain knowledge, the future Data Scientists will not be able to quickly translate a business problem into a
Data science.

5. Predictive Analytics will require divergent skills for different industries: Predictive Analytics is
becoming so specialized and divergent across industry sectors that the future Predictive Analytics tools and
features will be tuned for industry-specific applications.
Citizen Scientists will perform sophisticated Analytics: Analytics platforms will become so well-
equipped that Citizen Data Scientist will be able to execute Advanced Analytics tasks without the help of
experts.
Deep Learning will be simplified and operationalized: Deep Learning (DL) requires more simplification
for full adoption into Business Analytics platforms. DL techniques hold groundbreaking promise for
significant applications in forensic science through highly accurate facial recognition, and the wide
adoption of this technology into Analytics platforms will be a game-changer for the Business Analytics
solution provider market.

6. Continued Evolution in Data Science
A Dialogue Between Two Visionaries:
An interview conducted between two leading Data Scientists highlights milestones in the history of Data
Science. The post discusses the historic perspective of Data Science, the discipline, and the explorative
field as it unfolded through the personal experiences of two notable individuals.
Automation And The Future of Data Science:
Technological evolution has shown that while new technologies replace human tasks, they also create new
roles. Steam engines, electricity, and digital platforms at first replaced human roles, but later created new
types of jobs. Similarly, AI and Machine Learning are creating new roles for Data Scientists and business
users while replacing some old roles. Newer and better technologies disrupt, destabilize, and then give birth
to a new world with emerging job roles.
The Future of Machine Learning And Data Science:
Sebastian Raschka, applied Machine Learning and Deep Learning researcher at Michigan State University
and the author of Python Machine Learning, offers his views on what is changing in the world of Data
Science and Machine Learning.
The Future of Data Science And How AI is Changing The World:
This Intel video shows that for Data Science to reach the next level, it must go beyond number crunching.