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The Future Impact of Data Science on Business Analytics

By   /  August 9, 2018  /  No Comments

Data ScienceThe 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.

The Forbes article McKinsey’s 2016 Analytics Study Defines The Future Of Machine Learning 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 Trends 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.
  • 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.
  • 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 Scientists 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 Predictions about Data Science, Machine Learning, and AI for 2018 offers predictions about the AI-powered Data Science of the future. This post indicates that Data Scientists will increasingly move toward domain specialization, and the generalist Data Scientists will soon disappear. On the other end of the spectrum, the Citizen Data Scientists will have advanced tools at their disposal to perform advanced analytics tasks on their own.

Automation of Machine Learning: The Top Contender on the Analytics Vendors List

The Forrester Report Massive Machine-Learning Automation is the Future of Data Science confirms that while Machine Learning promises revolutionary breakthroughs in Predictive Analytics, the learning curve associated with this technology is often steep. For embedded Machine Learning tools to become user-friendly, automation is the only way. In enterprises, the future business analytics tasks will be handled by CEOs, managers, and other ordinary business users, and they will want quick solutions. Automation of Machine Learning technologies will help the experts and novices alike build predictive models for uncovering actionable intelligence or forecasting customer needs.

In the Huffington Post article Where Will Data Science Be in Five Years?, Anthony Goldbloom, co-founder and CEO of Kaggle, states that in near future, the centralized Data Science units will disappear and each business unit will have dedicated Data Science teams.

Automation Will Not Replace, but Aid Data Scientists

The future goal of most Analytics solution vendors is to provide speedy, automated tools to business users so that they can get their Business Analytics done with the minimum fuss. A post from Cloud Computing News, Why Automation Won’t Replace Data Scientists Yet, explains why specific Analytics tasks can be automated while others cannot be. As simplicity of use will play a key role in differentiating the major Analytics platforms from the other solutions, the vendors are now focusing on ease of use in the automation of key Analytics tasks. Data Preparation, Data Integration, and Data Modeling seem to be the top priorities for automation in the major solution providers.

GDPR will Force Businesses to Turn to AI-Powered Data Science

The European Union legislation of the General Data Protection Regulation (GDPR) has come into effect, and businesses globally are looking for AI solutions to remain complaint with the GDPR laws. The blog post 8 Experts on the Future of Artificial Intelligence & Big Data contains an account of how businesses that collect huge amounts of customer data will use AI to provision easy opt-in and -out of communication, generation of reports on customer data collection, and easy removal of the data.

The better trained Data Scientists of tomorrow will also be in a strategic position to build more accurate training models for reducing risk, preventing fraud, improving efficiency, and personalizing customer experience.

The Reinvented Data Scientist of the Future

The Datanami article The Future of Data Science states that with time-consuming and laborious processes like Data Preparation becoming automated, the Data Scientists will now be free to reinvent and explore business problems more holistically. Traditionally, Data Scientists have invested 80 percent of their time and effort in collecting and preparing the multi-sourced data for meaningful Analytics, leaving them little opportunity to pursue Advanced Analytics tasks.

Now, with Machine Learning tools to handle all the rote processes, the Data Scientists will be free to focus on the real issue — the Data Analytics phase. In the future, even automated modeling tools may be available to build instantaneous models to empower the Citizen Data Scientists.

           
Continued Evolution in Data Science

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.

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.

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.

This Intel video shows that for Data Science to reach the next level, it must go beyond number crunching.

 

Photo Credit: Timofeev Vladimir/Shutterstock.com

 

 

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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