The Arkansas Higher Education Coordinating Board (AHECB) has approved Arkansas State University’s proposal to offer a new bachelor of science degree program in data science and data analytics.

The need for data scientists and data analysts within the state and nation is growing extremely fast, as recognized in 2017 by Gov. Asa Hutchinson when he constituted a blue ribbon commission to report on “the economic competitiveness of Arkansas in data analytics and computing.”

The outcomes of the commission’s work recognized universities’ potential to contribute significantly to developing a data science and data analytics (DSDA) workforce in the state.

“We are excited to offer this new BS degree program to our students,” said Dr. Alan Utter, ASU provost and executive vice chancellor for academic affairs. “The data science and data analytics major will allow our students to compete for employment opportunities and graduate education in a wide variety of disciplines.”

Some of the special features of the DSDA degree are a capstone design experience, a credit-bearing internship experience, a 120-credit hour program where 85% of the courses already exist, and a program core that will cover content in statistics, computer science and coding, visualization, data governance and ethics.

Students will be able to choose a domain from a wide range of disciplines. Some of the domain studies in development are in the areas of health care, social sciences, engineering, computer science and geospatial technologies.

“I consider it a privilege to have facilitated an effort to develop the BS in data science and data analytics. It was an intense, collaborative effort executed over almost a year to develop a program with mostly existing courses,” said Dr. Abhijit Bhattacharyya, dean of the College of Engineering and Computer Science.

“This is a testament to the finest traditions in the university on collaborative endeavors and the recognition that data science/data analytics (DSDA) is a fast-evolving new discipline in the state and the nation. The convergence in priorities for DSDA within the state among government, industry and academia to develop the DSDA workforce also acted as a powerful motivation to develop the program,” he said.

Dr. Jason Causey of the Department of Computer Science will serve as the program’s interim director.

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In June 2018, India’s national think-tank, the NITI Aayog, released a discussion paper on the transformative potential of Artificial Intelligence (AI) in India. This paper said the country could add US$1 trillion to its economy through integrating AI. Since then, some of the biggest moves made by the government to act on this prediction is the formation of a task force on Artificial Intelligence for India’s Economic Transformation by the Commerce and Industry Department of the Government of India in 2017, and the Union Cabinet in December 2018.

These bodies approved an INR3,660 crore national mission on cyber-physical system technologies that involves extensive use of AI, machine learning, deep learning, big data analytics, quantum computing, quantum communication, quantum encryption, data science and predictive analytics.

But, what has been the progress in the nation since these ambitious missions were undertaken by the government? According to an analysis by research agency Itihaasa, the progress has been appreciable. When the agency used the number of ‘citable documents’, or the number of research publications in peer-reviewed journals, in the field of AI between 2013 and 2017 as a metric, India ranked third in terms of high quality research publications in Artificial Intelligence.

However, when parsed by another metric (citations, or the number of times an article is referred), India ranked only fifth behind the UK, Canada, the US and China which suggests that India must shift its focus to improving the quality of its research output in AI. The report also revealed that the Indian Institutes of Technology and the Indian Institutes of Information Technology were among the primary research centres for AI.

Currently, most of the traction in India is in the form of AI pilot projects from the government in agriculture and healthcare, and the emergence of AI startups in cities like Bangalore and Hyderabad. Though these are indications of grassroots level AI adoption, the pace of innovation isn’t comparable to the USA or China today.

Some challenges that the progress of AI in India faces are limited availability of manpower and of good quality and clean data, as there is no institutional mechanism to maintain high quality data. A report published by PwC in 2018 revealed another imminent challenge-that even with all the potential benefits of AI, which are envisaged to aid humans, people still have concerns regarding data privacy and are apprehensive to share data for a better experience. A vast majority of participants agree that they have major concerns regarding data privacy to the point that it is near unanimous (93%) and that they are hesitant to even share medical results knowing that it could help provide some personalised knowledge about their health, so data protection still remains a hazy domain hindering the growth of AI.

Another cultural challenge that India faces is the fact that the cost of failure is much higher here than in the West. While failing in an attempt at big innovation and grand goals might be seen as brave in Silicon Valley, failure often implies a loss of face in India and this has historically meant a lack of room for experimentation. All these challenges tell us that even with government funding and industry participation, India is just at the starting point of what seems to be a promising long road.

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• Marketing and Sales prioritize AI and machine learning higher than any other department in enterprises today.
• In-memory analytics and in-database analytics are the most important to Finance, Marketing, and Sales when it comes to scaling their AI and machine learning modeling and development efforts.
• R&D’s adoption of AI and machine learning is the fastest of all enterprise departments in 2019.

These and many other fascinating insights are from Dresner Advisory Services’6^th annual 2019 Data Science and Machine Learning Market Study (client access reqd) published last month. The study found that advanced initiatives related to data science and machine learning, including data mining, advanced algorithms, and predictive analytics are ranked the 8th priority among the 37 technologies and initiatives surveyed in the study. Please see page 12 of the survey for an overview of the methodology.

“The Data Science and Machine Learning Market Study is a progression of our analysis of this market which began in 2014 as an examination of advanced and predictive analytics,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. “Since that time, we have expanded our coverage to reflect changes in sentiment and adoption, and have added new criteria, including a section covering neural networks.”

Key insights from the study include the following:

• Data mining, advanced algorithms, and predictive analytics are among the highest-priority projects for enterprises adopting AI and machine learning in 2019. Reporting, dashboards, data integration, and advanced visualization are the leading technologies and initiatives strategic to Business Intelligence (BI) today. Cognitive BI (artificial-intelligence-based BI) ranks comparatively lower at 27th among priorities. The following graphic prioritizes the 27 technologies and initiatives strategic to business intelligence:

DRESNER ADVISORY SERVICES 2019 DATA SCIENCE AND MACHINE LEARNING MARKET STUDYToday In: Innovation

• 40% of Marketing and Sales teams say data science encompassing AI and machine learning is critical to their success as a department. Marketing and Sales lead all departments in how significant they see AI and machine learning to pursue and accomplish their growth goals. Business Intelligence Competency Centers (BICC), R&D, and executive management audiences are the next most interested, and all top four roles cited carry comparable high combined “critical” and “very important” scores above 60%. The following graphic compares the importance levels by department for data science, including AI and machine learning:

DRESNER ADVISORY SERVICES 2019 DATA SCIENCE AND MACHINE LEARNING MARKET STUDY

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• R&D, Marketing, and Sales’ high level of shared interest across multiple feature areas reflect combined efforts to define new revenue growth models using AI and machine learning. Marketing, Sales, R&D, and the Business Intelligence Competency Centers (BICC) respondents report the most significant interest in having a range of regression models to work with in AI and machine learning applications. Marketing and Sales are also most interested in the next three top features, including hierarchical clustering, textbook statistical functions, and having a recommendation engine included in the applications and platforms they purchase. Dresner’s research team believes that the high shared interest in multiple features areas by R&D, Marketing and Sales is leading indicator enterprises are preparing to pilot AI and machine learning-based strategies to improve customer experiences and drive revenue. The following graphic compares interest and probable adoption by functional area of the enterprises interviewed:

DRESNER ADVISORY SERVICES 2019 DATA SCIENCE AND MACHINE LEARNING MARKET STUDY

• 70% of R&D departments and teams are most likely to adopt data science, AI, and machine learning, leading all functions in an enterprise. Dresner’s research team sees the high level of interest by R&D teams as a leading indicator of broader enterprise adoption in the future. The study found 33% of all enterprises interviewed have adopted AI and machine learning, with the majority of enterprises having up to 25 models. Marketing & Sales lead all departments in their current evaluation of data science and machine learning software.

DRESNER ADVISORY SERVICES 2019 DATA SCIENCE AND MACHINE LEARNING MARKET STUDY

• Financial Services & Insurance, Healthcare, and Retail/Wholesale say data science, AI, and machine learning are critical to their succeeding in their respective industries. 27% of Financial Services & Insurance, 25% of Healthcare and 24% of Retail/Wholesale enterprises say data science, AI, and machine learning are critical to their success. Less than 10% of Educational institutions consider AI and machine learning vital to their success. The following graphic compares the importance of data science, AI, and machine learning by industry:

DRESNER ADVISORY SERVICES 2019 DATA SCIENCE AND MACHINE LEARNING MARKET STUDY

• The Telecommunications industry leads all others in interest and adoption of recommendation engines and model management governance. The Telecommunications, Financial Services, and Technology industries have the highest level of interest in adopting a range of regression models and hierarchical clustering across all industry respondent groups interviewed. Healthcare respondents have much lower interest in these latter features but high interest in Bayesian methods and text analytics functions. Retail/Wholesale respondents are often least interested in analytical features. The following graphic compares industries by their level of interest and potential adoption of analytical features in data science, AI, and machine learning applications and platforms:

DRESNER ADVISORY SERVICES 2019 DATA SCIENCE AND MACHINE LEARNING MARKET STUDY

• Support for a broad range of regression models, hierarchical clustering, and commonly used textbook statistical functions are the top features enterprises need in data science and machine learning platforms. Dresner’s research team found these three features are considered the most important or “must-have” when enterprises are evaluating data science, AI and machine learning applications and platforms. All enterprises surveyed also expect any data science application or platform they are evaluating to have a recommendation engine included and model management and governance. The following graphic prioritizes the most and least essential features enterprises expect to see in data science, AI, and machine learning software and platforms:

DRESNER ADVISORY SERVICES 2019 DATA SCIENCE AND MACHINE LEARNING MARKET STUDY

• The top three usability features enterprises are prioritizing today include support for easy iteration of models, access to advanced analytics, and an initiative, simple process for continuous modification of models. Support and guidance in preparing analytical data models and fast cycle time for analysis with data preparation are among the highest- priority usability features enterprises expect to see in AI and machine learning applications and platforms. It’s interesting to see the usability attribute of a specialist not required to create analytical models, test and run them at the lower end of the usability rankings. Many AI and machine learning software vendors rely on not needing a specialist to use their applications as a differentiator when the majority of enterprises value  support for easy iteration of models at a higher level as the graphic below shows:

DRESNER ADVISORY SERVICES 2019 DATA SCIENCE AND MACHINE LEARNING MARKET STUDY

• 2019 is a record year for enterprises’ interest in data science, AI, and machine learning features they perceive as the most needed to achieve their business strategies and goals. Enterprises most expect AI and machine learning applications and platforms to support a range of regression models, followed by hierarchical clustering and textbook statistical functions for descriptive statistics. Recommendation engines are growing in popularity as interest grew to at least a tie as the second most important feature to respondents in 2019. Geospatial analysis and Bayesian methods were flat or slightly less important compared to 2018. The following graphic compares six years of interest in data science, AI, and machine learning techniques:

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