Big Data and Analytics plays a transformational role in improving the quality of decision-making, bringing transparency and making a powerful impact on our economy. The number of businesses, organizations, and institutions now utilizing big data solutions has exploded in recent years, as has the amount of data collected. We asked Sema Barlas, Director of MScA in Analytics, the University of Chicago Graham School about what enterprises need to keep in mind as they put data analytics at the core of their business processes and how the University is helping the industry with the right talent to compete in the market.
The industry is seeing a rising importance of Big Data Analytics and AI. How do you see these emerging technologies impact the business sector?
Analytics in general, and automation and AI in particular, will revolutionize the businesses processes in every aspect, from customer service, product development, finance, operations, human resources, to logistics. Therefore, companies will start competing on analytics more and more, and analytics will become the engine for efficiency and productivity, as well as innovation, entrepreneurship, and growth.
How is The University of Chicago- Graham School’s Analytics and Data Science Program contributing to the growth and transformation of analytics and big data education?
The field of analytics is changing so quickly that it is difficult for programs to transform their curriculum to educate students on the state-of-art technologies and on the ways these technologies could be used to solve practical problems in impactful ways. As a result, companies are having difficulty in finding qualified analytics professionals who can lead analytics initiatives from day one, without a long training process. The Master of Science in Analytics (MScA) program at the University of Chicago Graham School prides itself in being at the forefront of teaching the state-of-art analytical tools with rigor so that students learn to use analytics creatively and flexibly and are able to apply analytics to solve practical problems effectively.
What is the edge The University of Chicago- Graham School’s Analytics and Data Science Program has over other institutes in the industry?
MScA differentiates itself from other similar programs in that (1) MScA offers a comprehensive curriculum where students develop in-depth understanding of wide range of core as well as such emerging analytics methodologies as automation and artificial intelligence (AI); (2) the program provides students with many opportunities to practice analytics through realistic simulations and by using the most advanced tools, such as Hadoop and Spark, on variety of different cloud-based platforms as well as university servers; (3) students learn to apply advanced analytics tools to solve real-life problems in elective courses and through the three-quarter long capstone process designed to address the business problem of a partnering company; (4) students are provided with a wide array of opportunities to develop interdisciplinary skills reflecting the inseparable relationship between business, analytics, and multilingual computation; and (5) the program offers a rich set of areas of specializations, reflecting the emerging reality that analytics is evolving differently in different industries, for different types of data, and for different kinds of business problems.
Kindly brief us about your role at The University of Chicago- Graham School’s Analytics and Data Science Program and your journey in this highly promising sector.
I am the Senior Academic Director and the founder of the program. I have a Ph.D. in Research Methodology and Quantitative Psychology from The University of Chicago. My expertise is in the areas of applied marketing models, statistics, research methodology, and consumer behavior. I have published papers in peer-reviewed journals with over 500 citations. Prior to joining the Analytics program at the University of Chicago, I taught at Argosy University in Chicago, McGill University in Canada, and the University of Groningen in the Netherlands. I also worked in the industry for several years as analytics professional at companies that include Sears, Roebuck, and Co. and Experian.
What would you advice to aspiring big data and analytics candidates?
Analytics is a difficult field. I advise aspiring big data and analytics candidates to accept from the start that they must be prepared to supplement their formal learning by learning new tools, techniques, and application methods on their own; it is impossible for even the best analytics program to equip them with all the skills they need to become successful analytics professionals. Therefore, analytics professionals must be lifelong learners.
The following are my specific suggestions. First, prospective students of analytics must have a solid understanding of calculus, linear algebra, and programming in R and Python. Second, as students learn analytics as part of a formal program, they should develop an interest in an application area (marketing, finance, etc.) and learn the business side in that area as much as they can. Lastly, students must continue to learn the new methodologies after they graduate independently and by taking courses offered by programs to their alumni; I should note, for instance, that the MScA program offers a free course and deep tuition discounts for alumni who choose to continue learning.
What are some of the challenges faced by the industry today?
There are at least five major challenges faced by industries attempting to compete using analytics.
Insufficient number of qualified analytics professionals.
1. Lack of data quality: Different units in many companies compile data from a variety of different sources and do not always have an enterprise-level database with common standards applied across these different units.
2. Data security issues: It is becoming increasingly more challenging for organizations to secure their data and to protect the privacy of data sources. We are only starting to understand the risk factors for data breaches that are damaging to the reputation of companies and harmful to customers. I believe companies will soon face potentially costly compliance requirements for emerging new regulations.
3. Lack of analytics culture: Many organizations still lack an analytics culture that is necessary for them to enjoy the benefits of the analytics fully. The return-on-investment in analytics will remain limited until everyone in an organization understands analytics at some level, believes in its value, and is able to think in analytical terms.
4. Lack of ethical guidelines: Ethics of algorithms is emerging as a new field. I feel many companies will suffer from reputation or other kinds of risks until guidelines for ethical use of analytics are clearly stated.
Please share some major achievements of The University of Chicago- Graham School’s Analytics and Data Science Program under your leadership.
The MScA program attracts a large number of students with excellent academic and professional credentials. At the inception in January 2014, the program started with only 30 students. Today, it serves to 300 highly diverse group of students and 258 alumni. Over 98% of the students who start the program successfully complete it. A recent survey revealed that nearly 100% of MScA alumni are employed, generally by reputable companies, and in many cases, at senior levels. Also, it should be noted that students start enjoying better career outcomes right after they start the program; so far, the MScA involvement has contributed significantly to the 33% increase in annual salaries generally and to more than 50% increase in salaries of the graduates.
Can you throw light on the latest employment trends in big data and analytics industry?
Analytics has indeed become the sexiest job of the 21st century, as predicted by Davenport in 2012. In my view, an increasing trend in employment opportunities in analytics will continue for years to come. Yet, a different set of advanced skills will be required for analytics professionals in different functional areas of business (e.g., marketing, finance, or AI) or professionals working with different types of data (internet of things, text, video). Professionals with in-depth knowledge of particular business and related analytics will enjoy lucrative career prospects; so, specialization in certain types of analytics will be necessary for many individuals to succeed. For instance, experts who focus on addressing data quality issues by using automation and AI will soon be sought after. At the same time, there will be an increasing demand for professionals who understand the business, know a broad set of analytical tools, and thus, could manage cross-functional teams.