Hot Jobs Topic: Data Analytics

Data Analytics Employer Panel on October 22 Reinforces the Importance of Mixing Business and Science Skills

On Thursday October 22, PSM students packed the board room in the CoRe building to hear from and talk to leading employers in the field of data analytics. Seven employers participated in a panel moderated by PSM instructor Christie Nelson, Ph.D., from The Command, Control and Interoperability Center for Advanced Data Analysis (CCICADA), including: Ashish Jain, Vice President, Business Intelligence, iconectiv; Rahel Jhirad, Director, Data Science, Hearst; Faisal Kahn, Senior Director, Data Science, Aetna; Ken Kranz, Associate Director, Big Data Solutions, Cognizant; Daniel Roberson, Manager Applied Analytics, Boeing Commercial Aviation Services; Raphael Alonso, Vice President at Systems and Technology Research (STR); William M. Pottenger, CEO, Intuidex.

Each employer described how analytics is applied to unique business applications at their companies. For example, several companies require predictive data modeling for different purposes. Ashish Jain from iconnectiv described using data modeling to build predictive models of subscriber churn, while Daniel Roberson of Boeing explained that they use such models to predict equipment maintenance needs and to optimize their supply chain planning. Dr. Bill Pottenger, an analytics professor for the PSM program and the CEO of Intuidex, a military intelligence firm that uses machine learning techniques to identify communications trends, discussed the challenges of building algorithms that function in near real-time and that require fast and constant adjustment. Raphael Alonso, also of a defense industry machine learning start up, and Rahel Jhirad from Hearst, where analysts need to program ads to align with media content,  echoed the need for data analytics professionals who can create models that adapt quickly to real time information flows. Faisal Khan, also a PSM instructor and a Senior Director of Data Science at Aetna, on the other hand, explained that the healthcare industry needs to build complex, but stable algorithms that hold up over time since many clients rely on a consistent process.  Ken Kranz, who oversees Big Data Solutions at Cognizant, a large consulting firm, discussed the versatile skill and knowledge set that consultants need to both help clients identify solutions unique to their business issues and to implement them effectively.

This discussion from employers generated a larger dialogue about the importance of understanding the business context for analytics work. Several panelists echoed the thought that students need to understand the industry in which they want to apply analytics work, because the business needs that drive the analytics work can be very different. Once you are inside of a company, your success often depends as much on your ability to apply your technical knowledge to a pressing business need as it does on the knowledge itself, according to the employers. As panelist Rahel Jhirad noted, “I am good at math, but I can also X-ray a business problem to see the economics, to see who is benefitting – You do well in analytics if you can understand the underlying business problem.” Faisal Khan added, “You need situational awareness – businesses don’t have time to understand statistics. You need to worry about the business context, how to achieve cost savings, and you need to summarize your results.” In consulting, Ken Kranz discussed the need for workers to be able to assess company goals and data structure in order to choose the right approach and tools for analytics.

Students asked panelists about the best ways to focus their learning to be successful in the field of analytics. Several employers told students to focus on the fundamentals – the underlying theory, math, and structure of analytics techniques and software. Raphael Alonso noted that it is harder to make up this kind of deep learning as you advance your career and take on more responsibilities. Panelists told students that the field involves a constant process of learning new techniques and software on the job, so it is important for students to learn the theory and other fundamentals thoroughly while still in school. Overall, employers like to hire people who have a strong ability to learn new things quickly – to adapt to new applications on the fly. Faisal Kahn did stress, however, that it is also helpful to “learn the buzzwords” around analytics for the industry you are targeting, and to learn the use some of the popular software. He told students, “Sometimes we do hire for specific knowledge, but the business world has complex needs” that are often specific to a specific employers’ needs at a given time.  Panelists agreed that deep knowledge of analytics theory, math, and business fundamentals are essential, but students can also benefit from developing a broad view of – and learning how to use some - of the applications commonly used in their target industry area. 

Finally, several students asked employers about how best to navigate the analytics job market with its complex skill requirements and confusing array of job titles, which range from business analyst to data scientists, analysts, and engineers. Several employers chuckled at the mention of the multiple job titles because, as several noted, there is little agreement across the field about how to define specific job titles. Some panelists suggested that those with a “business” in the title often deal with more summarized data, than those with the term scientists or engineer. Large companies tend to develop more specialized titles and job descriptions, but since this is an emerging field, employers are creating job titles and duties that meet their own needs. This often means that what one data scientist does at one company could be very different from what another does at a separate company.  One employer suggested that students focus on summarizing their strengths on their resumes so that the employers can see where they fit best in their job structure.

……stay tuned for the next blog post where we apply an analytics solution to shed more light on this issue in….By the Numbers: What Skills Do I need to be successful in Analytics – and what’s the difference between all those job titles?