We've spoken to 10 experts in the field of data science, who have shared their views on business analytics.
Mark Kennedy, director of the KPMG Centre for Business Analytics at Imperial College Business School, says that forward-looking employers are getting an edge by making use of “cognitive” systems, which are better at matching people with jobs.
“Big data is definitely the future of candidate selection, but not of recruitment per se, as that will always come down to employers’ ability to get an emotional response from prospective hires — excitement, belonging, hope — through personal interactions,” he says.
“People analytics offers benefits not only to managing turnover and attrition, but also to candidate selection, project team assignments, succession planning, executive development, and organizational change programs,” he adds.
Theos Evgeniou, professor of decision sciences and technology management at INSEAD, believes that big data is reshaping management.
“Think of the impact excel had on managers’ daily jobs. The impact from big data may be a similar, or bigger, leap forward in terms of how it will change people’s jobs,” he says.
“Much like the introduction of excel required managers to develop basic technical and quant skills, going to the next stage will require even more quant and technical skills.
“Managers will need to learn new tools to analyze and visualize information, and also develop their ability to better communicate with data scientists.”
Yehuda Bassok, chair of the Department of Data Sciences at USC Marshall School of Business, says companies increasingly want to hire managers trained in the art of analytics.
“We’re seeing very strong demand from consulting companies, which are getting access to our students very early to identify the best and to hire them,” he says.
“I do see a lot of interest from consulting companies like KPMG and Deloitte, so probably their clients are interested in data analytics too,” he says, adding: “Some companies in the healthcare sector are also trying to use data and analytics in a more sophisticated way.”
Juan José Casado Quintero, academic director of the Master in Business Analytics & Big Data at IE Business School, says that the big data “revolution” is a key factor in how enterprises face their digital transformations.
“Companies are generating new products and services based on new analytical capabilities that help them gain competitive advantage, engage with customers, and differentiate from their competitors,” he says.
“Those companies with business models that successfully leverage the available information of their clients — for example Google, Amazon, Facebook, Twitter, or LinkedIn — become the leaders of their sector.”
Roy Lee, NYU Stern assistant dean of global programs, which include the MS in Business Analytics, says companies face challenges in developing a culture in which they can truly utilize their data.
“Unlocking the potential of analytics takes more than just hiring data scientists or building assets,” he says. “Organizations have to challenge long-standing processes and oftentimes strong-willed personalities to initiate a shift toward building a data-driven culture.”
He adds: “In a truly data-centric organization, data is the backbone for all business decisions.”
David Kiron, executive editor of MIT Sloan Management Review and former senior researcher at Harvard Business School, says that data is critical in getting to grips with consumer demand.
“If you aren’t using data to anticipate customer interests, your company is going to be at a disadvantage, especially when competing against the most advanced digital companies,” he says.
Competition from digital-savvy rivals is intense, he adds. “Many companies are going to find their business models disrupted if their leaders do not find a way to quickly shift to a digital mind-set.”
Arne Strauss, associate professor on the MSc Business Analytics at Warwick Business School, believes that businesses must form increasingly interdisciplinary teams to harness the power of big data.
“Successful projects in business analytics are often run by interdisciplinary teams, with experts on analytics, information technology, methodology, and on the business implication of the problem,” he says.
He adds: “Apart from the general shortage of people with skills in analytics, there is specifically a shortage of team leaders with sufficient skills in all these areas such that they can successfully manage the project.”
Peter Fader, who leads the Customer Analytics Initiative at Wharton School, is adamant that business schools must further champion the use of data in business management. “We’re placing a big bet on it here — not that just that analytics will be a big part of Wharton, but all business schools are saying the same thing,” he says.
“We’re training business leaders who can think and appreciate analytics…We think data is for making better decisions,” he says, and adds: “The real win both for a school and the whole field of analytics is in having real executives, C-level people, who are fluent in analytics…. That’s the bet we’re making: that there is enough folks with C-level ambitions for big companies that believe the path to the C-suite will include having analytics.”
Dustin Pusch, director of decision sciences and business analytics at George Washington University’s business school, highlights the security and privacy concerns around data, heightened by high-profile hacks on companies like TalkTalk and JPMorgan Chase.
“Providing access to relevant data in a timely manner across the various functions and levels of an organization while ensuring security has always been a challenge,” he says. “Although many organizations have fairly mature processes and frameworks in place to manage their data, several other organizations still struggle with it.”
Michael Goul, professor of information systems at the W. P. Carey School of Business, says that business school students will need to warm to data science.
“Since virtually all MBAs need to make cogent arguments from an evidence-based perspective, those who want to make a strong case for their strategic decisions will need relevant data science skills,” he says.
“They will for sure work with data science teams, helping to guide them in what hypotheses to test, what assumptions need to be examined for validity, and how well experiments designed to test innovative ideas turned out.
“To provide that guidance and interpret experimental results, they need to have more than an occasional acquaintance with what data science is about and how these experts do their jobs.”