AI and Analytics for Business
Delivering on Customer Analytics: Cultivating and Managing Highly-Specialized Teams
AI and Analytics for Business (AIAB) and Deloitte Analytics have independently partnered with hundreds of companies and students who seek to leverage customer analytics for a lasting competitive advantage, and even transform or disrupt entire industries. While AIAB and Deloitte work on completely different projects with separate goals, a key recurring theme has emerged: for an analytics project to succeed, it must 1) draw on specialized talent from across the organization and 2) effectively tie to the organization’s long-term business goals.
Gap among Disciplines
“At AIAB, we’ve formally partnered with more than 20 corporate partners on their major R&D analytics challenges, and companies often struggle with the Research Opportunity program’s first step– the data asset audit and scoping of relevant business problems,” says Eric Bradlow, AIAB’s Co-Director. With specialized expertise spread across the organization, it’s easy for companies to become overwhelmed with the amount of customer data now available and exactly how it can inform business decisions. And we’ve equally seen the flipside, where companies know where they want to go but most of the relevant data lives in different business silos throughout the organization, many of which are either wholly unstructured or not easily brought to bear with one another to yield actionable analysis.
Bridge-building: Rising Demand for MBA Graduates in Customer Analytics
While it may seem common sense to align analytics challenges with business objectives, it’s easier said than done. The expertise required to achieve this alignment do not live in one department or person—or the so-called “unicorn candidate.” In terms of recruitment, companies have increasingly turned to MBA students interested in analytics to help bridge the divide between the technical and business strategy teams.
This trend is already playing out in MBA recruitment here at Wharton. Earlier this academic year, Wharton’s MBA Career Management office helped lead an effort to create the “Analytics & Data Science” job function in their recruitment database. The first two months alone saw 60 jobs postings across all industries. “These numbers really helped shed some light on the spike in interest from employers for MBAs in these strategic analytic roles,” said Maria Halpern, Director of Student Engagement for Wharton MBA Career Management, who helped spearhead the initiative. “Five years ago, this was unthinkable—these jobs came in as one-offs or from faculty referrals, and now it’s from all industries.” And this just scratches the surface. There are many companies that struggle in articulating the exact role they require, but now Maria can not only help tease out their specific needs but also direct them to similar job postings for reference.
MBA Student Profile
Greg Caiola (WG’16) came to Wharton specifically for its analytics programs with the intention of filling the bridge-building role for highly specialized technical and strategy teams. Prior to Wharton, he worked with Neustar and Freddie Mac as an analyst/data scientist. “There reached a point in my career where I either wanted to ramp up my analytics skills with a masters in statistics or my business ones with an MBA. A friend and colleague reached a similar turning point, and we pursued different paths: she went on to pursue a more technical degree in data science and statistics, and I went to Wharton for my MBA.”
For Greg, the bridge-building aspect was very real. As an analyst at prior jobs, there were times where it was difficult to relay the nuance of statistical findings. Sometimes selection bias was more minimal while other times it could put the business at risk to proceed with otherwise positive market research. “It can be difficult talking to the business strategy folks about these shades of gray, and it’s one of the reasons I pursued an MBA at Wharton—I wanted the quantitative chops to work with the data scientists while tackling broad business challenges.” Greg is now interning at eBay and serves as the VP for Careers at the Wharton Data and Analytics Club (WDAC).
Technical Training for MBA Students
So what is the greatest barrier for MBAs starting a career in analytics? Greg thought the main issue would be salary or set career trajectory, but when he followed up with club members individually, he was surprised to hear an entirely different story. “The recurring barrier for everyone I spoke to was the technical and programming skills.” Some members had never written a line of code in their prior work, and the learning curve can look too steep from that vantage. “I realized we had to help spread the word that coding wasn’t as hard as some people thought and that choosing customer analytics doesn’t mean you become an engineer—you would be working with a team of expert programmers.” The goal, for Greg and many here at Wharton, is to become fluent enough to step into a bridge-building role—the “glue” as Greg call is—to help the whole analytics function stay on track in solving key business challenges.
This does not mean students are off the hook. “To succeed in this bridge-building role,” says Pete Fader, AIAB Co-Director, “it is essential for business students to gain legitimate technical skills– they cannot merely conceive of how the quantitative work is done, but must be able to get their hands dirty with the data and interface with high-level engineers and statisticians.” Greg (and our alums) are quick to note the many opportunities to gain these fundamental skills both at Wharton and Penn more broadly—“and with WDAC, we’re only adding more!” AIAB is likewise helping to expand technical training for MBAs (and undergraduates), and in this past academic year alone our student-facing programs doubled, with more new offerings on the horizon.
With so many departments and student clubs at Wharton coalescing around customer analytics, this is a particularly exciting time for MBA students eager to understand its cross-industry applications. The technical skills and business training they acquire now will allow them to interface with highly-specialized stakeholders across an organization, deepen their skills in a vital, rapidly-emerging field, and ultimately set them on a trajectory to capture the C-suite or launch their own disruptive start-ups. For our part, AIAB continues to look to our corporate partners, alums, and students to broaden our co-curricular programs and provide graduates with the tools and networks they need to hit the ground running on multiple fronts in customer analytics.
The Demand for Cross-Discipline Expertise
At Deloitte, we’ve worked with hundreds of clients on analytics initiatives, and the reason why alignment is often easier said than done is commonly because: (1) lack of prioritization of the business needs that can be solved with analytics, (2) data integrity issues that limit what is achievable in meeting business goals, and (3) adoption of analytics insights to drive business decisions when the data is not 100% perfect. These hurdles require not only a cross-discipline team to tackle, as mentioned above, but it also requires alignment of objectives across various parts of the organization. As Mark Zozulia, Principal at Deloitte Consulting and national leader of our Business Intelligence and Data Warehousing services, reminds us, “analytics is very much a team sport, especially at the leadership level; the C-suite – CIOs, CMOs, and CFOs alike – will need to collaborate to build an interconnected analytics ecosystem to become more efficient and succeed.” In our experience, the most successful analytics projects typically start with building a clear strategic roadmap of how analytics will be utilized to address business needs, and spending time upfront to gain alignment across key stakeholders to agree on how capabilities will be built over time. Having business-minded professionals like MBAs, who are fluent (but not necessarily experts) in technical skills become a cornerstone for this bridge building.
Once organizations have a clear charter and priorities around analytics, they are often left with a void for the right expertise to execute. Technologies and vendors are vast and ever evolving, and our clients often seek in-house solutions due to the proprietary nature of data and the custom-solutions required to fit within the IT landscape of that company. Thus, many companies have gone down the path of creating a center-of-excellence (COE) for analytics within the organization. A COE model can be helpful in (1) creating momentum and elevating the usage of advanced analytics, (2) driving cross pollination of best practices across otherwise silo efforts, and (3) getting critical mass in hiring the right talent. However, in order for analytics COEs to be successful, businesses must be willing to fund and design the right talent models for long term success. Having clear hiring profiles, recruiting engines, and career tracks for data scientists and analytics experts have proven to be common challenges in establishing analytics COEs where top talent choose to stay. Without the right community and knowledge sharing amongst data scientists, solo-hires have often proven to be short-lived. Designing and building these people, process, and technology components of a successful analytics COE is another hot area today for MBAs to offer their expertise.
Recruiting World-Class Talent
Recruiting and deploying talent to respond to analytics challenges cuts across several disciplines, as mentioned. “Currently, at least, with analytics, there is no one-size-fits-all model, and our clients understand that as well as we do,” as David Rudini, Principal, Deloitte Consulting LLP and Wharton alum, points out. Generally, what we’ve seen is analytics talent pools fall under three types:
- Strategy: These individuals demonstrate deep understanding of business issues and advise clients on how analytics can help them make better decisions
- Advanced Analytics & Modeling: These individuals demonstrate deep expertise in and use specialized techniques and tools (e.g., predictive modeling, simulation, and data mining) and various internal and external data sources to drive business strategy, performance, and decision-making
- Information Management: these individuals possess skills to develop the technology and data infrastructure to fuel sophisticated algorithms
Contrary to what most might think when it comes to analytics, we estimate that 70% of the talent need is in Strategy and Information Management work, while the remaining 30% are tackling Advanced Analytics and Modeling challenges. Data scientists are one part of the equation, but recruiting those who can help frame, sell, and use analytics under often ambiguous settings is just as important. In practice, our teams who deliver on customer analytics projects are often interdisciplinary, and the bridge-builders who can be the glue amongst a team of specialists is very much in demand.
Supplying the Right Talent: What This Means for Universities
Because specializing in the field of business analytics is still relatively new, a small pool of “holy grail, hybrid talent” still remains relatively elusive. “These individuals are those who deftly serve a dual purpose – they are not only able to crunch numbers and algorithms, but can also derive deep business insights that benefit the business,” says Mark Zozulia. Universities have a tremendous role in shaping this next generation of analytics talent.
As students train and prepare to focus on a career in analytics, they should keep in mind that while quantitative skills are important, strong leadership and teaming qualities will be even more important in building bridges. Moreover, analytics challenges are typically domain or industry specific. For example, a supply chain analytics challenge for the technology sector will require very different contextual knowledge than a customer analytics role for the retail sector. Developing core business and industry expertise to amplify analytics foundations will also be key.
Lastly, with the ever changing technology and techniques utilized, students should seek to familiarize themselves with the next generation of methodologies. Machine learning has proven to be a very different way of approaching problems and tackling data challenges. Becoming familiar with these approaches will arm students with the right weapons, and developing hybrid courses that bring together a cross-disciplined approach to solving analytics challenges is a great practical approach for universities.
About Deloitte Analytics
Deloitte Analytics creates value for clients by helping them transform data into deeper and more rapidly accessible insights that power informed decisions. Spanning Deloitte’s portfolio of businesses, our analytics professionals work with organizations to help identify and address their requirements in business intelligence, data management, statistics, change management, technology, automation, risk and governance. Our broad-based approach is fueled by our deep industry knowledge, global presence, broad functional experience and mastery of technology. Combining advanced analytics skills – such as data visualization and performance enhancement – with the power of strategic technology alliances, Deloitte Analytics works with clients to provide tailored solutions that generate tangible and measurable results.
As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.