Building an Analytics Team

Those who cannot remember the past are condemned to repeat it.

—George Santayana

Creating a highly functional and successful advanced analytics and artificial intelligence (AI) team is a unique and nuanced endeavor. On the face of it, the process of building an analytics team may not seem to be different from building any other functional or technical team, but this is where problems typically start.

In this chapter, we will discuss the organizational context in which you and your new team will be operating, and, as part of the discussion, we will outline the characteristics of the communication and collaboration interface between the analytics team and the functional/operational teams. Also, we will examine hiring in non-traditional populations that you should consider when building a high-performance analytics team.

We will examine the possibility that young professionals can play a unique and valuable role...

Organizational context and consideration

One of the primary considerations to keep in mind is that the context in which the advanced analytics and AI team operates is different than operational teams. Operational and functional teams, like finance and manufacturing, work in their defined domains and rarely deviate from their cyclical processes. Analytics teams work across the entire organization, interfacing with all levels of the company with a focus on discovery and innovation.

The interface between the advanced analytics and AI team and the other groups and teams is one that needs and deserves more attention than it currently receives. The advanced analytics and AI team is focused on disruption, creativity, and innovation. This is an activity that is marked by success and discovery, followed by setbacks and retrenchment. Functional areas are characterized by smooth, predictable process flows, and well-established processes that move forward along an established path at an established...

Internships and co-op programs

One approach to gaining access to young, intelligent, and pre-screened talent is to work with select undergraduate and graduate-level universities to hire undergraduate and graduate-level students on internships and co-op programs. This approach enables the students to work on varied and challenging real-world problems and situations, and you and your team have an opportunity to "interview" these prospective employees over multiple months.

An important aspect of this approach is to assign the interns and co-op participants work that is important to the business and that has the possibility to change the way business is executed. Giving the students "busy" work is a waste of everyone's time and energy. You must be ready for you and your team members to mentor and manage the young team members. You must also be ready for projects to fail, but to learn from those failures, and you must be ready for projects to succeed and that...

Diversity and inclusion

Your team will benefit from diversity in all aspects that you can amalgamate. Diversity in age, gender, organizational level, geographic origin, socioeconomic means, faith (and lack thereof), educational background, sexual orientation, neurodiversity, circadian rhythms, and more will make your team more effective and valuable.

Advanced analytics and AI teams are made up of a wide range of personalities, from the introverted to the highly extroverted, from the careful and thoughtful to the impulsive and innovative. Analytics teams benefit greatly from the varied perspectives that a wide spectrum of individuals bring to the collaborative environment.

Perhaps you have seen teams where the leader hires mostly people that think, act, and look like themselves. In extreme cases, this is called conative cloning and it can lead to myopic thinking and a narrow focus when approaching challenges and the resulting solutions. If you think of hiring as a continuum...

Neurodiversity

Starting from childhood, I have seen and experienced a substantial range of human behavior over the course of my life. The United States has had a convoluted and complex journey when considering the best path of engagement and treatment for people who exhibit degrees of mental health.

It is important that you and your team are aware that the inclusion of neurodiversity is desired and has been, informally, part of the consideration of the assembly and management of development teams and advanced analytics teams for decades.

Neurodiversity is the new and current label for people who exhibit a wide range of mental health conditions and syndromes. Most of us are aware of Asperger's syndrome, Down's syndrome, and a number of other conditions that we now describe and label as neurodiversity.

Leading companies like Dell, EY (Ernst & Young), Microsoft, and SAP have well-established neurodiversity hiring and management programs. Companies need to establish...

Disciplinary action

Hiring and firing team members remains part of the management of any team. I have had employees confront me and express dismay that I was dismissing a staff member. The law requires many things of managers and corporations. When someone breaks the law or flouts company policy, they do not get a free pass just because they belong to a diversity category. When things go wrong, there are consequences; this does not change because we are hiring a wider range of people. As a manager, you must treat all employees, across all classes and categories of staff members, the same and that includes disciplinary action.

Performance management, merit, and incentive bonuses, and all aspects of personnel management still exist and must be executed with the advanced analytics and AI team.

There are aspects of human behavior that cannot be tolerated, and need to be avoided, or reacted to when they occur. If these aspects cannot be avoided or managed in a professional manner...

Labor market dynamics

As of late 2019, the United States is experiencing one of the longest economic expansions on record. The current unemployment rate in the United States is below 3%. Hiring practices have needed to evolve to ensure that employers can continue to find employees. Of course, this will change as the economic cycle ebbs and flows, but at this time we have an opportunity to bring in talented, skilled, and committed employees who may have been sidelined due to disabilities, personal situations, or other unfortunate life events:

With the national unemployment rate now flirting with a 50-year low, companies are increasingly looking outside the traditional labor force for workers. They are offering flexible hours and work-from-home options to attract stay-at-home parents, full-time students, and recent retirees. They are making new accommodations to open jobs to people with disabilities. They are dropping educational requirements, waiving criminal background...

A fit to be found

When I heard the phrase "fit to be found" a few years ago, I thought that this was absolutely ridiculous and the idea held no truth, but upon reflection and numerous opportunities to revisit the idea, I firmly believe that this concept is true. For each person, there is a perfect job or role and for each role or job, there is a perfect person to fill that role.

For each person that you have on your staff – or possibly this applies to you and your current role, every day that someone remains in a job that they despise, dislike, or do not want to do, they are keeping that job or role from the person who would absolutely love to be doing it on a daily basis. And for each day someone is in a job that they do not enjoy, they are not in the perfect job for them. I have stood up in front of multiple companies, divisions of Fortune 500 firms, and teams that I have led and outlined this premise.

I follow the explanation of the premise outlined...

Evolved leadership is a requirement for success

On my multi-decade journey of driving innovation in and through analytics, not just at the technological level, but at the practical, day-to-day level, I have been constantly reminded of the critical need for company leadership teams to understand, support, and sponsor corporate efforts in managing data as a strategic asset and creating analytics to drive transformation. Every organization that undertakes this transformational effort requires engaged and informed leadership that understands and views this as a long-term, strategic, organizational change management process.

"We need to become a focused medicines company that's powered by data science and digital technologies," [7] said Vas Narasimhan, CEO of Novartis AG, in an interview with the Wall Street Journal in February 2018. For every CEO that thinks like Mr. Narasimhan, there are multiple CEOs who have little to no idea what data and analytics can do for their...

Continual learning and data literacy at the organizational level

Advanced analytics and AI teams can do great work and deliver impressive models, but if the front-line workforce is not trained, upskilled, and directed to implement and use the new processes, models, and insights, then it is all for naught:

As AI tools become easier to use, AI use cases proliferate, and as AI projects are deployed, cross-functional teams are being pulled into AI projects. Data literacy will be required from employees outside traditional data teams—in fact, Gartner expects that 80% of organizations will start to roll out internal data literacy initiatives to upskill their workforce by 2020. [8]

In Gartner's third annual chief data officer survey, respondents said that the second most significant roadblock to progress with data and analytics is poor data literacy, rooted in ineffective communication across a wide range of increasingly diverse stakeholders. Data and...

Defining a high-performing analytical team

What does it mean to have a high-performing advanced analytics and AI team?

It means that the team is staffed with the highest-caliber team members that you can attract, afford, and retain. The team is cohesive and collaborative and willing to review the projects of each other and sub-teams. The team members are willing to work together for the greater good of the whole team.

The advanced analytics and AI team members connect professionally with executive and senior managers across the entire company. When the analytics team interacts with senior managers and executives, the staff members outside the advanced analytics team have a positive and assured reaction to the team members. The advanced analytics team members work in a collaborative manner with the subject matter experts and each group respects the other and builds trust across the teams.

The advanced analytics team presents initial results with confidence and receives...

The general data science process

Data science projects have a general process that the majority of well-run projects follow. Let's outline the overall data science approach to a project to ensure that we have a shared understanding of the approach. The structure of the team is irrelevant to this process. Any data science team will execute a project process for most data science-related projects that are similar to the following list of steps:

1. Project ideation
2. Engagement with project sponsors and subject matter experts
3. Project charter initiation
4. Project charter refinement
5. Project management
6. Convening team meetings
7. Obtaining internal and external data
8. Testing various analytical techniques
9. Building analytical models
10. Designing the user interface (UI) and user experience (UX)
11. Presenting interim results
12. Discussing the level of success or failure in the modeling process
13. Planning for the testing of models...

Team architecture/structure options

In my mind, most concepts exist on a continuum. Building a successful advanced analytics and AI team is typified by two approaches that inhabit the two poles of the relevant continuum – Artisanal or Factory:

Figure 2.1: Artisanal and Factory team structure comparison

The Artisanal team architecture/structure

Let's start with the Artisanal approach.

The Artisanal approach is where the data scientists are the owners, managers, experts, and driving force behind their projects.

The data scientists design and execute every step of the process. The data scientists engage with the project sponsors, the subject matter experts, internal and external consultants, syndicated data providers, and any other individual or group that has a role to play in the project.

Data scientists capable of executing and managing the artisanal approach possess exemplary communication skills, are open to listening to a wide range of...

The implications of proprietary versus open source tools

Over the past 20 years, there has been a significant change in technology that enables teams and individuals to design, develop, and deploy analytical applications. The change has been in the evolution and refinement of open source software and related platforms.

20 years ago, there were only proprietary software offerings from SAS, SPSS, Statistica, Minitab, and others. An entire generation, or possibly two generations, of psychologists, social scientists, mathematicians, and analysts grew up using these software systems in undergraduate- and graduate-level academic programs. When entering the business, research, and governmental workforces, those people brought their favorite tools with them.

More recently, open source systems like Knime, RapidMiner, and others offer community versions for free. In addition to the many open source tools and community versions available, the rise and evolution of the R and Python languages...

Summary

Building an advanced analytics and AI team is the same as building any other high-performance team in several respects, but in other crucially important respects, it is the converse of what you would expect. There are also nuances and subtleties that, even today, elude executives and senior managers.

Analytics teams, and those individuals that comprise those teams – the highly talented and skilled people involved in scoping, designing, building, testing, implementing, deploying, and in some cases, maintaining advanced analytics applications, systems, and environments – are unique.

In this chapter, we have examined the organizational context and considerations that may not be obvious when building an analytics team. We have provided a framework to use when thinking about the type of analytics talent that you may want to hire and how that talent can come into the new organization and have a positive impact in a matter of weeks or days. We have outlined...

Chapter 2 footnotes

1. Dell Autism Hiring Program, Dell website, January 3, 2020, https://jobs.dell.com/neurodiversity
2. Why Dell Is Making Neurodiverse Hiring A Priority, October 16, 2019, Danielle Hughes, https://www.catalyst.org/2019/10/16/why-dell-is-making-neurodiverse-hiring-a-priority/
3. Ibid.
4. In a Tight Labor Market, a Disability May Not Be a Barrier, September 5, 2019, Ben Casselman, https://www.nytimes.com/2019/09/05/business/economy/recruiting-labor-force.html
5. Ibid.
6. Ibid.
7. Novartis CEO Steers Drug Maker Back to R&D, February 2018, Wall Street Journal, https://www.wsj.com/articles/novartis-ceo-steers-drug-maker-back-to-r-d-1518962400
8. 8 AI trends we're watching in 2020, January 7, 2020, Roger Magoulas, O'Reilly Artificial Intelligence Newsletter, https://www.oreilly.com/radar/8-ai-trends-were-watching-in-2020/
9. Smarter with Gartner, Gartner Keynote: Do you Speak Data?, March 5, 2018, Christy Pettey, https://www.gartner...