Managing and Growing an Analytics Team

Nothing is as powerful as an idea whose time has come.

—Victor Hugo

After completing the previous chapter, you now have a good idea of what it takes to build a high-performing advanced analytics and AI team. It is obvious that after building the team, you also need to begin to manage the team to build team cohesion, connect the analytics team with stakeholders in the organization, and to begin the journey of delivering operational improvements through data and analytics.

In this chapter, we will examine the hiring process, the varying types of talent that you should consider adding to your team, the organizational dynamics of building a team, stakeholder management, and a number of other aspects that are critically important to a solid start to managing and growing your new team.

Managerial focus and balance

Managing and growing an advanced analytics and AI team is rewarding, challenging, and one of the most gratifying and frustrating endeavors I have ever been involved in. I would not trade my experiences and history in hiring and managing these brilliant minds for any other professional experience.

I have had the experience of being directed to manage groups of people who have been involved in executing highly routine work. I am not sure how people maintain interest in managing these groups and the work those team members execute. Also, I am unsure how people who do this work find it engaging and interesting past the first or second cycle of the repetitive process that is required of them, but we, as a society, are fortunate enough to have people among us who will do this work, but these are not the people you want on your analytics team nor would they want to be involved in, or qualified for, the highly variable, intensely challenging work of an advanced...

Sponsor and stakeholder management

Your personal success and that of the analytics team lies partly in your ability to connect and effectively partner with a wide range of people in the organization. The skills you possess, employ, and deploy in the area of emotional intelligence are paramount in this aspect of your work.

In a global organization, if you have a global role, you will need to meet with a substantial number of people, and depending on the culture of the organization, you may need to meet with those people either in one-on-one settings, small departmental groups, or in large auditorium/stadium settings, or perhaps in all of those settings. In a previous leadership role, I ran the global advanced analytics and AI effort and the associated teams. In the first year, I held over 630 meetings around the world. Those meetings included C-level executives, division managers, team leaders, individual contributors, external vendors, supply chain partners, and customers/patients...

An open or fixed mindset?

As we now have four generations in the workforce, a more collaborative approach to all phases of projects and work overall is required. We do see some leaders in organizations being more disconnected from the reality of the workplace than we do in other areas, but this dynamic is not a function of age. Of course, it can be, and we do see a correlation between age and an unrealistic view of how the workplace operates, but it is more a product of being closed and narrow-minded rather than having attained a certain age.

How do you know which end of the spectrum people are operating from? How do you know if they hold the detestable view that they know better than almost all others? It is an important element of their world view to understand. People who feel that they are smarter than others have a wide range of views and beliefs that are less than optimal in the workplace.

One way to discover how people think is to ask questions. Sounds simple, but,...

Productivity premium

There has been a great deal written about the productivity gap between the most talented and those possessing less talent. For the most part, it is true that highly skilled, talented individuals can produce 10 to 20 times more, and better, work than those at the bottom of the scale [1]. This discussion started in the late 1960s and was focused on programming when the dialog began, but research over the past five decades has illustrated that this phenomenon is prevalent in a wide range of human endeavors. "A study by Norm Augustine found that in a variety of professions – writing, football, invention, police work, and other occupations – the top 20 percent of people produced about 50 percent of the output, whether the output is touchdowns, patents, solved cases, or software." [2]

This dynamic is the same and may even be more pronounced in advanced analytics and AI teams. Talented and skilled people are better at all aspects of the job...

The rhythm of work

As discussed in Chapter 1, An Overview of Successful and High-Performing Analytics Teams, high-performing analytics professionals are different, in a good way, but it is also true that high-performing analytics teams are the same as all teams; no one wants to work hard and see a team member who does not. However, that is not to say that everyone needs to be and should always be working at 100% utilization; no one can sustain that level of work or that pace of delivery. Such an expectation is unrealistic and can be cruel if pushed too far.

After working for approximately 5 years, about the time that I completed my MBA, I began to view my career as a marathon that requires a moderate and steady pace. Before that realization, I worked full out, all the time; each day was a sprint. Let's be clear: I am not a marathon runner and I am only using the marathon as a metaphor; I am not commenting on people who run marathons competitively or even as an area of casual...

Personal project portfolio

Junior data scientists will likely come from the intern and co-op participant pool. Hiring people for full-time roles who have spent time with the analytics team and have been exposed to the broader organization is a much better way to hire full-time staff as compared to the traditional hiring process, especially in such a heated and frothy market as the data science market is today and looks to be in the foreseeable future.

If junior staff members do come from the intern and co-op participant ranks, then you understand their hard and soft skills and you know what they can and cannot do well. You, and they, understand the team dynamics and type of work and the cadence that is expected from them and the team. The ramp-up time to productivity is shorter and less time-consuming.

Junior members of the advanced analytics and AI team should be expected to undertake one main project and one or two service requests, to engage with the Community of Practice...

Managing team dynamics

Hiring and managing a high-performance analytics team presents interesting and subtle challenges. There are areas where you have options to choose between multiple courses of action and there are areas where it may seem like you have options, but in reality, you do not.

One of my primary objectives in the upcoming sections is to save you time, effort, and in some cases anguish. Read on for guidance on how to make progress in hiring and managing in the most effective manner possible.

The front end of the talent pipeline

Interns and co-op participants are a viable and valuable way to grow your advanced analytics and AI team. One aspect of internship and co-op programs that I feel strongly about is that you need to pay participants. Pay them the fair or market rate for top talent relative to their tenure and experience, which admittedly is almost non-existent at this point in their careers, but it is hoped and expected that the people being brought into internship and co-op programs hold great promise as the next generation of new staff members on your analytics team, correct? The trend over the past few years for unpaid internships is a ridiculous approach to what should be a relatively serious element of the talent acquisition process. If you are going to request or expect that someone undertakes productive and valuable work, why would you not pay them? And if the "work" you are asking them to do is meaningless, why would you bother?

If you can afford...

It takes a team

Talented and skilled individuals are better at collaboration. In general, they have a more evolved world view or mature understanding of human nature. They are less possessive of the ideas that they may have generated or those that they may have borrowed in the process of understanding and solving multi-step, interconnected, and interrelated challenges. This is important for several reasons.

First, most challenges that are appropriate for the advanced analytics and AI team to solve are mission-critical, difficult, and wide-ranging, thereby requiring more than one person to develop solutions to them. By nature, the challenges faced by you and your team are best solved by a team, or at least a sub-team, and hence collaboration and communication within the analytics team and between the analytics team and the functional team that has "hired" your team to solve their challenge are required and crucial to the success of the majority of projects.

There...

Simply the best

One of the most valuable, unique, and rare qualities individuals that make up a phenomenally successful advanced analytics team possess is the ability to understand the constituent elements that encompass, comprise, and come together to cause or perpetuate an outcome.

On the face of it, most people think that they know why processes happen, what the process actually is, and what the outcomes are, but do they really know? Are they truly aware of the nature and driving forces of why processes work and why a company operates in a certain way? Typically, "common knowledge" is not accurate or a true representation of how the world works.

Many people operate on intuition and gut feelings. A number of these people have been world leaders and have written best-selling books on the topic of managing in this manner. The problem is that these leaders are the exception, not the rule. The majority of people are operating under misguided perceptions of how processes...

Organizational maxims

I have learned valuable lessons over the decades of being an analytics professional and manager/leader.

These lessons are better understood and acted upon rather than pondered. In the next three sub-sections, I will lay out a few of these lessons that apply in a global context.

Take these as truths.

People do not change in the time required

I know that this is a controversial statement and one that raises the ire of some people but hear me out. You and your team are tasked with selecting, undertaking, and succeeding with projects that will drive positive change in the organization. This is your responsibility and you are accountable for the achievement of the goals and objectives. You have about a year to execute on the first cycle that results in positive and measurable change.

People can change, but usually the window for change at a deep personal level, which is what is needed to affect the personality traits that are problematic, is measured...

Summary

In this chapter, we covered a wide range of topics that set the context and provide a constructive corporate environment to begin to build a high-performing advanced analytics and AI team. As the leader of this new team, it is important that you connect with sponsors (executives), stakeholders (management), and subject matter experts (functional staff) and have a positive, collaborative, and cooperative relationship with all those groups.

It is crucial to your success and that of your new team that the executives and their downline organizations embrace analytics and engage in the process of building analytical applications that will be the leading edge of change and transformation in processes, people, products, and more.

Hiring the right people and ensuring that the leadership is oriented to an open mindset, ready for experimentation and change, are necessary environmental elements to evoke and realize the benefits of advanced analytics.

You are now aware of...

Chapter 3 footnotes

1. A good programmer can be as 10X times more productive than a mediocre one, December 2013, multiple authors and contributors, https://softwareengineering.stackexchange.com/questions/179616/a-good-programmer-can-be-as-10x-times-more-productive-than-a-mediocre-one
2. Ibid
3. The Scorpion and the Frog, https://en.wikipedia.org/wiki/The_Scorpion_and_the_Frog