- Data scientists need a tailored portfolio of projects that they own and manage to have a sense of autonomy.
- The top skill or personality trait a successful data scientist can possess (and should possess) is curiosity.
- Managing a successful analytics team and individual analytics professionals is different than managing any other type of team.
- Data and analytics will be ubiquitous in the very near future.
Analytics teams are different than any other team in the organization and analytics professionals are unique variant of creative professionals. Providing challenging, interesting and valuable work in the form of a personal project portfolio of work for a data scientist can be done and needs to be done to ensure productivity, job satisfaction, value delivery, and retention.
The interview in detail:
1. What are the fundamental concepts of building and managing a high-performing analytics team?
It is critically important to remember that data scientists are creative and intelligent people. They cannot be managed well in a command-and-control environment.
Data scientists need a tailored portfolio of projects that they own and manage to have a sense of autonomy. If they have a portfolio of projects and can manage their time and effort, the productivity of the team will be much higher than what is typically seen in teams managed in a traditional manner.
The relationship of the analytics leader with their peers and executives of the company is critically important to the success of the analytics team.
It is very important to realize that most analytics project fail at the point of where analytical models are to be implemented in production systems.
2. Tell us about your book, Building Analytics Teams. How is your book new and/or different from other books on Data Analytics?
Building Analytics Teams is focused on the practical challenges faced by people who are building and managing high performance analytics teams and the staff members who make up those analytics teams.
The book is different from other books in that it examines the process of building and managing a team from a holistic view. The book considers the organization framework, the required processes, the people, the projects, the problems, and pitfalls.
The content of the book guides the reader through how to navigate these challenges and provides illustrations and examples of how to be successful.
The book is a “how to” guide on how to successfully manage the analytics process in a large corporate environment.
3. What was the motivation behind writing this book?
I have not seen a book like this, and I wish I had a book like this earlier in my career.
I have built a number of analytics teams. While building and growing those teams, I noticed certain recurring patterns. I wanted to address the misconceptions and the misperceptions people hold about analytics teams.
Analytics teams are unique. The team members who are successful have a different mindset and attitude toward project work and team work. I wanted to communicate the differences inherent in a high-performance analytics team when compared to other teams.
Also, I wanted to communicate that managing a successful analytics team and individual analytics professionals is different than managing any other type of team.
I wanted to write a guide for managers and analytics professionals to help them understand how the broader organization views them and how they can interface and interact with their peers in related organizational functions to increase the probability of joint success.
4. What should be the starting point for data analytics enthusiasts aiming to begin their journey in Data Analytics? How do you think your book will help them in their journey?
It depends on where they are starting their journey.
If they are in the process of completing their undergraduate or graduate studies, I would suggest that they take classes in programming, data science or analytics.
If they are professionals, I would suggest that they take classes on Coursera, Udemy or any other on-line educational platform to see if they have a real interest in, and affinity for, analytics.
If they do have an interest, then they should start working on analytics for themselves to test out analytical techniques, apply critical thinking and try to understand what they can see or cannot see in the data.
If that works out and their interest remains, they should volunteer for projects at work that will enable them to work with data and analytics in a work setting.
If they have the education, the affinity and the skill, then apply for a data science position. Grab some data and make a difference!
5. What are the key skills required for someone to be successful working in Data Analytics? What are the pain points/challenges one should know?
The top skill or personality trait a successful data scientist can possess (and should possess) is curiosity. Without curiosity, you will find it difficult to be successful as a data scientist.
It helps to be talented and well educated, but I have met many stellar data scientists that are neither. Beyond those traits, it is more important to be diligent and persistent.
The most successful business analysts and data scientists I have ever worked with were all naturally and perpetually curious and had a level of diligence and persistence that was impressive.
As for pain points and challenges; data scientists need to work on improving their listening skills, their written & verbal communication and presentation skills. All data scientists need improvement in these areas.
6. What is the future of analytics? What will we see next?
I do believe that we are entering an era where data and analytics will be increasing in importance in all human endeavors. Certainly, corporate use of data and analytics will increase in importance, hence the focus of the book.
But beyond corporations, the active and engaged use of data and analytics will increase in importance and daily use in managing multiple aspects of – people’s personal lives, academic pursuits, governmental policy, military operations, humanitarian aid, tailoring of products and services; building of roads, towns and cities, planning of traffic patterns, provisioning of local federal and state services, intergovernmental relationships and more.
There will not be an element of human endeavor that will not be touched and changed by data and analytics. Data is ubiquitous today and data and analytics will be ubiquitous in the very near future.
We will see more discussions on who owns data and who should be able to monetize data.
We will experience increasing levels of AI and analytics across all systems that we interact with, and most of it will be unnoticed and operate in the background for our benefit.
John K. Thompson is an international technology executive with over 30 years of experience in the business intelligence and advanced analytics fields. Currently, John is responsible for the global Advanced Analytics and Artificial Intelligence team and efforts at CSL Behring.