TDWI | Training & Research | Business Intelligence, Analytics, Big Data, Data Warehousing

5 Minutes with an Analyst: Randy Law, Analytics and Insights

Great data scientists need to be open to a wide variety of perspectives.

• By James E. Powell
• November 3, 2016

Randy Law is the vice president of analytics and insights at Market Force Information where he leads a team engaged in customer experience and other projects with multilocation international clients across several industries, including quick service restaurants, casual dining, retail, grocery, convenience, and hospitality. We asked him about his work as an analyst and what it takes to be successful.

UPSIDE: What's the one thing you wish people knew about your job?

Randy Law: How rewarding it is! The predictive analytics industry provides such a diverse set of opportunities to help clients solve core business problems and answer complex practical questions. These opportunities allow us to talk with clients about their business and the data they have available (or could acquire) to help them become a better business.

I personally feel tremendous gratitude when I have the opportunity to develop professional relationships with our clients while also getting to work with some of the most complex and challenging data sets imaginable. I'm able to discover insights that our clients can act upon to better serve their customers and stakeholders.

Are you working on anything interesting right now? If not, what's your dream project?

Yes, I am currently leading a team of analysts on a major UK retail client project that is very compelling. Our client is trying to understand how their brand standards for operational excellence across all of their store locations impact the customer experience.

For example, does associate engagement with customers on the fashion sales floor have a bigger influence on customer satisfaction than associate assistance provided in the fitting room? If yes, by how much? Once we have the full picture, we will help them update their operational metrics and relative weighting so they can optimize their messaging and encourage their employees in targeted ways that enhance the customer experience.

What's a personality trait you think people need to succeed at your job?

Openness. Great data scientists need to be open to a wide variety of perspectives. As with most professions, we may all have the core knowledge, skills, and abilities to do high-quality work, but we also tend to gravitate towards using the tools and techniques that we were trained on (sometimes, years ago) and with which we have achieved success consistently. That works for a period of time, but the most successful data scientists are the ones who actively seek out new and innovative frameworks and techniques to provide better client service over time.

For example, our analytics team already uses some core machine-learning tools for discovering insights for our clients. However, we know that this part of the industry is exploding and we must harness these tools and techniques more aggressively to provide the best value possible to our clients and to stay ahead of our competition, so it's an area where I've been investing more time.

What's a typical day like for you? Do you work mostly with a team or mostly alone? Which do you prefer?

Typical day -- what's that? All kidding aside, most days involve a wide variety of activities, from talking with clients about their business problems to writing proposals. Other tasks could include acquiring, cleaning, and preparing data sets, conducting analyses, summarizing results in PowerPoint and/or facilitating a discussion around analytics results with clients, and engaging in action planning.

As the analytics department leader at Market Force, I may do a little more of the proposal work and some client conversations than others on the team, but all analysts here do all of these activities to some degree. Most of my work is done in conjunction with other team members and I encourage everyone on the team to do the same.

We all need our focus time "in the zone" to power out the core parts of our analytics plans for our clients, but we also need to be deliberate, thoughtful, and collaborative with our clients and with our fellow data scientists to think deeply about the client business problems we are trying to solve and questions to answer before diving into the details. I believe the best data science can only be accomplished through a collaborative dialogue with a variety of project stakeholders.

Which do I prefer? Both, actually. The fact that I get to do teamwork and alone-work makes this profession and my job such a joy.

What's your biggest pet peeve (abused buzzword, overhyped idea, etc.) and why?

"Just run some analytics on that and tell me what you find." My biggest pet peeve is when someone comes to our team with the belief that data science is a form of magic with data and that we should be able to discover some profound insights for them by being handed a data set -- or two or three -- without context or discussion.

It is true that some of our projects involve using truly bleeding-edge techniques on experimental data sets with little direction on what we expect to find. However, we consistently do our best work after having a thoughtful conversation with stakeholders about the business problems they are trying to solve and key questions they need answered.

After that kind of conversation (or two or three), and armed with just the right kind of data, we can usually uncover insights that are considered very valuable, reliable, and actionable, and help our customers make confident decisions about how to improve their business.

Randy Law has held consulting and leadership positions with Arrow Performance Group, Anton Collins Mitchell, McKesson, JD Edwards/PeopleSoft, Gateway Computers, Ernst & Young, the Texas Department of Protective and Regulatory Services, the National Aeronautical and Space Administration (NASA) and the Naval Training Systems Center. Law has a bachelor's degree in psychology and a master's degree in clinical psychology from Michigan State University, as well as a doctorate in personality and social psychology with an emphasis in quantitative methods from the University of Texas at Austin.