SVP and Global Product Manager of Business Data Analytics & Digital Innovations
On this episode, Chris and Cindi discuss data literacy as a thought process that is nurtured by a good old-fashioned willingness to take things apart and put them back together again, what common data sense is and how it can be taught to people without shaming them, and why "the five whys" are an invaluable technique for solving almost any problem.
They also examine the real -- and counterintuitive -- purpose of self-service analytics. Plus, Cindi and Chris explain why data's role as the new oil is only as good as knowing what engine can use it.
All that and more on today’s episode with Chris Powers.
What skills prepare someone to make the leap from customer service rep to an SVP in a major corporation's innovation group, and how does such a foundation serve the needs of the company and its clients, alike?
I think a lot of it just starts with who you are as a person -- your inquisitiveness, the way you think and look at things. ... When I was two, I started playing the piano. By the time I was four, I was taking the television apart and putting all the transistors and tubes and stuff on the floor, and Mom was just like, 'Well, when you put it back together, it just better work.' There was no scolding. I think that encouragement helped at an early age. When I got my first car, it was like, 'Well, how does this thing work?' ... I didn't realize for most of my journey that I was on a path of data literacy and understanding how things work.
The ultimate goal is for our clients – and in general, anybody that's in the data space – for the people who need those insights to get them. But if you don't understand what they need the insights for, or how everything breaks down, then you're just shifting data from one point to another without actually understanding the mechanics behind it. Knowing how to read a graph isn't data literacy. It's a skill, but it's not necessarily understanding what action you need to take when you see it or where that data's coming from. And you have to be inquisitive to interrogate your data.
Sometimes clients know what they want, but they don't know how to ask for it. This is where having an understanding of the thought processes that drive their expectations can make a world of difference, as Chris illustrates from his time working in retail.
A customer had come in and they were looking for VCRs. [I said] 'Oh, they're over here against the wall, by the TVs.' Then after five or 10 minutes wandering around by the VCRs, they came back to me and they said, 'I don't see any VCR tapes.' We had them by the register, like in most places you check out, those are add-ons. So that was my first recollection of somebody asking for something that wasn't what they wanted.
So it made me realize that ... [sometimes people] know what they need ... but they might not be asking for the right thing, and you have to be inquisitive. You have to go down that path of the five whys.
I went to management and I said, 'This isn't the first customer that's come in and asked for VCR tapes, and they're just making an assumption that they're next to the VCRs. Why do we always have them at the register? We should put them over next to the VCRs where the customers think they are.' We did that, and the following month we had a 480 percent increase in sales. And then the district wants to know: 'What is this store doing?
Citigroup is a massive organization. From a financial services perspective, what are the top data sets or business questions Chris works with?
Being a global bank, one of the biggest things is the fact that you have global data. The number one thing that pretty much drives most businesses is how you're servicing your clients. So the biggest data that we have to deal with is what are we doing in the different spaces, in the different markets. Our clients want to know how they're doing, how they're performing, where are our transactions, if they're moving into new markets, out of markets, how to move things between markets. Everything you could possibly think of from a financial perspective is there and is generating data.
Every little thing that happens around the world, and even at home, contributes to movements around the market. Or new players enter, or players leave, everything has a direct impact, and then you see that in the data. So the more that you can interrogate your data and understand what happens if volumes are increasing or decreasing in a particular market, [the more you can do with that data].
How do organizations turn data into meaningful impact?
Trying to bring the data to the people or the people to the data, whichever direction are you going, you start with having the data, looking at it, making decisions, and those decisions bring up more questions. So what you want to do is figure out: what questions are you not asking that you should be? That's where you look at the tools and you say, okay, the business comes, they ask for data, we get the data, we give it to them, then they can make sure that we're delivering what we promise to the client. They can make sure that our KPIs are where we expect them to be. But then you can start looking at things that you may not have thought about and start drilling down into that data.
So one of the things we were trying to solve for is with a large amount of data, what are things we're not asking of our data, where we can get some insights today that we didn't think about yesterday that allows us to do something new tomorrow?
If someone says "We don't need another BI tool," how does Chris make the case for innovation?
Start with what you're solving for. If you have a tool and you need that tool to do something, the first thing you want to do is look at the tools you already have. And are you taking advantage of what you have and are you using those tools to solve that problem? And if not, then what's the differentiator between the tools that allow you to have that well-rounded toolkit?
"It's the five whys on the pushback as well. If you're pushing back because it's a financial consideration, then that's a different conversation than if it's a pushback because you think one solution is better than another. I keep mentioning data literacy, but that's another aspect of being able to argue with data. So if you get that pushback, it can't be protectionism. It can't be one tool over another because you're invested in that tool emotionally.
What do self service analytics and teaching managers to "do their own fishing” have in common?
Nobody wants to take the steps in between; they just want to get the data. I am not convinced that we've reached a point where self-service would be successful because I know how to drive a car, but if you put me on a race track and gave me a Formula One car, I'm not driving it! Or, if I am, I'm going 40 miles an hour around the curve. I don't have that skill set. So I think we need to build up the data literacy -- or the skill of working with and understanding your data -- so that you can be self-service.
Chris points out the counterintuitive purpose of self-service that a lot of us are missing.
The goal of self-service is really to get data to the person who needs it as quickly as possible, but a lot of people misconstrue that as reducing client touchpoints. You actually want to increase client touchpoints because -- just look at your data. The more you communicate with your clients, the better your NPS scores are, right? You have higher levels of customer satisfaction the more you actually talk to your customers. If you can figure out how to get the information that the clients need to [them] as quickly as possible ... you have the time to spend making the touchpoint with the client about the things that are impacting them [and] you're actually reducing your overhead and ... providing better service.
On bringing in new data and ensuring it gets to the people who need that data to make decisions.
We talked a lot about data literacy, but there's domain expertise too. ... You want to take the data that you're getting and you want to marry it with the people that have that expertise, because data doesn't do anything. We're talking about data being the new oil, [but] if I gave you a cup of oil, it doesn't do anything. You've got to do something with it.
Recognizing its tremendous importance, what is Chris doing at Citigroup to help improve data literacy for the overall industry?
We bring in different vendors and we do [virtual] workshops with our employees. We try to show people how they can work with their own data so that they understand it is a journey that they can take. [We try to] keep it simple -- I like to call it common data sense. We all have common sense, but we don't always have common data sense, like, ‘Should I be doing it this way or that way?’ You build that trust, and then once you have the trust, the journey is much easier.
Chris Powers has been at Citigroup for the last 24 years. In his current role working with client experience data, he focuses on providing the tools and insights to interpret the data so people can be empowered to make the best decisions for their clients. But for him, it is not just about the data. People tell stories with their data, and to do that effectively, they need to be able to understand what their data is telling them. Chris works to create a community of people that can effectively work and communicate with their data in order to build a data-driven culture. He doesn’t just do this inside Citi.
Chris is one of the founding members of the Tampa ThoughtSpot user group and is also a 2020 Qlik Data Luminary, creator, and co-organizer of the Tampa Qlik Meetup group. Since his childhood, he has had quite the journey to get where he is today. Learning how to overcome his own data anxiety, navigating through a rollercoaster of education decisions, and putting his sometimes exhausting questioning of "But why?” to good use. He spent more than 10 years in customer service before finally joining Citi. Now Chris uses the unique experiences from his journey to help people with common data sense.