Lutz Beck is the chief information officer for Daimler Truck North America. Beck has been with Daimler Truck for more than 17 years, serving in various roles throughout his tenure, including chief information officer of Daimler Truck Asia.
Beck is the keynote speaker at the 2022 Business Analytics Symposium, which takes place on Feb. 25 and is hosted by the College of Business and the Center for Business Analytics. This year’s theme is “With Great Power Comes Great Responsibility: The Ethics of Business Analytics.”
Here, we speak to Beck about what data can do for customers as well as how to manage it responsibly and ethically.
How should we manage the power that data gives to us? What ethical issues in data analytics are top of mind for you and your team?
It’s not so much about power as about innovating and improving on what we are doing with data and knowing what data adds value for our customers and ourselves. Talking about ethical components, privacy and ownership is key for everything we do. Additionally, it is important to use data, build analytical models on the existing data.
And, very important, when you work with personal data or when you work with customer data, you need to consider ownership for the data, meaning there needs to be a data-sharing agreement, which allows data usage. And always focus on value add for all parties involved.
We have lots of examples of companies collecting data and using it to make our lives more convenient. But if you come to a point where data is used not for value add, more for influencing, the ethical points come into play.
As a society, we have lots of data and information. This is good; we need it for innovation and improvements, but every person still decides what to do with personal data. And when you are working with big data, and when you are working with algorithms, there is a thin line to use data in the wrong way, and there, I see a clear-cut line where we need to say, no, it is unethical if you do that.
Do those kinds of concerns influence the products and services that your company is creating?
Of course, and there are strict rules around it. There are regulations around data internally, about how we are dealing with it, what we are doing with it, how we are displaying it, how we are sharing it, all those kinds of things. Everything is around this kind of rule set, and everyone needs to follow this rule set.
How does the company develop those rules? Are they created by legal teams in consultation with engineers and people in IT?
First, we have what we call data regulations or data governance. And this data governance needs to be in place to give you a legal perspective, to make sure that we are complying with all the regulations.
Second, there needs to be internal rules, in terms of how data is shared. So when you look at data that is open to everyone, you need to put the governance in place so you understand what kind of data you have and the structure of the data.
Especially, sharing is very important, because you need to have data ownership right in the company. And then you need to make sure the data owner actually gives access to data or declines to give access based on certain elements. So all this data governance, which we have in place, has to be followed by everybody in the company.
We have a chief data officer within my area. And then we have what I would call data stewards or data owners in the respective areas. And they all together are building the data board in the company, and they are deciding the rule set. And they are also looking that everybody is following this rule set.
How do you determine your customers’ data needs?
When you look at data, you need to understand what is valuable and what is not. So what can we use to provide our customers, our dealers, value add and better services? And then, of course, we have discussions with our customers and with our dealers to understand their needs. For example, we have fleet customers who might say, ‘It would be perfect for us to have a specific data set to secure uptime of their fleet. Is there a possibility to use the data?’ And then we create together with the customers the working model for the request.
We have a very high number of connected trucks. When you look at where transportation is going, customers need to know where their trucks are or what is happening with their trucks. Is there any service event coming up that influences the uptime of the truck? So we are using data in a way that benefits our customers. But of course, we are always considering data privacy and ownership.
How would you advise companies interested in data analytics to get into this field?
First of all, it is important that you acknowledge that data is one of the key assets in the company and, used correctly, you can add a lot of value.
Now we are creating an enormous amount of data. My take at the moment is that the percentage that you can actually use is very small. It will increase over time because the quality of the data will be better and better.
Let’s say you are in a smaller company, and you acknowledge there is a lot of value in the data you have. And then you ask, what is my internal data? What is my external data? You just need to define the data set which you can use adding services, being more efficient or driving smart decisions based on data. Therefore, you need to classify your data. You need to identify what is valuable data and then focus on that, to really understand the value of each specific data set. And then you can start giving data to your customers and using your internal data. But first, you need to categorize and understand the content of the data.
Where do companies get the people with those skills? Are they in-house or coming out of universities and technical schools?
For us, people are coming from universities and technical schools. But we also have people in the business who actually know the content of our data. I also work with students who have access to the latest tools. All the agreements are in place, and I give them my data. And they create valuable output. It’s a lot of fun to see them working, to see how committed they are to doing this. We were working with OSU on this program last year, and we got valuable results.
In your view, are students being properly educated to address ethical questions with data?
From a technical point of view, yes. I think it’s okay. And I like the OSU program. That gives students practical experience. They’re working with a real problem or real data analytics topics.
What I’m still missing a bit is this link to ethics as practiced in business and industry. I believe you learn the most when you have practical experience. This happens only when they have a real example and experience. Students can see there is a rule in this company, and they know why something is happening. But as a data scientist, you want to do everything. You want to have all this data and play with everything, and you want to get everything out there.
This is happening more and more, and it is in some ways like social media. For me, social media is a good thing, right? Everybody’s on social media. Now, as a CIO, I shouldn’t be on social media because of exposure or risk. I know what is done with the data. And I have good friends in the same field who are not on social media, and they do not even allow their kids to be on social media. People sometimes forget when they’re looking at social media because we are so used to everything. Now the data is available right away. And I do believe we will have more and more regulations there, because people say they don’t want their data everywhere.
It’s not just with social media. It should be with all data, and this is where we, as companies, need to act as well, because we can set the examples, and we can make it better. We need to set an example here, how to deal ethically with data. And of course, we need to improve or evolve over time. Because there will be more regulation. So we need to be on the mark to make sure that we are following the rules.
How do you see business and industry helping develop public policy in data analytics?
We in industry need to come together with legislation to find pragmatic solutions. We know we will work with data in a completely different way in the future. Now, what we need to define is how we work in a very practical way. We need to keep a high rule set.