Data Science for C-Level Executives: Why Top Managers Can’t Do Without Data Know-How These Days

April 07, 2022

Why it is so important to ask the right questions in the age of Big Data

Relying on your gut and good instincts is a thing of the past; big data is what really matters today: Data are the hard currency of the future. Companies’ capacity to use them will decide about their success. And many a leader is going to have a tough time with that.

Harald Trautsch, CEO of Dolphin Technologies, and Axel Polleres, scientific head of the Professional MBA Digital Transformation & Data Science at the WU Executive Academy, explain why it’s not only CIOs/CTOs and CEOs who should know their data, but why data know-how may also come in handy for CFOs, COOs, CMOs, and heads of HR. They also share why handling big data is all about asking the right questions.

A woman, with a tablet in her hand, and a man stand together and look at the tablet
In the age of Big Data, it is necessary for CEOs and decision makers to have a handle on their data. Photo © shutterstock - Iokanan VFX Studios

The new gold rush is all about data. In the future, businesses will only be able to survive if they make full use of the opportunities offered by digitization. And digitization creates digital data. Many companies, however, still lack the know-how about the efficient use of data and the identification of the information that will really give them a head start.

Interdisciplinary Data Know-How at All Company Levels

Not only data experts and data scientists in IT departments but also, and especially, top managers should know how data can be collected and analyzed. They also need to be able to decide which data to collect in the first place. Axel Polleres, scientific head of the Professional MBA Digital Transformation & Data Science at the WU Executive Academy, explains, “Basic digital skills have become indispensable in all areas. Comprehensive data know-how has become an absolute must at all levels of a company.”

Harald Trautsch, a Gobal Executive MBA graduate and CEO of Dolphin Technologies, the market leader in the field of insurance telematics, holds an industry keynote on data science in the framework of this MBA program. In this lecture, he shares with the students how companies handle and use data in practice. For Trautsch, the interdisciplinarity of data science also fosters a better understanding of other areas.

Harald Trautsch Portrait

Harald Trautsch

  • Co-founder and CEO of Dolphin Technologies

Data science is a valuable tool that helps us to broaden our expertise and think outside the box. Being able to collect and analyze data is not sufficient, though. The quality of your data evaluations will depend on the questions you ask. That’s also why different people will glean different information from the same set of data – because they ask different questions.

This means that every executive and every decision-maker in a company, and particularly top managers, need to understand data. The focus and implications of a person’s data know-how will, of course, depend on the respective person’s position within the company:

Chief Executive Officer (CEO):Taking Decisions Based on Data

Top managers often have high expectations when it comes to data science: “It’s all about using algorithms and artificial intelligence so as to get as much out of available data as possible,” Alex Polleres relates. “The thing is, however: sometimes you have too little data for a meaningful analysis. In many cases, companies would have to collect data in a more systematic and comprehensive way to be able to answer their questions. Sometimes companies collect a lot of data which are not useful for their business. And data always come with a risk: namely, that unauthorized third-parties might get hold of them. Personal customer information is extremely sensitive. Companies should therefore ask themselves for which purposes exactly they want to collect such data”, Polleres advises. He explains that it doesn’t make sense to collect data just for the purpose of having them. It is not even efficient, as collected data don’t remain useful indefinitely.

A close-up of a tablet. One hand is holding the tablet, another is pointing at it.
Simply collecting data isn't enough - you also have to know how to use it. Photo © shutterstock - thinkhubstudio

“CEOs need data to be able to make the right decisions,” Harald Trautsch adds. “They will be well advised to use the notorious gut feeling and their entrepreneurial spirit only in those cases in which it’s impossible to make decisions based on data. In all other cases, they will need the right information gleaned from available data sources as a basis for their decisions. The right analyses and assessments of figures and KPIs can significantly improve entrepreneurial decision-making.”

Chief Information Officer (CIO)/Chief Technology Officer (CTO): Improving Data Security

CIOs and CTOs are expected to thoroughly understand all matters related to big data, data collection, and analyses. Harald Trautsch shares, “CIOs and CTOs have a significant influence on the way in which data from different systems are merged. They have the power to make sure that this is done in a meaningful way. Decisions on the architecture and structure of any services will be based on company data, third-party information, and the company’s basic strategy for handling data.”

Axel Polleres Portrait

Axel Polleres

  • Professor and Head of Institute | Department of Information Systems & Operations | Institute for Information Business | WU Vienna | Austria

Data governance is almost more important than data science. The CIO/CTO has to make sure that everyone across all levels within their company has a certain basic idea of data security.

Chief Finance Officer (CFO): Enhancing Financial Planning

CFOs are responsible for collecting and assessing appropriate financial data. “Financial experts tend to evaluate their data – turnover, costs, income – in retrospect,” Harald Trautsch analyzes. “They should, however, rather ask themselves how they can use lessons learned based on the available data for strategic financial planning. This requires a critical assessment and scrutiny of the data: Those who make decisions and establish business plans should be able to understand the underlying data. They should be able to answer questions like: Why should we invest X million euros in project B? Is our information and the underlying data even valid?”

Axel Polleres adds, “Many companies are not even aware of the fact that data have a monetary value. Data may give rise to costs and pose high financial risks – for example if the wrong people get hold of sensitive personal customer information or analyses produce wrong results due to the poor quality of the underlying data. IT infrastructures are often vulnerable to cyberattacks. Companies have to decide whether it is safer to store their data on their own servers or in a cloud.”

A woman holding a laptop and a man pointing at the laptop are standing in a server room
With sensitive data, it's always important to consider cyberattacks - what data can be backed up, where, and how? Photo © shutterstock - Gorodenkoff

Chief Operation Officer (COO): Optimizing Processes

“Data are indispensable, especially when it comes to operational tasks,” says Axel Polleres. “COOs may well use their creativity in order to make the most of the available data. That’s why they have to become actively involved in their companies’ data governance. When we teach students about data science, we make sure to point out its interdisciplinary nature. This should ideally also be reflected when data science is put into practice. While CTOs are expected to have the technical know-how, COOs should have an adequate knowledge of processes and structures. Harald Trautsch explains, “Data governance know-how is also essential for COOs. They have to understand process and production data to be able to use them as a basis for decision-making and adapt processes and KPIs, for example in the field of quality management, to the corporate strategy. Quality improvements do not necessarily result in a lower but in an optimized error rate. This way, COOs can make sure that their company meets the market’s needs and won’t lose market shares because the prices they charge are too high.”

Chief Marketing Officer (CMO): Avoiding Wasting Money on Expensive Campaigns

In the field of marketing, the use of valuable data offers a huge potential for better reaching a company’s target groups. Axel Polleres explains: “Data on pricing, seasonal effects, and customer journey analyses may help CMOs to better understand their customers’ behavior, attract the right customers, and build lasting customer relationships. Algorithms and methods, such as cluster analyses, can be used to fine-tune marketing campaigns to certain target groups, while customer segmentation allows for addressing their individual needs in a targeted way. Harald Trautsch warns, “Those who use the wrong data in marketing or don’t interpret data in the right way, may lose a lot of money.” He shares an example: “I once had a client who wanted to use a digital ad campaign to attract new customers for a smartphone app. His agency focused on the number of downloads without paying any attention to registrations or actual purchases. As a result, the campaign was continuously optimized for the wrong target group. The app only became a success once they had finally identified the right metrics among success indicators.”

[Translate to English:] A tablet in close-up. A finger points at it.
In marketing, you can do a lot with data, but you can also lose a lot of money if you misinterpret it. Photo © shutterstock - ESB Professional

Chief People Officer (CPO)/Chief Human Resources Officer (CHRO): Data Are not the Magic Bullet

In recruiting and human resources management, a lot of sensitive personal data are collected and stored. That’s why data security again plays an important role, the experts agree. Axel Polleres says, “Recruiters should not rely too much on algorithms and their data output. Standardized selection processes are still designed by humans, and though they may seem to be objective, there’s a risk that they are still biased. If algorithms are used to pre-select job applicants, rich CVs of interesting candidates may be rejected because they do not match the predefined requirements. Machine learning may also teach algorithms biases after all. And then nobody is responsible for the decision, because it was the algorithm that took it.” “Artificial intelligence does have many benefits, but it still has to be clear who’s responsible for making the final decision”, Axel Polleres explains. The issue of biases is currently an important topic of AI research, he shares.

Harald Trautsch adds that the implementation of HR measures also generates valuable data, such as the results of surveys among employees or 360-degree feedback. “Also here, it is important to take a closer look at these results: if X percent of my employees are not satisfied with their jobs, this still doesn’t mean that they want to quit. In many cases, a slight change of working conditions would already result in a measurable increase in the employees’ satisfaction. Again, it’s all about asking the right questions to get useful responses.”

In summary, the two experts agree: C-level executives with a digitization strategy and data related know-how will make all the difference when it comes to their companies’ success in the future.

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