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New technologies are tools, not enemies

Interview with Martine George, Professor of Management Practice at Solvay Brussels School of Economics & Management

With almost 20 years of corporate experience on her professional odometer, Martine George felt she could create more impact and “pursue a more fulfilling professional path” by returning to the academic world. “I sought to contribute by offering a practitioner's perspective on data analytics, in a broader sense, to the upcoming generation of students.” Martine George now not only teaches young students as Professor of Management Practice at Solvay Brussels School of Economics & Management, she also coaches executives and professionals (software engineers, experts in analytics…) who are going through significant transitions. “For me, the biggest challenge in the analytics value chain is transversality.”

If lifelong learning applies to anyone, it certainly does to Martine George, her LinkedIn-profile amply reveals. “I completed a PhD in Physics in the late 90s, focusing on machine learning at a time when it was less prevalent than it is today. Back then machine learning was considered more exotic than the hype it is nowadays.”

Yet, as a PhD you stepped away from an academic career to follow a corporate path.

Martine George: “When I entered the corporate world, I brought with me a strong scientific background, particularly in the field of machine learning, and was well-equipped with technical and quantitative skills that were suitable for a career in data mining. Throughout my corporate journey, I discovered a passion for data and cultivating a more data-driven perspective within companies. I aimed to assist them in making important decisions based on factual and evidence-based insights. Additionally, I developed a passion for nurturing and fostering talent, particularly in assembling teams of experts from scratch. At the time, this type of job was not well-established, requiring extensive efforts to search for the right individuals possessing the appropriate skills and competencies to form effective teams. It was a challenging endeavor that demanded a diverse range of qualities, ones that could not be acquired solely through a traditional curriculum.”

"By paying attention to the available data from customer service, coverage, usage, billing, and marketing, I could construct a comprehensive view of an organization"

Your journey from company to company was quite diverse.

“I transitioned into different industries, such as telecom and banking. You may wonder why I made these changes and choices. I initially worked in the energy industry, specifically in practical energy research as part of the R&D activities. The goal was to apply the knowledge and insights I gained during my PhD to an industrial context. This involved utilizing neural networks and other technologies to support decision-making processes in the energy industry. During this period, my curiosity to learn more was sparked, and it was then that I discovered the field of data mining. I sought to understand its essence, recognizing that it encompassed both technical aspects and a realm that I was less familiar with — business.”

“To illustrate my lack of knowledge, I will provide you with an anecdote: at that time, I wasn't entirely satisfied with my job in the energy sector. Therefore, I decided to explore new opportunities. In 2000, I joined Belgacom Mobile (currently known as Proximus), where I discovered that they had an excellent data warehouse due to the nature of their business, which generated a vast amount of data. With enthusiasm, I proposed the idea of establishing a data mining activity from scratch to the company. They were intrigued and interested in my proposition, and they agreed to pursue this initiative. When they offered me the opportunity to develop the idea further, I realized I lacked a deep understanding of running a business. However, I saw this as an opportunity to learn and understand business through the lens of data. By paying attention to the available data from customer service, coverage, usage, billing, and marketing, I could construct a comprehensive view of an organization.”

Perhaps, by viewing through the lens of data, you provided the company with a brand new perspective on its own strategy and operations?

“Around that time, text messaging was booming, with a volume underestimated by a factor of more than 10. Additionally, data mining started to play a crucial role in customer retention, particularly due to the government's consideration of number portability. Prior to this, there were clear distinctions between operators, especially in the B2B sector, making it difficult for businesses to switch operators and retain their client base. However, the telecom policies in Belgium shifted towards opening up portability, prompting a benchmarking analysis of similar policies in other countries. In some of them, there was a rotational rate of over 70%, indicating that companies risked losing their historical databases due to not anticipating this change. The historical operators, who held the largest share of the market, understandably became concerned. They realized the need to develop tools to anticipate churn behavior, and this became my initial responsibility in the project. The goal was to identify high-value clients with a high propensity to switch operators, despite being extremely profitable for their current provider.”

I sought to contribute by offering a practitioner's perspective on data analytics to the upcoming generation of students"

Then you switched jobs again?

“I indeed was also attracted to other industries as well. I also ventured into the financial services industry, where access to data was abundant. It was an industry with a pressing need for digital transformation. I was fortunate to be at ING during a period when they spearheaded a significant digital transformation, challenging the established oligopoly in Belgium. This transformation began around 2005, as they recognized the importance of changing their business model. They aimed to shift away from a branch-based model and embrace digital technology to streamline operations and offer simple products—things that we now take for granted. Through these experiences, I encountered diverse contexts of companies, industries, and varying levels of data accessibility, all of which enriched my understanding.”

What were the biggest challenges you encountered throughout your journey?

“One of the most significant challenges I faced at every company I worked for, was the concept of transversality, where interdisciplinary collaboration and integration were essential. It was in light of this challenge that I decided to return to academia, as it provided a platform to pursue endeavors that held greater personal meaning for me in the latter part of my career. I sought to contribute by offering a practitioner's perspective on data analytics, in a broader sense, to the upcoming generation of students.”

You returned to the academic world where you had to contribute to a new field of expertise?

“A new field may be an exaggeration, but yes, some ten years ago there was only one optional course for business intelligence in the curriculum of the Master of Business Engineering program. I was tasked with creating a mandatory course on the topic in the master in Management Science. The notion was to acknowledge that this subject would become a valuable asset for any business student in the future.”

"I think that the biggest challenge in the analytics value chain is transversality"

Are students aware of the importance of data in their future jobs?

“Students nowadays are becoming increasingly conscious of the importance of data. By the time they arrive in class, they have already learned some Python programming and other related topics, which is different from the curriculum in the past. My goal is to guide them towards a pragmatic understanding of how data can benefit them as future business professionals. It's crucial to expose them to the topic of data as multidisciplinary, encompassing technical aspects like programming, analytical tools, data quality, and database modeling. Additionally, the quantitative aspect is significant, drawing on the strong quantitative foundation at Solvay. We introduce them to concepts in machine learning, experimental design, and measurement. In addition to the quantitative aspect, we connect their background in business to analytics. We explore the types of problems that can be tackled with analytics, both strategically and tactically, and highlight the specificities that can arise across industries. It's essential not to overlook ethics, privacy considerations, and the integration of project management within the analytics context. Moreover, based on my field experience, I emphasize that the success of analytics depends not only on the technical aspects but also on effective communication of results and the capability to adjust the message to the audience (technical professionals, senior managers…).”

Can you elaborate on communication as a succes factor?

“The ability to bridge the gap between technical expertise and effective communication is paramount. This challenge arises because analytics is akin to a relay race where you need to pass the baton to different individuals, each with their own background, expertise and language, to ensure successful implementation and impact. I think that the biggest challenge in the analytics value chain is transversality. There are IT experts, quantitative analysts, statisticians, and business professionals involved, and ultimately, it is the decision-maker who determines whether the insights will be used or not. This moment of truth is crucial because if the decision-maker doesn't trust the analytics or the experts behind them, all the efforts may go to waste. It's a matter of trust and effective communication, speaking the same language at the same time. While other aspects are important, they are necessary but not sufficient conditions for success.”

"It's important to be clear about how technology can support progress and innovation in the future while being mindful of its limitations"

Do you consider yourself a role model for women in tech?

“From a university perspective, the gender balance in business-related fields is quite good. While there are some differences, I wouldn't say there are particular barriers, except for the lack of interest among some women. However, it's important not to generalize. More young women are genuinely interested and involved in these topics.”

“As for being a role model, well, I do not like this type of labeling. Anybody with interest in this topic will find his or her own way to contribute in this amazing multidisciplinary domain. I am passionate about the field of analytics, which I discovered 25 years ago, and I'm still incredibly excited about it. But for me, it's more about transmitting knowledge and ensuring that this topic is considered important for future generations. I also believe in being critical about how technology can bring both good and bad outcomes. It's important to be clear about how technology can support progress and innovation in the future while being mindful of its limitations.”

Do companies have a clear vision of what to expect from data analysis?

“Some companies invest a significant amount of time, space, and resources in purchasing infrastructure for modern data platforms without truly understanding why or having a clear, compelling, and articulated connection between their strategic objectives and vision. This leads to investments being made without a strong rationale. It's not necessarily difficult for smaller companies to have a strategy and vision regarding data. It often depends on who is leading the company.”

What skill sets do students need when they enter the corporate world, especially in data-related fields?

"In general, they need curiosity, adaptability, resilience, critical thinking, active listening and co-creation abilities. These are key aspects, particularly in data-related domains. They should also have sufficient training in data to approach it as a natural way of looking at things. With data being everywhere and easy access to the internet, new technologies like ChatGPT should be integrated into education as additional tools rather than seen as enemies of learning.”

"There are missed opportunities for organizations that fail to invest in data teams and provide adequate edcuation and training to bridge this divide"

Do you think companies that don't invest in data teams and education like this are missing out on significant opportunities?

“Many companies now recognize the need for data teams and good IT infrastructure, including data analysts, data engineering, analytics translators and many others. However, there sometimes exists a significant divide between these experts and the rest of the organization, as many employees struggle to understand how data can be effectively exploited to create tangible assets. This gap hampers the full utilization of data's potential within companies. Therefore, there are indeed missed opportunities for organizations that fail to invest in data teams and provide adequate education and training to bridge this divide. Senior management, including the CEO, plays a crucial role in shaping the culture and understanding the importance of data.”

“Creating a startup provides an opportunity to incorporate data as a core element based on the nature of the business. If data is crucial to the business model, it is essential to establish data collection practices from day one. Neglecting the attention to data systems, even if they are basic, can become a nightmare for a simple operation, especially for small businesses. Many companies with limited revenue struggle to allocate resources for organizing their data. They are not specialists in the field and require a user-friendly system that can be implemented quickly. This is a common need and can determine their success or failure.”

How does data shape success?

“For me, the key factor in determining the success of an initiative is its actionability and utilization. Generating insightful information from raw data is insufficient if the insights are not used by anyone. Some data scientists are very interested in the intellectual challenge rather than the practical application of their results. However, the true value lies in how insights support decision-making processes and influence outcomes. In marketing campaigns, for example, it is crucial to develop experimental designs beforehand to assess the impact of analytics-driven targeting compared to doing nothing or using random targeting. This allows for conclusive insights on the effectiveness of the campaign. The process of scaling up and improving analytics involves an iterative approach of learning by doing, experimenting, and drawing conclusions from well-designed experiments. It is a continuous improvement journey that requires never resting on analytical laurels. Fostering such a culture involves strong leadership and alignment across the organization. Today, I believe most companies in the financial and insurance industry is aligned with the importance of data and analytics.”

Martine's data

  • 25 / 25 years of passion for analytics.
  • 30 / As a lifelong-learner, I spend about 30 days per year on average to continue to train and learn in analytics, story-telling with data, coaching, positive psychology, coaching supervision, sustainability, teaching innovation, corporate governance, and many other topics of interest.
  • 300 / Minimum number of students per year since 2016 that I try to open awareness to the topics of analytics and all of their facets.
  • 31 / The demand for composite data analytics professionals will grow by 31% by 2030 (Forbes).

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