Sitting with a Faculty Interview with Jamil Hebali

Please introduce yourself to the community.

JH: It's a pleasure to be here and share my experiences with all of you. My name is Jamil Hebali, and I am a Marketing Professor at EHL. Additionally, I’ve been working as an SBP coach for over 16 years.

With over 17 years of experience in the field, I'm excited to be here today. Throughout my career, I’ve taught various courses in marketing at EHL, including marketing strategy, operational marketing, market research, market intelligence, and datadriven business.

How did you first start at EHL and what brought you here?

JH: In 2006, I began my journey at EHL by teaching two key courses: Consumer Behavior and Market Besearch. Simultaneously, I started coaching SBPs (Student Business Projects).

From drawing from my own background in market research and experience working with a multinational company in this field, I was able to bring valuable expertise to SBPs. This expertise focused mainly on enhancing the way data is collected, analyzed, and utili zed for decisionmaking in businesses and companies worldwide.

Could you elaborate on what drew you to education?

JH: Education, in my opinion, holds immense value in its practical aspect. For instance, in the market research course, I believe in avoiding a purely theoretical approach because it is a technical course. Instead, I find it more e ffective to transfer competences and skills to students by showcasing real-life applications.

For that, since the start of my teaching career, I’ve incorporated various techni q ues and softwares to demonstrate to students how data is collected, cleaned, and analyzed. And these demonstrations are built upon real cases, enabling students to grasp the practical applications of concepts.

Let's delve into the role of data in the hospitality industry. You currently teach a course titled "Data-Driven Businesses." Could you share the main learning takeaway for your students?

JH: This course has undergone several name changes to adapt to the rapidly evolving field. Initially centered around big data strategy in the hospitality industry, it has transformed into "Data-Driven Business," reflecting the holistic nature of the subject.

In the past, the focus was on operational aspects, such as data analysis, deriving insights, and making decisions based on global data. However, as the field progressed, the approach expanded. N ow, it now encompasses more than just analytics ; it involves transforming organi zations into data-driven entities where every decision-making process is rooted in data.

So yes, data has become the capital driving businesses, surpassing monetary value. And companies must harness and explore data to create new opportunities, drive business growth, and deliver value to stakeholders.

Perhaps the main takeaway for students is to learn how to develop a data strategy. Companies are increasingly recognizing the importance of this strategy to maintain competitiveness in the market.

Take the example of Nokia. It was once a market leader in mobile phones but failed to anticipate the future and adapt. Their downfall was largely attributed to the lack of predictive analysis and failure to utilize data effectively. In contrast, successful companies thrive on data-driven management, enabling them not only to manage but also to anticipate the future. This forms the key learning takeaway from the course: developing a data strategy and leveraging data analysis for informed decision-making.

Throughout the course, we engage in practical exercises utilizing various softwares and algorithms, primarily R and Python. These tools enable us to analyze data and apply machine learning methods to transform data into actionable insights for overall improvement at a global level within companies.

The importance of data in the hospitality industry cannot be overstated. How much data is enough data for hotels to make informed decisions?

JH: You’re absolutely right. Data plays a key role in the hospitality and hotel sector. The industry generates a vast amount of structured data, and the challenge lies in effectively processing it by finding, extracting, cleaning, and preparing it for analysis. This processing phase accounts for over 50% of the entire data strategy process. Hotel businesses recognize that data has the potential to create value by offering opportunities for customer organization, loyalty strategies, competitive advantage, and more.

However, they often struggle with where to begin and what to prioritize in terms of utilizing the available data. The main challenge lies not in the availability of technology solutions but in developing a comprehensive data strategy. That said, it’s important for the hospitality sector to start by asking a fundamental question: What areas do they want to improve, and how can they leverage their available data?

By answering this question, they can identify areas for improvement through a simple analysis of their current situation. The next step involves assessing the types of data they possess and its location. Different categories of data, such as consumer data, brand data, market data, and competitive data, need to be understood in terms of availability and how they can be combined to create business opportunities. Companies that do not base their decision-making processes on data, or fail to develop automated datadriven systems, risk missing out on valuable business opportunities and falling behind in the market.

Small enterprises, particularly family-owned hotels, may find data collection and analysis challenging. What can these businesses do to optimize their performance? Is there a shortcut to data analysis?

JH: Great question! It’s essential for small enterprises to consider how they can adopt a data-driven approach, even with limited time, resources, or expertise. It’s not solely a technological matter; it requires a shift in mindset. They can begin by utilizing tools like Excel to implement a data-driven strategy.

Unfortunately, there is no magic solution or technology that can instantly transform a business into a datadriven one. Rather, this requires a change in mindset and thinking about how data can be effectively used. Now, this is all truly in the hands of people and managers. It's about using data to make decisions. As said, data can be used with simple tools like Excel to perform analysis and make decisions based on the results. Many companies mistakenly believed that investing heavily in technology and purchasing the best tools would automatically enable them to become data-driven. However, they often failed to utilize these tools because they lacked a clear understanding of how to leverage them effectively. This is a common mistake made by many companies.

To become a data-driven business, start by changing your mindset about your business. Assess the data you have, interpret and analyze it. Even if the data is limited, there are different stages in the evolution towards becoming data-driven. We begin with basic market research, progress to business intelligence, and now we talk about big data. The latest stage is data-driven decision-making, which is more strategic. It means that all decisions are based on data. The difference between these stages lies in the volume and quantity of data involved.

Lastly, there are various steps and technologies available to extract and analyze data. If you have a small amount of data, you could benefit from tools like Excel. For larger quantities of data, other tools may be employed, but the fundamental principles and mindset remain the same. It is a matter of adopting the right mindset.

You mentioned predictive analytics. Let's discuss the future of data-driven decisions in the hospitality industry in light of AI (Artificial Intelligence) software and solutions. What are your thoughts?

JH : With the existing AI software and applications, everything is set to change. The emergence of AI will revolutionize how we interact with data and conduct business. In the coming weeks and months, I firmly believe that the combination of AI and data will provide concrete solutions for managers and companies to enhance their business operations.

I have had interactions with marketing managers who expressed how tools like ChatGPT enable them to quickly prepare client communications. They can request specific topics, and within seconds, the content is generated. These advancements are helpful for managers in developing their communication strategies. This is just the beginning, and I anticipate significant advancements in the coming years that will provide added value accessible to anyone. The expertise required will not be limited to individuals with technical proficiency.

For instance, in some of my courses, we use AI and various algorithms, but coding consumes a significant amount of time and necessitates a passion for the subject. But with AI, the technical aspect can be handled, and the expertise required will be accessible to a broader range of individuals.

As we gather more data, there is a risk of falling into the trap of conscious bias, where we interpret patterns that align with our preconceived notions. How can we utilize data objectively to avoid this bias and ensure our data-driven decisions remain unbiased?

JH : For this, the human aspect is key. Technology is here to assist us, but it is important to use it properly to create value. And if we rush and move too fast, problems can arise. Trust between individuals is essential when dealing with data. While accessing data without proper authorization and making decisions based on it is easy, legal aspects are evolving, albeit not at the same pace as technology.

Eventually, we will have a framework that protects individuals, respects privacy, and benefits the economy. Balancing these factors is a complex issue.

What are some key metrics and performance indicators that organizations should track to evaluate the effectiveness of their data-driven initiatives?

JH: Starting with a small and smart objective is fundamental for managers who want to implement a datadriven approach. Experiences have shown that starting too big often leads to failure. By beginning with a small project and gradually building on it, you can apply the experience gained and progress towards becoming a data-driven organization.

The key performance indicators (KPIs) to focus on will depend on your specific strategy and objectives. When developing a data strategy, it’s important to define clear objectives and desired outcomes. And metrics can be defined based on these objectives, including ROI, efficiency improvement, reduction in decision-making time, and creating a competitive advantage. Other metrics may include added value, market share increase, and various objectives aligned with desired outcomes.

Data-related metrics focus on aspects such as data quality, reliability, validity, security, and accessibility. Implementing effective data governance is a significant challenge for companies, as it requires expertise in legal compliance, technical capabilities, and management alignment with the company's goals.

What are the potential risks and limitations associated with relying solely on data-driven decision-making, and how can businesses mitigate these risks?

JH: There are different competences required to implement a data strategy, and it is impossible for one person to possess them all. But based on my experience, the most important competence is business analytics. Technical skills are valuable, but without knowledge about the business itself, achieving success is challenging.

In implementing a data-driven approach, you need to have a team with diverse competences, including data architecture, engineering, science, and analytics. The role of a business analytics expert, for example, is very important in providing direction based on a deep understanding of the business. Technical experts then implement solutions aligned with this direction, while the business analytics expert evaluates the results and interprets the outcomes.

Although AI may provide various competences in the future, currently, the human point of view and evaluation remain critical. While AI can deliver good outputs, the perspective and evaluation of managers are indispensable in implementing solutions.

Thank you Jamil! Would you like to share any closing remarks?

JH: To recap, I’d like to mention that some managers and companies may not be convinced that data is the ultimate solution for business transformation and improvement. Also, we didn't discuss another important aspect: data as a primary source of innovation. Innovation is not limited to creating new products, services, or technologies. It can also involve innovating the business model itself.

Examples like Nespresso, Facebook, Uber, and Airbnb demonstrate how data can be used to innovate and create new businesses. Data allows us to be present everywhere, all the time. Therefore, my advice is to recognize the immense power of data and explore its potential to unlock significant added value from these new sources.