Alaeddin Khader, Head of Data Engineering and Machine Learning at Miral Experiences, talks about how his team uses data analytics to remove business blind spots. It helps operational efficiency by presenting a 360-degree understanding of consumer behaviour, where demographic data is enriched with transactional and behavioural information about the customer.
Alaeddin Khader, Head of Data Engineering and Machine Learning at Miral Experiences, discusses the crucial role of data engineering in creating immersive guest experiences. He also shares insights on using machine learning to boost operational efficiency and the key components of a modern data stack. In his interview, he highlights the skills and characteristics he looks for when building his team and the emerging technology that will be disruptive in the near future.
Excerpts from the interview;
How is the role of Data Engineering defined in the business of immersive destinations and experiences?
As an organisation focused on immersive destinations and experiences, the company’s strategy revolves around creating smiles for guests visiting us worldwide. To achieve this, we strive to be a data-driven organisation where all business and strategic decisions are data-driven. This is where the role of data engineering becomes crucial, as it is at the heart of our company’s data strategy.
Data Engineering is responsible for establishing and maintaining a robust and scalable data platform that enables efficient and effective data processing and analysis. The skilled professionals in the Data Engineering team ensure that data is clearly available, governed, and processed according to clear procedures and processes. This includes implementing data quality, integrity, and security and developing data pipelines and workflows to streamline data ingestion, transformation, and storage.
A strong data culture and training programs are also fostered within our organisation to promote data literacy and empower our team members to make data-driven decisions. With a well-established data engineering function, our organisation is able to unlock the full potential of our data assets, leading to business growth, improved financial cost efficiency, revenue uplift, and operational excellence. In essence, data engineering plays a pivotal role in driving our company’s success by providing the foundation for our data-driven approach to decision-making and enabling us to deliver exceptional experiences to our guests.
How do you use machine learning capabilities to help operations teams add efficiency?
The operations team needs to understand the customers, the products and the procedure more to be more effective, and data analytics is the key to removing the blind spots.
As a data engineering team, we provide the organisation with clear visibility of the data from various touch points to enable the operations team to understand consumer behaviour from 360 views, where the demographics information is enriched with transactional information and behavioural information about the customer. Also, a lot of customer KPIs are created on the fly. In order to help the operations team, we have built machine learning models to segment the customers so that they can target them with marketing automation campaigns based on their consumer behaviour.
Can you give us an example of how data and analytics are used at one of your destinations?
As an example of data analytics being used in our destinations, we are utilising data and analytics to provide the organisation with comprehensive daily revenue analytical reports and datasets, which offer a holistic view of various aspects of what is all that is happening in the business, such as sales, visitation, and revenue. And not only provide the business with interactive dashboards but also with self-service datasets to do their analysis and custom reports.
In another example, we recently developed a price elasticity model using machine learning algorithms based on historical records and customer behaviour to assist our team in setting pricing for FnB items. Thanks to this model, we were able to increase revenue while maintaining customer satisfaction and demand.
What advice would you give business leaders to measure the success of their data projects?
As a business leader, measuring the success of data projects can be challenging. However, key measures of success could include the number of insights generated, cost reduction achieved, revenue generated, reliability of the data product, stakeholder feedback, and continuous improvement efforts. It is important to treat the data product as a valuable asset and continuously optimise and update it for ongoing success.
I advise every business leader to set clear objectives and KPIs for each data product and keep monitoring and evolving them to maintain and increase the usability and profitability of the data product. It is important to make those data-driven and easily trackable measures as the value and impact might vary from time to time depending on a lot of organisational and economic-related factors.
What are the key components of a modern data stack?
Here are some thoughts on the key components that are a must for the data stack:
- Data Governance, Quality, and Security: Ensuring data governance policies, data quality, integrity, privacy, and compliance with security measures is critical to maintaining trust, accuracy, and regulatory compliance.
- Data Storage: Effective and efficient data storage is fundamental to managing and retrieving data for analysis and decision-making.
- Data Integration and Transformation: Integrating, transforming, and preparing data from various sources into a unified format is essential for data consolidation and analysis.
- Data Modeling: Creating appropriate data models or data structures is important for organising and managing data for efficient retrieval and analysis.
- Data Visualisation: Presenting data visually appealing and informatively helps understand and interpret data for decision-making.
- Data Ingestion: Acquiring and ingesting data from various sources into the data stack is the first step in the data processing pipeline.
- Data Orchestration and Workflow: Managing the flow and processing of data across different components of the data stack is important for streamlining data operations.
- Data Catalog and Metadata Management: Cataloging and managing metadata for understanding and discoverability help locate and utilise data effectively.
There might be some more components depending on the organisation and environment, but the above are common components that can’t be missed, in my opinion.
What skills and characteristics do you look for when building your team?
When building my team, I value a combination of skills and characteristics in data engineers to meet the challenges of the market. Some key skills and characteristics that I look for include:
- Attitude: A positive attitude towards work, colleagues, and challenges is crucial. I seek team members who are optimistic, adaptable, and resilient in the face of setbacks. A proactive and solution-oriented attitude is valuable in overcoming obstacles and driving success.
- Hunger for Learning: In the rapidly evolving field of data engineering, a thirst for continuous learning is essential. I look for team members eager to learn and keep themselves updated with the latest industry trends, technologies, and best practices. Curiosity and a growth mindset can contribute to innovation and continuous improvement.
- Programming Ability: Strong programming skills are fundamental for data engineers. Proficiency in languages such as Python, Java, Scala, or SQL and experience with relevant data engineering frameworks and tools are crucial for building scalable and efficient data pipelines. The ability to write clean, maintainable, and optimised code is highly valued.
- Data Proficiency: A solid understanding of data fundamentals, such as data modelling, data integration, data warehousing, and data processing, is critical for data engineers. Also, familiarity with big data technologies, such as Hadoop and Spark, is also important to master data engineers.
- Collaboration and Communication: Strong teamwork and communication skills are essential for data engineers as they often work closely with data scientists, analysts, and other stakeholders. Collaborating effectively, communicating clearly, and sharing ideas and knowledge is important for building a cohesive and high-performing team.
When building my data engineering team, I look for individuals with a positive attitude, hunger for learning, strong programming ability, data acumen, and effective collaboration and communication skills. These qualities are vital in meeting the dynamic data engineering landscape’s challenges and driving market success.
What is one emerging tech that will be disruptive in the near future?
Since I have been in the field of Data and AI for a long enough time, I will start by highlighting one of the emerging technologies that are expected to be disruptive in the future, which is artificial intelligence (AI), during my work in the field I was able to witness the potential and huge evolution of the AI adoption and use cases. However, we need to know that the disruptive potential of AI depends on factors such as its maturity, adoption rates, regulatory environment, societal acceptance, and market dynamics.
There are, for sure, other promising technologies that I am fascinated about and are expected to have a significant impact in the future, including Quantum computing, 5G, Augmented Reality (AR), Virtual Reality (VR), IoT, and Advanced Robotics. However, as someone in the Data and AI domain, I always relate those technologies to the potential impact on the evolution of AI. For example, quantum computing has the potential to significantly impact the evolution of AI by providing increased processing power, improved optimisation capabilities, enhanced data analysis, and enhanced security measures.
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