Economy

Air traffic between Dubai and Chinese cities has witnessed significant growth during the recent period, following the opening of the Chinese markets and the abolition of all restrictions and precautionary measures adopted by the Chinese authorities since the beginning of the “Covid 19” pandemic in 2019. Dubai Civil Aviation Authority data showed that the number of weekly flights between Dubai and 8 Chinese destinations It increased from 14 flights per week last January to 54 flights per week last month, which indicates the size of the growth in the seat capacity of airlines operating between Dubai and China.

Steady rise

Majid Al Joker, Chief Operating Officer of Dubai Airports, said that the reopening of China’s borders was an important development that contributed to giving a major impetus to supporting the recovery stages within the travel and tourism sectors worldwide, as it is (the last piece in the recovery of the tourism sector) as described by the World Tourism Organization in earlier.

He added that China is one of the fastest growing tourism and aviation markets during the years before the pandemic, so this step constituted a good sign for the growth of passenger traffic in 2023 and beyond. And with the increase in the number of flights for airlines, we expect a steady rise in passenger traffic to and from China via Dubai International Airport during the coming months, specifically in the second quarter of this year.

Al-Joker confirmed that there are 5 airlines operating between Dubai Airport and Chinese destinations, including Emirates Airlines, as these companies operate regarding 54 weekly flights between Dubai International Airport (DXB) and 8 different destinations throughout China.

Strong request

For his part, Adnan Kazim, Chief Commercial Officer of Emirates Airlines, said that there is a strong demand to and from the Chinese market, as the company operates regarding 35 weekly flights to this market, with strong and full occupancy rates, indicating that the carrier is witnessing stronger summer bookings than the stage Before the pandemic, amid increasing growth in its operations, while continuing to operate the giant aircraft, increasing seat capacity, and adding more flights to existing destinations.

Dubai has emerged strongly among the first 10 global destinations that the Chinese plan to visit during their upcoming holidays, according to the destination index survey issued by the “YouGov” statistics and research company. According to the data of the Department of Economy and Tourism, the number of Chinese visitors that Dubai received increased from 19 thousand visitors in the first two months of 2022 to 52 thousand visitors in January and February of this year, a growth of 174%, and thus Dubai succeeded in a short time in recovering 24% of the number of visitors. Chinese tourists in the pre-pandemic period, which amounted to 209 thousand visitors during January and February 2019.

Urid Group announced the conclusion of a strategic partnership with Axon, a global leader in technologies related to public safety.
Ureed is a leading provider of managed communications and IoT solutions to its customers across its global presence. Under this partnership, Ureed will be the preferred provider of Axon communications solutions in the Middle East and North Africa region including Algeria, Tunisia, Jordan, Kuwait, Qatar, Oman, Iraq and Bahrain.
Headquartered in Scottsdale, Arizona, USA, Axon employs a wide range of connected hardware and software solutions for public safety purposes. Axon’s products include Taser devices, wearable cameras, in-vehicle cameras, digital evidence management cloud solutions, productivity software, and real-time capabilities.
Sheikh Mohammed bin Abdullah Al Thani, Executive Vice President of Ureed Group, said: “We are proud of our partnership with Axon as a global leader in public safety solutions, and this partnership will be the first in a series of future partnerships that will help us achieve our strategic goal of strengthening our presence in the market.” Global Managed Communications for the Internet of Things.
He added: “Through our partnerships with Axon to provide managed communications solutions for the Internet of Things to leading international clients, we embody our commitment to digital transformation, investing in innovation, and providing our customers with the best products, services and technologies that enable them to develop their worlds.”
Ureed is looking forward to establishing its global position as a leading provider of managed communications for the Internet of Things across the region.

Data has become the lifeblood of business, driving innovation and empowering business leaders and other decision-makers with actionable insights. The data science lifecycle is a systematic process that transforms raw data into valuable insights, which helps innovation and business outcomes. The crux of this process lies in operationalizing data — making it available, accessible, and actionable for data-driven organizations.

Why Operationalizing Data Is So Important Today

Operationalizing data refers to the process of integrating data and analytics into the daily operations of a business or organization to drive improved decision-making and reach goals, and it’s a critical component of any leadership playbook today. Here’s some research that shows how operationalizing data has become critical for businesses today.

Data-Driven Decision-Making: According to a study by MIT, companies that adopt data-driven decision-making have a 4-6% increase in productivity and a 5-6% increase in profits compared to companies that do not.

Competitive advantage: A report by McKinsey Global Institute suggests that companies that leverage big data and analytics effectively can achieve a 5-6% higher productivity rate and a competitive advantage over their peers.

Data investments: According to a study by NewVantage Partners, over 97.2% of surveyed organizations reported investing in big data and AI initiatives to drive digital transformation.

And yet, many organizations are still struggling:

Data accessibility: A study by Dresner Advisory Services found that only 35% of employees on average have access to the data and analytics they need to make informed decisions.

Data maturity: A study by Harvard Business Review Analytics Services found that only 20% of organizations had achieved data maturity, meaning they were fully able to leverage their data to drive better decision-making and outcomes.

A Five-Part Framework for Operationalizing Data

By focusing on the core steps in the data science lifecycle, business leaders can effectively operationalize the use of data in their organizations. Here’s a five-part framework for operationalizing data in your organization:

Data Collection and Preparation: Operationalizing data in this phase means implementing robust data pipelines, gathering data from various sources, and ensuring data quality. Data engineers and data scientists work together to preprocess, clean, and aggregate data. This process includes removing duplicates, filling missing values, and transforming data into the desired format for further analysis. By operationalizing data at this stage, organizations can reduce errors and inconsistencies, ensuring that they work with accurate and reliable data.

Data Exploration and Analysis: The next phase involves exploring and analyzing the data to uncover patterns, trends, and relationships. Leaders must provide data scientists with the right tools and platforms for analysis, such as Jupyter notebooks, Python libraries, and visualization tools like Tableau or Power BI. User-friendly interfaces help data scientists quickly generate hypotheses, identify patterns, and develop a deeper understanding of the data. This iterative process of exploration and analysis is essential in generating valuable insights and guiding the development of data-driven solutions.

Feature Engineering and Model Selection: Feature engineering is a critical step in the data science lifecycle, as it involves selecting the most relevant variables or features from the dataset that will contribute to the predictive power of the model. This stage involves automating the process of feature extraction, transformation, and selection using tools like TensorFlow, Scikit-learn, or H2O.ai. By operationalizing data in this manner, organizations can ensure that their models are built on the most relevant and meaningful features, increasing the accuracy and efficiency of their predictions.

Model selection entails scientists choosing the most suitable machine learning algorithms for the task at hand. Operationalizing data here means providing data scientists with access to various algorithms and libraries, facilitating experimentation with different models, and enabling them to select the best-performing one.

Model Training and Evaluation: Once the features and model have been selected, the next step is to train the model using the prepared data. In this phase, data scientists focus on fine-tuning the model’s parameters and improving its performance. By leveraging platforms like Databricks or MLflow, organizations can streamline the model training process and track the performance metrics of various models.

Operationalizing data also involves setting up cross-validation techniques and evaluation metrics, such as accuracy, precision, recall, or F1 score, which helps identify the most effective model that can be deployed in production.

Model Deployment and Monitoring: The final phase of the data science lifecycle is deploying the trained model in a production environment and monitoring its performance. Operationalizing data during this stage involves setting up a seamless transition from development to production, ensuring that the model can be deployed with minimal downtime and integrated into existing systems. Tools like Kubernetes, Docker, or TensorFlow Serving help organizations manage the deployment and scaling of their machine learning models.

Monitoring the model’s performance is vital for maintaining its effectiveness and reliability. This means setting up performance monitoring systems that track the model’s accuracy.

Final Thoughts

Simply having data is no longer enough. It must be operationalized to grow and scale in business today. By following the above data science lifecycle framework, you can take the steps required to operationalize data in your organization.

Want more tech insights for the top execs? Subscribe to the Leadership channel: