Senior Manager of Revenue Management & Cloud Implementation at Etisalat Egypt

The cloud plays a crucial role in accelerating data transformation and acts as a fundamental facilitator for innovative business opportunities. Cloud enables disruptive business models and data and analytics use cases through federated data architectures that make data easily sharable across business units and within enterprise alliances. By extending the availability of data for analytics use cases, organizations can unlock novel business opportunities.

Many executives understand the significance of data in their business and operations. However, a considerable number of companies have yet to make the required investments to fully leverage the potential of their data for their enterprises. Common Challenges are legacy systems, dispersed data sources, and the absence of a comprehensive data strategy.

The Benefits of Cloud for Data & Product Development

Adopting an operational approach that treats data as a product can greatly improve data discoverability and accessibility for development teams, leading to a significant three to six months reduction in the time required to execute use cases. By clearly defining data ownership, organizations can significantly enhance data quality and streamline the efficiency of both technical and business teams.

Cloud-based data platforms enable seamless collaboration among geographically dispersed teams, facilitating real-time data sharing and accelerating product development cycles. With cloud-based data analytics tools, organizations can gain valuable insights from their data, enabling data-driven decision-making and enhancing the quality of products and services.

The cloud offers numerous benefits for data management and product development, providing organizations with agility through DevOps, DataOps, and MLOps tooling, resilience by offering disaster recovery solutions through several availability zones in a single cloud region, elasticity by cutting infrastructure cost by automating infrastructure orchestration and dynamically provisioned compute resource to the required workload, and resources required to innovate and stay competitive in today's fast-paced business environment.

Cloud-Enabled Data Architecture Models

Cloud-enabled data architecture models refer to the various ways data can be organized, stored, and accessed in cloud computing environments. These models provide flexible and scalable solutions for handling data in the cloud. Some common cloud-enabled data architecture models include:

Data Warehousing: In this model, data from multiple sources are integrated and stored in a central repository known as a data warehouse

Data Lakes:  It offers central, cost-effective, scalable storage for large volumes of structured and unstructured data. As the platform evolves, organizations have the flexibility to add analysis capabilities (such as streaming and SQL analytics). Data lakes require users to have a high level of skill and experience to analyze unfamiliar and unprocessed data.

  ​Choosing the right architecture model is often essential to driving the right transformation, so the decision should consider both technological and organizational factors 

Lakehouses combine the advantages of a data lakes, cost-effective, scalable storage with a data warehouse's reliable and performance reporting offering. They provide central storage for business intelligence and SQL analytics as well as for data applications requiring unstructured or near- or real-time data.

Data mesh uses a decentralized data architecture that allows for federated development and provisioning of data products. Companies must have strong capabilities in data observability and discoverability to make data accessible across the organization. Data product owners create, maintain, and offer scalable, business-oriented data products as a service to the entire organization, requiring enterprises to adopt an agile working model.

Choosing the right architecture model is often essential to driving the right transformation, so the decision should consider both technological and organizational factors.

Accelerate Value Capture from Data through Cloud

A data transformation that successfully captures value through the cloud begins with comprehensive preparations that include both the enablers of the transformation and the transformation itself. There below are three very important prerequisites:

Formulate the data strategy and how it will help achieve business goals, and companies can identify appropriate use cases. Organizations can also explore possible synergies with existing cloud service offerings.

Providing necessary training and resources Need for team leaders to ensure their team members have access to relevant training, tools, and resources to develop their data skills.

Determine the target architecture and the split between on-premises and cloud capabilities. Companies should conduct a deep dive into regulatory, data privacy, and data-residency requirements. Assessing the feasibility of cloud migration based on the existing landscape can help companies determine the right balance between on-premises systems and cloud services. This exercise can identify the cloud-based platforms to support the chosen data architecture and align the technology with talent availability.