Eliga ESA - Enterprise Data Platform

Roadmap to your EDP Success

Phase 1: Laying the Foundation

In this phase we set out to understanding the "why" behind the data platform. It's crucial to align the data platform with the organisation's business objectives and identify areas where data can drive real value. By thoroughly assessing the existing data landscape, including data quality and infrastructure, potential challenges can be identified early on. Engaging with business stakeholders to understand their data needs and key business questions will ensure the platform is built with the user in mind.

Phase 2: Designing the Blueprint

During this phase we focus on the "how" of building the data platform. Key decisions around the cloud platform, account strategy (single vs multi-account), and data platform approach (data lake vs data warehouse) will have long-term implications. The technology stack must be carefully chosen to support both current and future needs, taking into consideration scalability, cost, and ease of use. A well-designed blueprint will lay the foundations that will satisfy current requirements and be agile enough to evolve to meet future needs.

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Phase 3: Bringing it to Life

This is where the data platform takes shape. The landing zone is established, providing a secure foundation for the platform. Data pipelines are set up, data is migrated from legacy systems, and the chosen technologies are implemented. We collaborate closely with technical teams and business stakeholders to ensure smooth execution and alignment with requirements. We can either drive or oversee the building of interactive dashboards and reports that clearly present data insights to business users. This is critical for enabling data-driven decision-making.

Phase 4: Continuous Evolution

A data platform is not astatic entity; it needs to continuously evolve to remain valuable. We promote training programs for users which are essential to empower them to effectively use the platform and its tools. We offer ongoing monitoring and performance tuning which will optimise efficiency and keep costs under control. Regularly evaluating the platform's effectiveness against its initial goals and adapting the roadmap ensures the platform continues to deliver value and meet evolving business needs. Fostering a data-driven culture across the organisation will maximise the impact of the data platform and drive innovation.

The Challenges Implementing EDPs

Scalability for Future Analytics

Initial data platform implementations may not require the scale and processing power necessary for future, more complex analytics requirements. Planning for future needs during the initial design phase is crucial to avoid platform limitations and costly re-architecting.

Adaptability to Changing Requirements

The platform must adapt to changing market circumstances and incorporate new data sources seamlessly. Designing the platform with a flexible hierarchy that allows new data granularities to be added without rebuilding dashboards and KPIs is crucial.

Organisational Capability and Skill Gaps

Aligning technology choices with the organisation's existing skills and capabilities is essential. While initial implementations may leverage familiar tools, planning a roadmap to evolve the platform alongside the team's skills is crucial. Providing training and up skilling opportunities to bridge skill gaps is essential for successful platform adoption.

Standardisation and Governance

Implementing best practices for governance and standardisation from the outset is crucial for managing the complexity of an enterprise data platform, particularly as the platform evolves to incorporate machine learning and AI. Well-defined standards and guidelines streamline the industrialisation of data science models and ensure the successful delivery of business value.

Performance Considerations

Balancing performance with consumption costs is a key technical challenge. Design choices regarding data storage, processing tools, and data access methods impact both application speed and overall cost. Understanding data volumes and usage patterns, both current and future, can inform the choice of tools and storage solutions that optimise performance without excessive costs.

Data Ingestion and Granularity

Deciding when and how to ingest additional data, whether through new datasets or increased granularity, involves trade-offs between cost and value. Ingesting data before it's needed can lead to increased consumption costs without immediate benefits.Architecting the platform to support future data ingestion while controlling initial data volumes is crucial for cost optimisation.

Tool Selection and Consumption Costs

Different tools and technologies have varying consumption patterns and cost structures. Choices made in the design phase regarding data processing tools, storage solutions, and data access methods will significantly impact ongoing consumption costs.

Balancing Data Warehouse and Data Lake Approaches

Data warehouses are often optimised for BI applications but less flexible for data science, while data lakes are suitable for machine learning but may lack enterprise governance. Choosing the right approach and potentially integrating both data warehouse and data lake capabilities requires careful evaluation of the organisation's needs and priorities.

How we address the Challenges

We look for executive buy-in and organisational change management

Obtaining buy-in from leadership and fostering a culture of change is crucial for successful platform implementation. Executive support helps secure resources and facilitates the adoption of new processes and technologies. Engaging stakeholders across the organisation early on minimises resistance and ensures alignment with business objectives.

We select the correct automation and tooling

Implementing a well-defined data access control mechanism, such as purpose-based access control, is critical for managing data security and compliance. Establishing clear governance procedures for data usage, quality, and metadata management ensures data integrity and supports responsible data sharing across the organisation.

We administer data access and governance

Automating repetitive tasks, such as data ingestion, pipeline deployment, and testing, reduces manual errors and improves efficiency. Selecting appropriate tools that support automation and integration with existing systems is vital for streamlining development workflows and minimising operational overhead.

We implement iterative development and continuous improvement

Adopting an iterative approach to platform development, starting with a few critical use cases, allows for incremental value delivery and continuous improvement based on user feedback. Regularly reviewing and updating the platform ensures it stays aligned with evolving business needs and in corporates new technologies and best practices.

Enterprise data platform