Success in Data Analytics: Platform Architecture Fundamentals, with David Hill (CTO)
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Introduction
Welcome to our new series on success in data analytics! Each week, I will be joined by David Hill, CTO of AABI, who has over 20 years of experience delivering global data warehouses. In this week's installment, we will explore platform architecture fundamentals, focusing on key strategies for organizations embarking on the mission of building a data warehouse or lake house. Our discussion will center around three main areas:
- Focusing on Objectives, Not Requirements
- Forecasting Data Volumetrics for the Platform
- Maintaining a Single Platform Architecture
David emphasizes the importance of these subjects as they can significantly help organizations optimize their data analytics initiatives. Let's delve into each point in detail.
Focusing on Objectives, Not Requirements
Objectives provide a high-level vision of what an organization aims to achieve, while requirements often delve into granular specifics that can overwhelm stakeholders. It's vital to identify three to five key objectives that align the business and technical teams. This alignment ensures everyone understands the purpose behind the project, which is essential, given that analytics is a shared domain, not just the responsibility of IT departments.
For example, a common objective may be to consolidate multiple reporting databases into a centralized cloud platform to enhance procurement efficiency. This focused approach helps in achieving better governance right from the start, creating a solid foundation for further development.
Forecasting Data Volumetrics
Understanding anticipated data growth is crucial when planning an analytics platform. David likens this process to estimating the volume of water that a bucket can hold; organizations need to have a clear picture of their data inflow over time.
By projecting data volumetrics for three to five years, organizations can effectively manage costs associated with their data platforms. David notes that having multi-year data growth plans helps mitigate risks and stands to benefit organizations financially by reducing unforeseen expenses.
Maintaining a Single Platform Architecture
Once objectives have been established and data volumetrics estimated, the next step is to design a coherent platform architecture. David warns that many organizations fall prey to the allure of the latest technologies without assessing whether those solutions align with their specific needs.
Developing a high-level platform design based on objectives and forecasts allows organizations to create standards and avoid costly changes mid-project. Sticking to a chosen platform for a medium to long term ensures better resource management and continuity.
David also highlights the importance of seeking external advice during vendor selection processes. Engaging with professionals who have experience across various platforms can provide valuable insights into making informed decisions.
Conclusion
In summary, focusing on objectives, accurately forecasting data volumetrics, and maintaining a consistent platform architecture are crucial for the success of any data analytics initiative. These fundamental principles lay the groundwork for future growth and better governance.
Keywords
- Data Analytics
- Platform Architecture
- Objectives
- Requirements
- Data Volumetrics
- Centralized Platform
- Cost Management
- Vendor Selection
- Governance
FAQ
Q1: Why is it important to focus on objectives rather than requirements?
A1: Focusing on objectives provides a clear and shared vision for the project, helping to align both business and technical teams, ensuring everyone understands the purpose behind the analytics initiative.
Q2: How far into the future should an organization forecast data growth?
A2: It is recommended to forecast data volumetrics for three to five years to effectively manage costs and mitigate risks associated with unexpected data growth.
Q3: What are the risks of changing platform architecture mid-project?
A3: Changing platform architecture during a project can significantly increase costs and lead to wasted time, as well as resource misalignment. Stability in platform choice is critical for effective implementation.
Q4: How should organizations select the right data platform?
A4: Organizations should work out their high-level objectives, assess their data needs, and then consult experts with experience across various platforms to make educated decisions rather than following trends.