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How can you ensure data architecture supports experimentation?

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Data architecture is the design and implementation of how data is stored, processed, and accessed in an organization. It plays a critical role in enabling experimentation, which is the process of testing new ideas, products, or features with data and feedback. Experimentation can help businesses innovate, optimize, and learn from their customers and markets. However, not all data architectures are conducive to experimentation. In this article, you will learn how to ensure your data architecture supports experimentation by following some best practices and principles.

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Soon Heng TAN ???? ???? ??????????

Head of Data & Analytics (Data Management) at Public Service | Master of Technology (Knowledge Engineering) | CDMP |…
Kala M.

Data Architect at Singapore Institute of Management

1 Data governance

Data governance is the set of policies, standards, and processes that ensure the quality, security, and usability of data across the organization. Data governance is essential for experimentation, because it ensures that data is consistent, accurate, and reliable for analysis and decision making. Data governance also helps to avoid data silos, duplication, and conflicts that can hinder experimentation. To implement data governance, you need to define clear roles and responsibilities for data owners, stewards, and consumers, as well as data quality metrics, data cataloging, and data lineage.

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Renjith Sebastian

Sr Staff Data & Information Architect at GE Healthcare
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1 Data Gathering and Preservation: Scalability and adaptability should be considered while gathering and storing data in a centralised location. Think about utilising data warehouses or lakes. Utilise schema-on-read techniques to support various and changing data sources. 2 Data Governance and Quality: To preserve data consistency and quality, implement data governance practises. To ensure that data is precise and dependable for trials, use data cleansing and transformation methods. 3 Data Privacy and Security: To protect sensitive information, put strong data security and privacy procedures in place. Respect data privacy laws and make sure that the right access restrictions are in place.

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Jithin Majeed

Data Architect at Mawer Investment Management
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Maintain high data quality to improve accuracy of experiments. Design the architecture to scale effectively as data volumes grow, ensuring it can accommodate large-scale experiments. Implement data version control to track changes and maintain data lineage, which is vital for reproducibility. Secure sensitive data with proper measures to protect privacy and compliance. Effective metadata management simplifies data discovery and understanding. Establish clear data governance policies and procedures to maintain data integrity and adherence to regulations. Lastly, design the architecture with flexibility in mind, allowing it to adapt to changing experiment requirements, tools, and techniques.

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2 Data integration

Data integration is the process of combining data from different sources and formats into a unified view or repository. Data integration is crucial for experimentation, because it enables you to access and analyze data from multiple systems and platforms, such as web, mobile, CRM, or ERP. Data integration also allows you to enrich and transform data to suit your experimentation needs and goals. To enable data integration, you need to use appropriate tools and techniques, such as ETL (extract, transform, load), ELT (extract, load, transform), or data pipelines, as well as data APIs (application programming interfaces) and data lakes.

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3 Data modeling

Data modeling is the process of defining the structure, relationships, and constraints of data in a logical and physical way. Data modeling is vital for experimentation, because it determines how data is stored, organized, and accessed for analysis and reporting. Data modeling also affects the performance, scalability, and flexibility of your data architecture. To optimize data modeling, you need to use suitable data models and schemas, such as relational, dimensional, or document-based, as well as data warehouse and data mart design principles.

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4 Data analytics

Data analytics is the process of applying statistical and mathematical techniques to data to discover patterns, trends, and insights. Data analytics is the core of experimentation, because it enables you to test hypotheses, measure outcomes, and evaluate results. Data analytics also helps you to generate and validate new ideas, products, or features based on data and feedback. To facilitate data analytics, you need to use appropriate tools and methods, such as BI (business intelligence), data visualization, A/B testing, or multivariate testing, as well as data science and machine learning.

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5 Data culture

Data culture is the mindset and behavior of the organization towards data and experimentation. Data culture is the foundation of experimentation, because it influences how data is valued, shared, and used for decision making. Data culture also affects the adoption, collaboration, and innovation of experimentation across the organization. To foster data culture, you need to promote data literacy, data ownership, and data ethics among your employees, as well as data-driven leadership, communication, and feedback.

By following these best practices and principles, you can ensure that your data architecture supports experimentation and helps your organization to achieve its goals and objectives. Experimentation is not only a technical process, but also a strategic and cultural one, that requires a robust and flexible data architecture.

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Soon Heng TAN ???? ???? ??????????

Head of Data & Analytics (Data Management) at Public Service | Master of Technology (Knowledge Engineering) | CDMP | TOGAF | AWS-SAA | GCP-DE | GCP-MLE | DCS-DS | CSM | CSSLP | EDHL Enterprise Data Harmonisation Leader??
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Experimentation often involves data understanding, collaboration, and self-service empowerment: 1. Data architecture (DA) provides the framework to streamline process of making disparate data (from different systems) accessible, discoverable, and understandable to facilitate data experimentation for authorized data consumers. 2. DA should be designed to allow different departments to manage/share own data products within their functional domain, and facilitate data producers/consumers collaboration. 3. DA can be designed to empower authorized users with self-service data preparation and analysis tools accessing the right data that are required. Depending on users' comfort level and proficiency, make available the right tools for them.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Kala M.

Data Architect at Singapore Institute of Management
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Data processing is more important as same data can be transformed in multiple ways according to the analysis and will be stored in order to access and retrieve the data fast and in efficient manner. Hence ETL is important aspect in data architecture.

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How can you ensure data architecture supports experimentation?

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3

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5

6

1 Data governance

2 Data integration

3 Data modeling

4 Data analytics

5 Data culture

6 Here’s what else to consider

Data Architecture

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How can you ensure data architecture supports experimentation?

1

2

3

4

5

6

1 Data governance

2 Data integration

3 Data modeling

4 Data analytics

5 Data culture

6 Here’s what else to consider

Data Architecture

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