Step by Step Plan to Build a Data Foundation with a Cloud Data Platform

Estimated reading time: 14 minutes

Data is a critical asset for many organisations. However, many find themselves struggling to leverage their data effectively. Their data is often siloed, inconsistent, difficult to access, and time-consuming to prepare for analysis. This is where the concept of a Data Platform or Data Foundation comes into play. A data platform offers a robust and streamlined approach to managing and leveraging your data assets.

This article explains why you should build a strong cloud data foundation. It explores the added value it brings to businesses across various industries.

What is a data platform or data foundation?

At its core, a cloud data foundation serves as the bedrock of your data strategy. It’s a unified and well-governed environment that integrates data from various sources. Additionally, it transforms the data into a consistent and usable format, and makes it readily accessible for analysis and decision-making. 

A data foundation is the essential infrastructure that empowers your business to move beyond simply collecting data to actively leveraging it for tangible benefits.

The value proposition of a data foundation is clear. It transforms raw, often chaotic data into actionable insights that drive business advantage. By establishing a solid data foundation, your organisation can experience a multitude of benefits, including accelerated time to insights; improved data quality; enhanced agility and scalability; stronger data governance and security. It also brings you optimised resource utilisation and cost efficiency and self-service analytics.

Ultimately, a robust data foundation empowers your business to move swiftly and confidently, making data-backed decisions that lead to better outcomes, increased efficiency, and a stronger competitive position. It addresses the common data challenges that often hinder progress. Once your solid cloud data platform is established, you can evolve its capabilities to support specific AI data platforms.

Data challenges you can solve with data platforms

The digital age creates more data than ever before. Organisations have vast amounts of data originating from many sources – customer interactions, sales transactions, marketing campaigns, operational systems, IoT devices, and more.

An IDG study even claims that an average enterprise analyses over 400 data sources.

This data deluge, while holding immense potential, often presents significant challenges for organisations:

• Data Silos: Data residing in disparate systems and departments, making it difficult to gain a holistic view of the business.
• Inconsistent Data Formats: Data from different sources often comes in varying structures and formats. It requires significant effort for integration and standardisation.
• Data Quality Issues: Inaccurate, incomplete, or outdated data can lead to flawed analyses and misguided decisions.
• Complex Data Pipelines: Manual and fragmented data processing workflows prone to errors and difficult to maintain.
• Security and Compliance Concerns: Ensuring the security and privacy of sensitive data while adhering to evolving regulatory requirements.
• Lack of Data Discoverability: Difficulty in finding and understanding available data assets, leading to duplication of effort and missed opportunities.

While offering immense scalability and flexibility, a cloud-native data warehousing environment presents new challenges. Data needs to be easily discoverable and available for both technical and business users. Maintaining data security requires clear data lineage and the ability to apply fine-grained access control principles. Ensuring consistency across data and transformations becomes crucial. Moreover, new skills and expertise are required to effectively leverage cloud-native technologies, potentially increasing the time to value.

Without a well-defined data foundation, organisations often find their teams bogged down in the complexities of data wrangling. It leaves less time to focus on generating valuable business insights. The workload associated with orchestrating can easily detract from the core goal of leveraging data for strategic advantage.

6 Benefits of Cloud Data Platforms

The benefits of building a data foundation extend far beyond mere technical improvements. They translate directly into significant business advantages:

1. Time Savings and Accelerated Insights

A data foundation’s primary benefit is the reduced time required to access and prepare data for analysis. Instead of spending weeks manually integrating and cleaning data from disparate sources, a well-established foundation automates these processes. Data ingestion tools can seamlessly connect to various systems, and transformation frameworks allow for efficient and repeatable data manipulation.

2. Ensuring Data Consistency and Quality

Data quality is paramount for reliable analysis and informed decision-making. A data foundation implements mechanisms to ensure data accuracy, completeness, and consistency. This includes enforcing naming conventions for tables and columns, standardising data descriptions, and embedding data quality tests within the transformation pipelines. Tools like Elementary can be integrated to provide data quality reports and tracking. By establishing a single version of the truth, a data foundation eliminates the confusion and errors that can arise from working with inconsistent data.

3. Empowering Agility and Scalability

Modern data foundations built on cloud platforms offer inherent agility and scalability. Infrastructure-as-code tools like Terraform enable the easy deployment and management of data foundation components across different environments, such as development, testing, and production. This allows for rapid experimentation and the quick rollout of new data pipelines and analytical capabilities.

Furthermore, cloud-native data warehouses can seamlessly scale storage and compute resources up or down based on demand, ensuring that the data foundation can handle growing data volumes and increasing analytical workloads without requiring significant upfront investment or complex infrastructure management. This scalability is particularly valuable for businesses experiencing rapid growth or dealing with seasonal data fluctuations. The flexibility to deploy data foundations in for example any Cloud Data Center region also provides adaptability based on factors like data locality and environmental considerations.

4. Strengthening Data Governance and Security

Protecting sensitive data and ensuring compliance with regulations are critical for all organisations. A data foundation provides a centralised platform for implementing and enforcing robust data governance and security policies. Read here how a data foundation helps you comply with regulations like the GDPR .

5. Optimising Costs and Resource Allocation

While the initial investment in building a data foundation is required, it ultimately leads to significant cost savings. Even to more efficient resource allocation in the long run. 

• By automating data integration and preparation tasks, organisations can reduce the manual effort and associated costs. 
• Leveraging serverless cloud services for orchestration and compute ensures that resources are only consumed when needed, minimising operational overhead. 
• Cost monitoring and attribution through the use of for example BigQuery job and table labels provide visibility into data processing costs. It enables easier optimisation and budget management. 
• Sandbox environments allow developers to work with data without incurring the costs associated with full production datasets. This optimises development expenses. 

In some cases, smaller organisations can sometimes remain within the free tiers of certain cloud services. 

6. Fostering Collaboration and Innovation

A well-documented and easily accessible data foundation empowers business users to engage in self-service analytics, reducing their reliance on the data team for basic reporting and analysis. This frees up data professionals to focus on more complex analytical tasks and strategic initiatives. 

The Technological Components of a Data Platform

A modern data platform or foundation is more than just a data warehouse. It is a comprehensive and integrated ecosystem designed to streamline the entire data lifecycle. It encompasses various key components and principles:

• Cloud Data Platform (e.g., Snowflake, BigQuery etc.): A scalable and serverless platform for storing and analysing large datasets, this platform should be able to manage natively structured and unstructured data
• Data Ingestion Tools (e.g., Airbyte, Fivetran): Services that automate the extraction and loading of data from various source systems into the data warehouse.
• Data Transformation Frameworks (e.g., dbt, Dataform): Tools that enable code-based data transformations within the data warehouse, supporting version control, testing, and documentation.
• Orchestration Services (e.g., Cloud Workflows, Cloud Scheduler): Serverless platforms for automating and scheduling data pipelines and other tasks.
• Infrastructure as Code (IaC) Tools (e.g., Terraform): Solutions for managing and provisioning cloud infrastructure resources using code.
• Monitoring and Alerting (e.g., Cloud Monitoring): Services for tracking the health and performance of the data foundation and providing automated notifications for issues.
• Data Governance Tools (e.g., Dataplex, Data Catalog): Platforms for managing metadata, ensuring data discoverability, and enforcing data governance policies.
• Infrastructure as Code (IaC): Managing and provisioning the underlying infrastructure using code, typically with tools like Terraform. This enables consistent and repeatable deployments of data foundation components across different environments.
• CI/CD Pipelines (e.g., Cloud Build): Automated workflows for building, testing, and deploying code changes to the data foundation.
• Data Security: Implementing measures to protect data from unauthorised access, breaches, and misuse. This includes encryption, access controls, data masking, and auditing.

A well-architected data foundation provides a clear pathway for data to flow from its source systems, through necessary transformations, and into the hands of analysts and decision-makers. By integrating these key components effectively, businesses can create a robust and efficient data ecosystem that fuels their analytical capabilities.

An overview:

Tool Type	AWS	Microsoft Azure	Google Cloud	Databricks	Snowflake
				Runs on AWS/Azure/GCP	Runs on AWS/Azure/GCP
Cloud Data Warehouse	Amazon Redshift	Microsoft Fabric (latest offering) Azure Synapse Analytics (legacy offering)	Google BigQuery	Databricks SQL	Snowflake Cloud Data Platform
Data Ingestion Tools	Amazon AppFlow, AWS Glue Jobs, AWS Glue Zero ETL, AWS Data Pipeline, AWS Database Migration Service (DMS), Kinesis Data Firehose	Azure Data Factory (ADF Standalone, Fabric, Synapse), Logic Apps, Azure Functions, Event Hub, Fabric Eventhouse	Cloud Data Fusion, Cloud Dataflow, Datastream, Pub/Sub, BigQuery Data Transfer Service	Auto Loader, Delta Live Tables (DLT), LakeFlow	Snowpipe, Kafka Connector, Spark Connector, Extensive Partner Network (Fivetran, Airbyte, Matillion, Informatica, etc.) Snowflake Openflow (not before June 3rd)
Data Transformation Tools	AWS Glue Jobs (Spark ETL), Amazon EMR, Redshift (SQL), (dbt common via adapter), Athena (SQL)	Azure Data Factory (ADF Standalone, Fabric, Synapse), dbt	dbt and Dataform	Delta Live Tables (DLT), Databricks Notebooks, dbt	Snowflake SQL, Snowpark (Python, Java, Scala), Dynamic Tables, Streams & Tasks, (dbt extremely common via adapter), Partner Tools (Matillion etc.)
Orchestration Services	AWS Step Functions, AWS Managed Workflows for Apache Airflow (MWAA), AWS Glue Workflows, EventBridge	Azure Data Factory (ADF Standalone, Fabric, Synapse), Astronomer	Cloud Composer (Managed Airflow), Cloud Workflows, Cloud Scheduler	Databricks Workflows	Snowflake Tasks, (Often orchestrated by external tools like Airflow, ADF, Step Functions, Prefect, Dagster), can be executed every 10 sec to foster near real time data processing
Infrastructure as Code (IaC) Tools	AWS CloudFormation, AWS CDK, (Terraform widely used)	Bicep, Terraform, Azure Resource Manager Templates (ARM),	Terraform	Databricks Asset Bundles (DAB), Databricks Terraform Provider,	Snowflake Terraform Provider, SQL Scripts, (Managed alongside underlying cloud’s IaC)
Monitoring and Alerting	Amazon CloudWatch, AWS CloudTrail, Amazon Simple Notification Service	Azure Monitor, Azure Log Analytics	Google Cloud Monitoring, Google Cloud Logging	Databricks Lakehouse Monitoring	Snowflake Resource Monitors, Query History, Task History, Snowsight Dashboards, Account Usage Views, Integration with partner tools
Data Governance Tools	AWS Lake Formation, AWS Glue Data Catalog, AWS Glue Data Quality, AWS DataZone, Macie (Data Security), Amazon Sagemaker Unified Studio Catalog	Microsoft Purview	Google Cloud Dataplex, Cloud DLP (Data Loss Prevention)	Unity Catalog	Snowflake Horizon (Object Tagging, Access History, Masking Policies, Row Access Policies, Column Lineage), Partner integrations
CI/CD Pipelines	AWS CodePipeline, AWS CodeBuild, AWS CodeDeploy (Gitlab CI and Github actions widely used)	Azure DevOps, GitHub	Google Cloud Build	Databricks Repos, Databricks Asset Bundles (DAB)	Integration via SnowSQL (CLI) Native Terraform Provider Native Github INtegration Connectors + External tools (Azure DevOps, GitHub Actions, Jenkins, GitLab CI etc.)
AI/ML Capabilities		Copilot Studio, AI Foundry, ML Studio, Azure AI Services		AI/BI Genie,  Databricks IQ, Mosaic AI, MLFLow	Cortex ML Cortex for GenAI Cortex Analyst Cortex Agents Cortex Intelligence
Data Sharing & Monetisation Capabilities		Azure Integration Services		Delta Sharing, Marketplace, Cleanrooms	Native Data Sharing capabilities since 2017

Step by step plan to build your data foundation

Building a successful data foundation is not a one-time project but an ongoing journey that requires careful planning and execution. The key steps typically involve:

1. Understanding Business Needs: Clearly define the business objectives and the data insights required to achieve them.
2. Assessing the Current Data Landscape: Identify existing data sources, their formats, quality, and accessibility.
3. Defining Data Strategy and Architecture: Develop a comprehensive data strategy that outlines the overall vision, goals, and principles for managing and leveraging data. Design a logical and physical data architecture that aligns with the business needs and technology choices.
4. Selecting Technologies and Tools: Choose the appropriate cloud platform, data warehouse, integration tools, transformation frameworks, and other necessary components based on requirements and budget.
5. Implementing Core Components: Build the initial data pipelines for ingestion, storage, and transformation. Set up essential data governance and security measures.
6. Iterative Development: Adopt an iterative approach, gradually expanding the data foundation to incorporate more data sources and analytical capabilities based on evolving business needs.
7. Continuous Monitoring and Optimisation: Regularly monitor the performance and health of the data foundation and identify areas for optimisation and improvement.
8. Training and Enablement: Provide adequate training and support to data teams and business users to effectively utilise the data foundation.

Collaboration between business stakeholders and technical teams is crucial throughout the entire process to ensure that the data foundation effectively addresses business needs and delivers tangible value.

Success Cases: Transforming Businesses with strong data platforms

Numerous organisations across diverse industries have benefited from building robust data foundations. Check out our customer success stories or take a look at these anonymised examples: 

Building a Data Foundation: start to prepare your future needs now

Building a strong data foundation is no longer a luxury. It provides the essential infrastructure for turning raw data into actionable insights. It fosters agility, ensures data quality and governance, optimises costs, and empowers a data-driven culture. Building a data foundation using modern cloud-native technologies and a clear strategy is essential for organisations preparing for Artificial Intelligence (AI) and machine learning.

Once you have a robust foundation, explore how to evolve your data platform to specifically drive innovation in Artificial Intelligence.