Unlocking Business Value: Best Practices for Building a Scalable and Efficient Data Platform

In today's hyper-competitive business landscape, data is the new oil, and building a scalable and efficient data platform is no longer optional but essential for business success. However, the process can be overwhelming and confusing.To build a scalable data platform, organizations can leverage a variety of available cloud technologies. By leveraging the scalability, flexibility, and cost-effectiveness of the cloud, organizations can quickly and easily build a data platform that is tailored to their specific needs and that can evolve as their data needs change.

Here are some best practices to consider when building a data platform:

• Define the business objectives: Defining business objectives is a critical step in building a data platform because it helps organizations focus on the most valuable areas of their platform. By clearly defining the goals and objectives that the platform is intended to support, organizations can ensure that resources are allocated appropriately, and that the platform is designed to meet the specific needs of the business. This can help organizations avoid investing resources in unnecessary areas and instead prioritize the most critical components of the platform that will have the most significant impact on business outcomes.
• Building a Data Model that aligns with Business Objectives and Reality: It's crucial to ensure that your organization has a high-fidelity data model that accurately represents the reality of your business environment. This means having a clear understanding of the data model's structure, relationships, and constraints and ensuring that it aligns with your business objectives. This will enable you to make data-driven decisions based on reliable information, leading to better business outcomes. A high-fidelity data model is the foundation for answering critical business questions and making informed decisions that drive your organization's success. **This bullet is contributed by Richard Watson
• Identify the data sources: To build a data platform, organizations need to identify the data sources that will feed into the platform. These sources could include transactional systems, log files, sensors, social media feeds, and more. Leverage the free assessments provided by major cloud providers and their partners
• Select a data ingestion and processing technology: Organizations will need to choose a technology for ingestion and processing of data from the identified sources. Options could include cloud-based data ingestion and processing tools such as Spark,Apache Kafka or Amazon Kinesis, or a more general-purpose data processing platform such as Google Cloud Data Fusion or Azure Data Factory. See: Data Ingestion: 7 Challenges And 4 Best Practices (montecarlodata.com)

• Select a data storage technology: Organizations will need to choose a technology for storing the data ingested and processed by the platform. Options could include a data warehouse such as Amazon Redshift or BigQuery, a data lake such as Amazon S3 or Google Cloud Storage, or a more general-purpose data storage solution such as Google Cloud Bigtable or Azure Cosmos DB, Azure Gen2
• Select a data visualization and analysis tool: To enable business insights and decision making, organizations will need to choose a tool for visualizing and analyzing the data stored in the platform. Options could include a business intelligence tools such as Tableau or Power BI, a data visualization tool such as Google Charts or D3.js, or a more general-purpose data analysis platform such as Google BigQuery or Azure Machine Learning.
• Data Management ( Data Governance + Data Quality):

Data Governance:

1. Establish clear policies and procedures for managing data.
2. Assign ownership and responsibility for data governance.
3. Ensure data quality and accuracy through regular monitoring and cleaning.
4. Protect data through proper security measures.
5. Establish a framework for data classification and access control.
6. Maintain compliance with relevant regulations and best practices.
7. Develop a data retention and disposal policy.
8. Provide training and education on data governance to employees.

Ensure Data Quality: Implementing data quality controls are essential for building a reliable and accurate data platform. Inaccurate data can lead to flawed insights and decision making, which can have negative impacts on business outcomes. By implementing data quality controls, organizations can ensure that the data ingested into their platform is accurate and reliable. This can help to improve the accuracy and reliability of the insights derived from the data, leading to better decision making and ultimately better business outcomes. Additionally, by continuously monitoring and improving data quality, organizations can maintain the integrity of their data platform over time and ensure that it remains a valuable asset to the business.

Here is a practical incremental roadmap to implement data quality controls without getting overwhelmed:

1. Start with a data quality assessment.
2. Define data quality standards.
3. Implement data cleansing and enrichment.
4. Establish data quality metrics.
5. Integrate data quality into the data ingestion process.
6. Implement data quality audits.

• Protecting Data Environments:

Data protection is critical for organizations to safeguard their valuable data assets. To achieve this, data leaders and cybersecurity teams must implement a comprehensive data protection framework that includes risk identification, quantification, and reduction capabilities. The scope of the data protection program should cover data warehouses and analytics environments, with data-centric controls used to protect against common risk scenarios, such as unauthorized access and insecure data stores. It's also essential to automate the identification of sensitive data elements and apply appropriate classification levels while integrating data inventories and discovery tools to gain visibility into your sensitive data.

Establishing governance policies and standards, prioritizing a plan of action for data protection, and adequately funding the program are critical components of a successful data protection strategy

Here is a checklist for data leaders and cybersecurity teams to protect data environments:

1. Implement a comprehensive data protection framework to identify, quantify, and reduce risks.
2. Include data warehouses and analytics environments in your data protection program and use data masking or test data management practices in non-production environments.
3. Use data-centric controls to safeguard against common risk scenarios.
4. Automate the identification of sensitive data and apply appropriate classification levels.
5. Integrate data inventories and discovery tools to gain visibility into your sensitive data.
6. Apply data retention policies and prioritize your plan of action for data protection.
7. Staff your data protection program with defined roles and responsibilities and provide training to ensure all staff understand their responsibilities.
8. Ensure adequate funding and technology architecture to support your program.
9. Establish and enforce governance policies and standards to ensure compliance and continuous improvement.

By following these steps and continuing to monitor and optimize the platform, organizations can build a scalable data platform that enables business insights and decision-making. It is important to regularly review and update the platform to ensure that it is meeting the evolving needs of the organization and to take advantage of new technologies as they become available.

The implementation of the DataOps Tech Stack has been a game-changer for Artera's data platform. By utilizing tools such as Azure Synapse, Azure Gen2 Storage, Delta Lake, Azure Functions, Logic Apps, Spark Notebooks,Neo4j,Azure Key Vault, Azure Purview, Power BI Premium, Azure DevOps and Trunk-Based Development. We (Team: smARTERA) have been able to provide a scalable end-to-end solution for data integration, transformation, processing, governance, and security. I strongly recommend these best practices for anyone looking to build a scalable data platform on the cloud.

If you're interested in learning more about our approach, I invite you to reach out to me directly. I am happy to provide insights and answer any questions you may have.