How can you design a cloud data architecture that ensures governance and quality?

Designing a cloud data architecture that ensures governance and quality demands an integrated approach. Start with a clear data governance framework, which sets out guidelines and standards for data access, consistency, and security. Tools like Google Cloud's Data Catalog can help by providing centralized metadata management, ensuring discoverability and understanding of data. Implementing automated data quality processes is paramount. Google Cloud's Dataflow facilitates real-time data quality checks, while BigQuery aids in analytics and reporting. Regularly audit and validate data to ensure adherence to set standards.

Crafting a cloud data architecture with top-notch governance and quality ???? starts with a robust data strategy ????. Clearly articulate data goals and rules, outlining who can access, modify, and delete data ????. Implement data cataloging ????? to maintain a data inventory, fostering transparency. Enforce data quality checks ???? at ingestion points, ensuring only pristine data enters your architecture. With a well-defined strategy, your data architecture becomes a stronghold ?? of trust, compliance, and excellence ??. #DataArchitecture #Governance #Quality ??????

Although we are all wary of boiling-the-ocean, it is important to put a decent amount of time and assessment into your data strategy; make sure you take into account your organisation's goals and the needs of the various data users across the business.

A data strategy is not about picking the latest technology. It's about how data enables and supports your business strategy. It imperative therefore you start with the business. What is the vision and strategic objectives important to your business? And how can data support these initiatives? Only then can you look at the current state environment and how the people, process and technology either support or constrain these efforts. Often you will identify gaps in capabilities preventing your business from doing more with data. It's these gaps which, when addressed will unlock business value.

Your choice will be influenced by your current tech stack, the volume of data, projected growth, and any specific integration needs. For instance: If you're already invested in the AWS ecosystem, Redshift might be a natural choice. If you want serverless scalability and are okay with a consumption-based pricing model, BigQuery is strong. If you need seamless scaling and are looking for a data platform specifically designed for the cloud, Snowflake is worth considering. For big data processing with ML capabilities, Databricks could be a fit. Consider conducting a pilot or proof-of-concept with one or more platforms to see which fits best with your use case.

As per the requirement specific and organizational specific, we have to choose the cloud data platform starting from Azure, AWS, GCP and even more but we have to make sure these key things. Are we getting the proper quality and governance flexibility to modify the platform as per the requirement, Usability to incorporate to the idea we have

Design of the Data pipelines will depend on the some of the following points : 1) Type of Processing ( Batch vs Stream) 2) Latency Requirements 3) ETL or ELT 4) Data Storage ( data lake,delta lake ,data warehouse) 5)Data Sources and Volume(Different data sources (e.g., databases, APIs, logs) may require different pipeline configurations.) 6) Integration with Existing Systems: Evaluate how the data pipeline will integrate with existing systems and tools in your organization, such as databases, analytics platforms, and BI tools. 7) Future Growth and Flexibility: Design your data pipeline with future growth in mind. Consider how easy it will be to add new data sources or modify existing workflows. 4)

When it comes to designing data pipelines not all data is equal. Frequently requirements will dictate all data needs to be provided in near real time, but this can become complex to manage and costly. It's important to drill-down into the rationale behind the ask to make sure that the business can justify the cost of delivering data in near real time. Generally it's likely you will strike a balance between delivering data in near real-time through to micro-batch and batch frequencies.

The data pipeline consists of a series of data-handling steps. It helps move data from various data sources to its consumption step. The data pipeline design should align with the goals of the business requirements. We can build the data pipeline using an ETL pipeline with a data warehouse and a data pipeline in the data lake. All Cloud platforms provide various services to ingest, transform, orchestrate, publish, monitor and visualise data.

Don't get too hung up on choosing between Kimball, inmon or data vault. Think of a cloud platform as a blank canvas which will support all well-designed analytical data models. The most important thing above all else is you design your model correctly to meet the needs of the business and to take advantage of the underlying architecture. If you data consumers can't understand your model, or if it doesn't perform well then it doesn't matter what data model methodology you've chosen in the first place!

The world has moved away from a waterfall style of designing your entire enterprise data model in one go - it takes too long to realise value, and by the time you get to the end things have changed. Think agile: tackle a single business process at a time, and be prepared to incrementally improve and refactor if necessary. There are ways that this can be done if you have the correct design and processes in place.

Make sure you feed your data quality metrics through to your data catalogue, so that users can have an understanding of the quality of each data set and then decide if it is suitable for their purposes.

A real-life example of a cloud data architecture ensuring governance and quality is commonly observed in a healthcare organization. They use AWS's cloud services, including Amazon S3 for data storage and AWS Glue for ETL processes. Data governance policies define who can access sensitive patient information, ensuring compliance with HIPAA regulations. Automated data quality checks using tools like Talend ensure that patient records remain accurate and reliable. Regular audits and user training reinforce governance practices, while metadata management tools like Collibra provide transparency into data lineage and definitions. This architecture safeguards patient data while maintaining its integrity, critical in the healthcare sector.

The data ownership and accountability component is often the hardest part of the data journey. The technology improves and works (most of the time). However, getting alignment around the ownership and governance of data products in the context of a South African market that loves decentralized business operations and has Group functions with limited control/influence, is really challenging. I have however had the fortune of working with forward thinking clients that have recognized those challenges and embraced Data Mesh principles to help their business leaders see the vision, embrace the change, liberate the value and work towards maturing as a Data Driven business. A tailored Data Governance Framework and a Business Value mindset helps.

Data governance is a methodology that ensures data is appropriately set and meets and supports business needs, including operations. Effective data governance ensures that data is consistent and trustworthy and doesn't get misused. Data governance is a core component of overall data management.

an important (toooo often overlooked) component to consider is Data Catalog and Metadata Management. The data catalog is a centralized repository that provides a view of all the data assets within your organization. It contains metadata, which is data about your data.

When it comes to data strategy there is no one-size fit all. the approach businesses need to take, they have to start from the business objective and build a business case, this will inform the business on the next approach which can be either they just need to do data analysis and create reports that will inform the business direction. Businesses need to always consider the external data from the segment they are operating in, for example, a Telco business will have to be aware of developments like 4G and what threats and opportunities this comes with. a painful example is the Nokia case study where they ignore data about the development of smartphones and the OS.