What are the most important feedback goals for data architecture professionals?

- Focus on cost control, resource utilization - Adherence to Data security and governance - Finding easy and fast implementation strategies

Data Architecture is one of the crucial stage for any organization. Innovation, Continuous improvement, communication and collaboration is the pillars for Data Architecture. All the stake holders should be visible as one team for outside world.

Adding to the list are some key points to be considered as feedback goals for Data architects : 1. Ensure that the data architecture complies with regulations, safeguarding sensitive information and maintaining data integrity. 2. Focus on the efficiency of resource utilization, cost control measures, and the overall return on investment in data architectures. 3. Prioritize user experience by soliciting feedback from end-users and key stakeholders to gauge satisfaction with data architecture solutions. 4. Ensure that data architecture adheres to security, and compliance standards.

Communicate the 4 ways data meets business objectives: 1) Increase the value of data assets 2) Maximize return (internal and external) on data assets 3) Reduce data risk (compliance, regulatory, reputational, operational) 4) Minimize expense Which demonstrate your value as well as the importance of data

Aligning data architecture with business objectives necessitates comprehending the vision, mission, and goals of stakeholders, while ensuring the data architecture underpins these elements. This alignment is pivotal for fostering a business-centric focus that resonates with organizational strategies and data prerequisites. Moreover, it entails formulating a data architecture that promotes data integration across domains. Such integration facilitates a more coherent and comprehensive understanding of common metrics and bolsters the organization's blueprint for future applications.

In Data Architecture, before getting into next stage we need to check the following: 1. Analyze data patterns, completeness, accuracy and consistency with the stake holders. 2. Need to set Data Governance and Standrdization 3. Eliminating duplicates in data and also correct the errors in data followed by data accuracy check. This is has to go multiple cycles. 4. Security and regular audits are so important in Data Architecture.

- Continually refine based on feedback to ensure adaptability and precision. - Foster open communication to enhance user dexterity and autonomy. - Cultivate a culture aware of data quality and integrity. - Regularly review and enhance data quality and integrity. - Assess practices, standards, governance, tools, and training. - Maintain data at its natural level to prevent aggregation gaps. - Prioritize data freshness for relevance and reduced discrepancies. - Preserve historical data for traceability and explainability.

Feedback on data quality is essential for data architecture professionals. This involves assessing the accuracy, completeness, consistency, and reliability of the data stored in databases and data warehouses. Feedback can help identify data quality issues and guide efforts to improve data cleansing, validation, and integration processes.

The end goal of any data program is to derive actionable insights for business SMEs. These insights form the foundation for the corporate business strategy. It is pivotal to have comprehensive data profiling rules and standards built to ensure the completeness and validity of our data. Once the data is ingested in the DB/DWH/Data lake, we should build dashboards on top to continuously monitor any inconsistency. In addition, we should have a regular cadence with business users on whether the existing data enables them to make the right decisions. If not, the data team should take their feedback and identify the current cleaning/profiling methodology to improve the overall data quality process.

There are many aspects while building a data driven architecture which needs to be followed as standard practice: 1.data flow and infrastructure associated with it should be robust enough to handle any scenario where there is significant spike in data ingestion all of a sudden. 2.data collection and validation should be given importance once it is in our system. 3. Implementation of real time monitoring of data and maintaining the sanity of it through following various legal frameworks should be implemented so that cyber threats can be avoided.

Once business objectives and data quality and integrity in place we need to concentrate more on data modelling and data indexing. Large data sets split into smaller data sets using the technologies partitioning and Sharding. Frequently used data sets keep it in caching. One more important area we need to concentrate in data architecture is compression and data storage optimizations.

- Monitor Performance: Continuously monitor data ingestion, storage, processing, and visualization for optimal performance. - Choose Data Model: Select a model (relational, document, key-value) based on data complexity and queries for high concurrency and low latency. - Understand Scalability: Familiarize with scalable architecture principles, including horizontal and vertical scaling. - Optimize Storage: Align infrastructure with workload needs and optimize settings like buffer size and caching. - Implement Optimization Techniques: Utilize indexing, query optimization, and (de)normalization for better performance.

Feedback on performance is crucial for data architecture professionals. This includes monitoring and optimizing the performance of databases, data pipelines, and data storage systems. Performance feedback can help identify bottlenecks, optimize queries, and improve overall system efficiency.

The primary objective of any designed solution is to maintain a continuous feedback loop and adaptability to change as required. Therefore, implementing a process that consistently evaluates performance metrics and facilitates a feedback mechanism for redesign or adjustments is an integral component of all architecture solutions, ensuring sustained optimal performance

Surprisingly, the same could be said about any aspect of an IS landscape, whether it is data, architecture, operation model, governance, etc... it's very probable that those articles is just AI generated content and all this is just a POC for making ML cheaper, because there are so many of us who will be fooled into entering relevant reactions here. Interested to see how mines will be incorporated in next articles ??

In Data Architecture, Make sure for all the team members / stake holders "continuous learning mindset" is the key because based on quality and quantity of data we need to change the architecture for better improvements.

In this new era upskiling of resources should be the priority for any organization which is establishing itself on data platforms. The team members need to be equipped with latest market trends such as Gen AI and various LLM models so that they can help the organization focus on cutting edge technology improvements for the growth of the company. The focus should be collaboration among themselves on fast fail approach to identify new set of implementations.

Finally, we can look at scalability of the architecture and scalability of the infrastructure. Monitoring and regular reviews and audits are most important in complete life cycle of Data Architecture. Keep the documentation upto-date as well as it should be available for all the stake holders/teams.

Business objectives: Accommodate diverse users like owners, analysts, scientists, and consumers through flexible architecture and agile feedback loops. Data quality: Checks/recons should be the foundation of the delivery. Imagine sectors like finance, healthcare, and pharmaceuticals. Performance: Easier for cloud based solutions to leverage elastic & serverless options. While operational costs will increase initially, the benefits often outweigh the impact until a performant solution is in place. Innovate strategically: Test new tools for BI & integration, but plan core architecture (storage/DW) changes carefully with stakeholder buy-in. Virtualizing data can help insulate the users until the new tools are tried and regression tested.