What are the key components of an effective data analytics and machine learning architecture?

Based on my experience in ETL and other data processing tasks, I've found that precise definition of the data source from all angles enhances the overall efficiency and effectiveness of the process. Incorporating Data governance into the source data definition process serves as a dual-purpose strategy, accomplishing both tasks effectively, given their interdependence and mutual support.

The architecture should focus on data preprocessing and feature engineering beyond storytelling and data visualization. These stages impact the model's predictive power and generalization ability. Furthermore, fostering a culture of collaboration with domain experts and stakeholders ensures alignment with business objectives and enhances the interpretability of machine learning outcomes. Also, implementing model explainability techniques enhances trust and transparency, crucial for widespread adoption and acceptance within the organization.

In my experience definition of source is important. Organization data goes through various hops and multiple transformations. Identifying the root source is important to understand the data journey. Source has to be a system that has defined input and output. Once the sources are identified , they needs to be documented using data catalog tool and measures for quality using data profiling tools.

Data sources should be well-cataloged: - Data content (explaining, what's in there by business words) - Owners of the data, - Structure, formats, refresh cadence, - Sensitivity (privacy requirements), data usage restrictions (if any), and etc. - Data source lineage (upstream and downstream) - Repository of existing analytics scenarios, models where this source data is has already been used with.

Proper analysis of Data Sources form the core for any Data Governance journey. These could be structured or unstructed and with different variety, varecity and volume. We need to have a strong infrastructure architecture to support storing and analysis of the data structures to obtain valuable insights.

The second most important would be profiling of the data and then comes the data integration and ETL. Data anomalies and data type issues can be identified by profiling the data . In the data integration and ETL process the most important part would be when trying to join different tables where we could get keys mismatched because of the dependency tables data load.

Data Integration is an important component of the Governance that would enable us to transform the data into usable insights. If you enable team to provide near real time integrations, enhance data quality, provide technical and business data lineage. Also could enable the system for scaliabiloyy and performance.

While performance, scalability, and reliability remain crucial, the perception of data reliability hinges on the customer's viewpoint. Therefore, tailoring the data implementation to a single customer's needs or perspectives shapes and dictates that particular viewpoint. Ensuring accurate determination, definition, and execution of all perspectives constitutes a process that may require several iterative cycles of design, development, and execution. As a result, maintaining flexibility for updates within the ETL process stands as a significant architectural objective to be achieved in the design.

Make ELT and transform logic simple and scalable at the same time. Keep a clear distinction between where dimensional (state) data is, and where activity (fact) data is. Preserve granularity from sources, and if grain needs to change (aggregates), make a clear justification for why it's done so, and keep the option to drill-down to lower grain (show data lineage to upstream lower grain). A data quality, and reconciliations need to be monitored on sources as well as on produced models.

In my experience, data definition or the metadata is one of the key aspect in data integration/ETL process. A well curated & well thought metadata helps save from numerous data integration challenges as well as helps build robust & precise governance & reporting solutions.

Data storage and processing platforms play a vital role as they serve as the backbone for the ultimate data presentation to the customer. Ensuring the availability, durability, and consistency of the storage solution, as required by the front-end analytics solution, facilitates a seamless and effective presentation layer for the final output.

Minimize data duplications in an org, and minimize code duplications in processing logic. At the same time, preserve data on transform stages, so you are ready to troubleshoot or re-process data easily if needed. Big data can be partitioned and rolled up based on its "coldness" and access patterns - to optimize storage utilization, as well as increase performance for access/analytics. Try utilizing popular solutions like Lakehouse to share a single copy of the data across multiple tech tools and lines of business within a data mesh environment.

It is imporant component of the architecture to plan for the proper data storage and processing technologies include lake house data fabric to enable availability, durability and consistency

Choose the right machine learning model and algorithm. You should select a model and an algorithm that are suitable for your data and your problem. You should also experiment with different parameters, features, and techniques to optimize the performance and accuracy of your model.

Data Analytics and Machine learning is important component of the today's architecture on lake house. Today's generative AI has taken steps to almost 0 training and few prompt to get the models to work and solve customer problems from varying industries.

It’s very important to make sure you can maintain, improve and run the models in production serving up valuable insights through certain customisations/abstractions. Your MLOps pipeline design is probably the most valuable asset in this area .

Data Visulalization and reporting is another important component of the architecture to enable proper presentation of the information that could be consumed by organizations.

Utilizing storytelling as a captivating presentation technique has consistently proven effective for all types of audiences. Therefore, employing reporting and visualization techniques that embody this approach has always been the secret to my success.

I agree with Vidya that data governance encompasses all the data processes. That's why I'd refer to the governance of data processes. But in my view, governance is more a discipline by nature (based on request and control) than a process (ie a sequence of actions triggered by events) although reaching a governed data state does for sure require several trial/error! What I mean is that the elements of the data governance (ie data policies, roles and responsabilities and data architecture) are much more stable than the data processes themselve. Just like a governement is nominated (ie role and responsabilities) and execute a program (ie architecture) to deliver on promises (ie policies) till the next election!

Undoubtedly, data governance is a crucial process that encompasses all the aforementioned data processes. It's essential to remember that data governance is not a one-time task; rather, it's a recurring process that requires adaptation over time. This may be prompted by shifts in governmental security policies, alterations in business strategy, or data modifications necessitated by organizational changes. Therefore, it's crucial to acknowledge that data governance is an ongoing process that should be integrated into all SDLC projects to ensure precise governance.

I'd highlight the top critical data governance components to be focused on (better going it at the beginning of establishing any data system): - Data ownership, stewardship - Data quality (it provides trust in the data) - Data compliance policies and processes (privacy, security, PII, GDPR, regionality, etc.) - Access and security, data sharing

Data Governance and management is no doubt a crucial component in the overall ecosystem. A robust data governance methodology & framework will definitely help organizations be compliant with regulatory requirements as well as avoid cost incur in data breaches and loss.

Data Governance is a crucial part of any Architecture design where the policies and frameworks are designed for handling the data across different components of the architecture. Having a robust Data governance framework helps organisations in wading through ever changing and complex regional regulations. It’s a continuous activity and requires periodic review with management. Of course the success of this framework lies in collaboration with all the stakeholders who have to follow the policies and guidelines that’s are an outcome of this framework.

Effective communication and stakeholders management is needed for a successful implementation of this architecture. This can be done by employing good Project Management principles. We should be able to track the deliverables along with the costs to ensure that the outcome is inline with our initial goals and objectives. We also need processes to handle any scope creeps or changes during the execution phases. Net net Project management is an essential requirement for any successful Data Analytics project.