How do you approach data modeling and data integration in a data architecture role?

There are two levels of modeling: a high level and a low level. The high level has two forms, ERD and Key-Based (KB), which help to identify major business entities, establish the scope of business information, and explore details respectively. The low level consists of Fully Attributed (FA) and Transformation models. FA models contain the details of a particular implementation effort, while TM represent a relational model that is optimized based on data volume, expected access patterns, and the capabilities of the warehouse. TM serves as a visual representation of the physical modeling. There are several other modeling designs, such as Relational, Star Schema, and Data Vault models, that are commonly used.techniques in data modeling.

I always will look at existing industry data models from DMBoK, O'Reilly books, industry associations, and prior engagements as a reference for analytical models (star schema, snowflake schema, and 3/4NF). From there I will use the rapid mart approach to design from the business needs backward and not from the available data elements forward. Once the logical model is developed we can interrogate the sources and alternative business needs to ensure the data model is robust.

The needs of the business are always the starting point for data. Low latency data is unlikely to be improved data and is the "inbound", "raw" or "bronze" zone for data. This can be accomplished with ETL, ELT, and virtualization. Normalized and "integrated" data typically is blending together multiple data sources or has simple checks and balances to ensure a level of quality and completeness. This "normalized", "base" or "silver" version of data can be accomplished with ETL. Aggregated data that makes reporting and business analytics possible must be loaded by ETL tools. These "AG", "Gold" or "Dimensional" zones are very specific for business purposes like sales, finance, and labor. And are a result of multiple transformations.

The choice of data integration strategy is dependent on the complexity of the data we aim to integrate. In some cases, an adhoc approach could suffice while in others, a multifaceted integration strategy may be necessary. Creating a robust data architecture strategy is crucial, as is understanding which data supply chain mechanism is required. Additionally, you need to decide which data components and capabilities require as part of the integration process. By taking these crucial factors, you can optimize your data integration strategy for your unique requirements in the various integration strategy such as middleware, storage, analytics/ product integrations.

Data modeling needs specific tools to mode, renew, print, print for a wall, review, reprint and align teams on data delivery and on data consumption. Light tools allow you to model and present a view of the data design model. Medium tools allow you to forward generate data definition language code to generate the data model into the database of choice. Large tools allow for data model repository, delta and difference comparisons. Extraction from one RDBMS and DDL generation into another. Excel, word, and PowerPoint can be data model tools, probably the best is ERWin for agnostic and a large community of users, the ETL tool modelers are good with the ETL nad database in their ecosystem

Any tool can be used for good and any tool can be used for bad. If there are standards that can be maintained in code development and at each step, these can be documented and can present a visual representation of the separate steps of extraction (what are the elements selected, filtered, and organized), transformation (how are the attributes transformed) and load (how are the transformed attributes loaded with insert, update, upset etc..) any of the tools for data integration can be used. As well any tool can be used to call a large procedural block of code hidden in a stored procedure or external call or function. I feel it is more about the technique of the team than the tool selected.

Data Modelling Best Practices are like any standard. If they are followed consistently, the team of data modelers, data engineers, data analysts, business analysts, and consumers agrees with them and understands them and there is flexibility and discussion before they become "law" - they will be good.

Data modeling is a great way to capture data requirements visually, it can be used for defining current state data architecture and future state data architecture. Data models can be tailored to the audience by defining conceptual data model, logical data model and physical data model. Conceptual data model is focused on high level entities and data flow, logical data model is a detailed view of structure of data entities and data elements, it is independent of physical database structure. Physical data model is a detailed view of data entities, data elements and physical characteristics of the database itself.

Just like data modeling standards, there are Data integration standards that can help the operations and day two questions on the type of integration (bronze, silver, gold) or (full, incremental, update).

I have worked with data for over 25 years. The number one challenge that I have seen in organizations is to deliver project value and fail to deliver operational excellence. Poor data hygiene, poor documentation, and adding new code, tools, and solutions without fixing the issues that currently exist. The worst sin is adding more bad code to a lot of bad code. Many organizations will update the technology and the people but not spend any effort on the process. And it is the process that differentiates good from great. Elegant code, process,es and documentation make adding, updating, or fixing so much easier.

Data Architects are now the new rising stars in the industry who are bridging the gap between micro-level business architecture modelling and macro-level data enterprise architecture. Since the architecture needs to address a number of complex aspects of an organization's strategy, the role of the data architect has become strategic. Business architecture artifacts such as the Data models not only serve as design artifacts for storage, integration, and data quality, but also influence the technical architecture of a solution, platform, or technology selection.