How can you standardize data in ETL processes for better governance?

In my experience, ETL processes play a pivotal role in data governance, facilitating the extraction, transformation, and loading of data into a centralized repository. Standardizing data in ETL processes is crucial for ensuring consistency, accuracy, and compliance. It enhances data quality, simplifies integration across systems, and supports effective decision-making. Challenges may arise from disparate data sources, varying formats, and evolving standards. To standardize data in ETL processes, employ data profiling tools, establish mapping rules, and validate against predefined standards. Examples include converting date formats, normalizing units, or ensuring consistent naming conventions.

The definition of data governance varies from company to company. More mature companies defines data governance as the set of policies that manage data for the entire company. ETL (Extract, Transform, and Load) techniques is just one of many methods that Data Engineers use to get data into the company. (Typically into a Database or Data Warehouse) Ideally data governance guides the entire lifecycle of the data and its usage across the company. This enables companies to truly be "data driven" because now the whole company knows the rules behind the data being used to make decisions. Also non-technical teams like legal, marketing, and other stakeholders can use the data and be ensured of its integrity across the company.

A padroniza??o de dados assegura consistência e uniformidade, melhorando a qualidade, reduzindo erros e simplificando o processo de ETL. Isso economiza tempo e dinheiro para empresas que se utilizam desse processo. Vale ressaltar que dados padronizados permitem processamento mais rápido e preciso, reduzindo o tempo do processo. Além disso, contribui para relatórios e análises de melhor qualidade, facilitando a identifica??o de tendências e padr?es para tomada de decis?es de negócios.

A padroniza??o ajuda a garantir a consistência dos dados, facilitando a integra??o de diversas fontes. Isso melhora a qualidade dos dados, facilitando as análises. A padroniza??o economiza tempo nos processos de ETL, acelerando a disponibilidade dos dados para análise. Sistemas padronizados s?o mais fáceis de manter, especialmente quando as regras de padroniza??o s?o estabelecidas e documentadas. Isso simplifica a implementa??o de solu??es de BI e a gera??o de relatórios. Dados padronizados s?o escaláveis e proporcionam maior flexibilidade para adaptar-se a mudan?as nos requisitos de negócios, contribuindo para o sucesso das opera??es analíticas e de tomada de decis?es.

ETL is Extract- Transform- Load , as defined in sequence , we build a process to Extract Data from Heterogeneous sources and stage or push to DataLake where Raw data exists. Then we can apply Transformations by using SQL native or tool provided functionality and standardize all rows into common format. during process of transformation, if we come across unexpected data that cannot be standardised to format can either be set to default value and generate a report with untransformed data to source system owners so this can be fixed in source and further in EDW system. This is one of the ways to handle governance. Many organisations follow different methods as per their Data Strategy and commitment towards trustworthy data

In my opinion, ETL is a standard practice to onboard heterogeneous sources, transform data on the fly before loading it to the data platform. ETL can be implemented with ETL tools like Informatica, Datastage, Pentaho etc. these tools have some of the built in functions, transformers that allow you to define the checks to validate the data standards, data quality before you load to the databases/tables. You can also report them using ETL native features. With varying set of tools and features, you may not be able to standardize the process to validate data across applications until you want to use db native features and validate data upon loading data to the raw or landing or staging layer. This can be standardized at db level.

This question is tricky because of the key words "Standardize, governance, and ETL" Standardization and governance are typically business functions designed to create order around the data used within the company. ETL is a technical process / tool that utilizes the data. For data processed internally, governance and standardized data makes ETL systems easy to develop because there is a common rule set for the ETL system to follow. For bringing data into a company, ETL systems can help convert external data and store it into internal data standards. Governance typically helps define the standards that ETL systems follow. ETL then helps enforce those standards whenever it processes data.