What emerging data governance trends can help ensure data integrity?

Cela fait maintenant deux décennies que les datalakes ont émergé petit à petit puis se sont grandement démocratisés vers les années 2010. On s'est alors vite rendu compte du grand défi que cela posait : un pêle-mêle de toute sorte de données, regroupées au même endroit certes, mais dont personne n'a plus le contr?le. --> la naissance du DataSwamp ( marécage de données ) Les metastores, qui sont des catalogues sur les metadatas ont peu à peu vu le jour. (L'un des plus connu et ancien est hive metastore.) Depuis plusieurs ont émergés, chacun avec ses particularités et ses avantages. Glue Crawler, un exemple parmi d'autres, a permis de faciliter ce catalogage en l'automatisant et permettant de découvrir ce qui se cachait dans les DataSwamp.

From my perspective, data cataloging is pivotal in managing and understanding organizational data assets. By creating and maintaining a comprehensive metadata repository, it enhances data engineers' ability to locate, comprehend, and trust the data they require. This process also aids in documenting and tracking changes and dependencies, contributing to effective data management. Moreover, data cataloging plays a vital role in enabling data governance by offering visibility, transparency, and accountability for data assets. It facilitates data discovery, access, and collaboration among stakeholders, establishing a foundation for organized and informed decision-making throughout the data lifecycle.

In my opinion, the area of data governance is dynamic and therefore new trends may emerge, especially that some companies are using end to end platforms in Data & AI. The relevant points from my perspective are: AI and machine learning in data governance, Data catalogs and metadata management, Automated data quality management, Real-time data governance, Data Ethics Frameworks, Privacy and compliance of data.

Data governance is the process of managing, accessing, and using data in a way that ensures its quality, integrity, and security.Data cataloging: Data cataloging is the practice of creating and maintaining a comprehensive inventory of data assets, along with their metadata, definitions, and usage. Data cataloging helps data users find, understand, and trust the data they need for their analysis and decision-making. Data cataloging also enables data governance by providing visibility, traceability, and accountability of data across the organization.

My perspective is that data lineage is essential for maintaining data integrity. It empowers data engineers to validate accuracy, completeness, and validity, ensuring the reliability of information. Beyond this, it serves as a cornerstone for effective data governance, facilitating auditability, compliance, and impact analysis. By tracking the history and dependencies of data, it not only resolves anomalies but also provides valuable insights into the business value and overall quality of the data, enhancing decision-making processes throughout the data lifecycle.

Data lineage is the documentation of the origin, transformation, and destination of data throughout its lifecycle. Data lineage helps data users and data stewards track the data flow, identify the data sources and dependencies, and assess the data quality and impact. Data lineage also supports data governance by ensuring data accuracy, consistency, and compliance.

Acrescento que é muito importante sempre que possível criar processos automatizados para verifica??o da qualidade dos dados. Utilize ferramentas para automatizar o gerenciamento da qualidade de dados assim como todo o ciclo de vida desde a ingest?o dos dados até o consumo dos mesmos. Implementei com o meu time de engenharia de dados processos completos de governan?a de dados 100% automatizados utilizando ferramentas Opensource (OpenMetadada e Datahub) e também servi?os gerenciados das Cloud Providers (AWS e Azure) gerando muito valor para os clientes. Os resultados foram maravilhosos.

data quality is paramount for meeting the expectations of data consumers. Accuracy, completeness, consistency, timeliness, relevance, and usability are crucial dimensions. Data quality is integral to data integrity, shaping the reliability and trustworthiness of outputs. It significantly influences data governance by necessitating well-defined policies, standards, and rules. Improving data quality involves employing techniques like profiling, cleansing, validation, monitoring, and reporting. Prioritizing these aspects ensures data aligns with desired quality levels, fostering reliability and supporting effective decision-making processes.

Data quality is the measure of how well data meets the expectations and requirements of data users and data consumers. Data quality encompasses various dimensions, such as accuracy, completeness, timeliness, validity, and consistency. Data quality management is the process of defining, monitoring, and improving the data quality across the organization. Data quality management also facilitates data governance by enhancing data reliability, usability, and value.

In my view, data security is paramount in safeguarding data from unauthorized access, modification, or destruction, ensuring its integrity. Protection against breaches, leaks, or corruption is crucial for maintaining the confidentiality, availability, and authenticity of data. Moreover, data security plays a pivotal role in supporting data governance by enforcing compliance with laws, regulations, and ethical standards. Managing access rights based on data sensitivity and stakeholders' roles further enhances overall data security measures. In essence, a robust data security framework is indispensable for preserving the trustworthiness and ethical handling of sensitive information.

Data security is the protection of data from unauthorized access, use, modification, or disclosure. Data security involves various techniques, such as encryption, authentication, authorization, and auditing. Data security also enforces data governance by safeguarding data confidentiality, integrity, and availability.

In my perspective, data ethics is crucial in applying ethical principles to the entire data lifecycle. By guiding the collection, processing, analysis, and sharing of data, it becomes a cornerstone for ensuring data integrity. Emphasizing fair and beneficial use, data ethics respects and empowers stakeholders, contributing to a culture of responsibility, accountability, and transparency within data governance. Addressing social and ethical implications, including privacy, consent, bias, and fairness, establishes a framework for ethical data practices. Ultimately, integrating data ethics not only upholds integrity but also fosters a trustworthy and socially responsible approach to handling data.

Data ethics is the application of ethical principles and values to the collection, processing, and use of data. Data ethics considers the impact of data on individuals, groups, and society, and aims to ensure data fairness, transparency, accountability, and responsibility. Data ethics also guides data governance by promoting data trust, respect, and justice.

One of the emerging data governance trends that can help ensure data integrity is the use of blockchain technology. This technology can be used to create an immutable record of all data transactions, making it nearly impossible to tamper with or alter data. Another trend is the use of data lineage, which tracks the origin and transformation of data throughout its lifecycle, allowing for better data quality control. Additionally, implementing data quality rules and data validation checks can help ensure the accuracy and consistency of data. Finally, the adoption of privacy-enhancing technologies, such as differential privacy, can help protect sensitive data while still allowing for valuable insights to be gained from it.