How can you improve data quality with data lineage and provenance?

In the Power BI server, there is an option to view the published data sources and dashboards in a lineage format to understand the connection and flow across sources and reports. This helps to understand the importance of a data source and when modifications are required, it's easier to ensure the changes do not affect the other connected reports. This improves the consistency and performance of the data management platform and brings more confidence in the solutions.

One way to improve data quality is by applying the DAMA (Data Management Association) approach to data lineage and provenance. Data governance is important. Establish an organization for data. Data policies should require the maintenance of data lineage records in order to ensure accountability for data quality. Modern analytics and data management tools offer many ways to create data lineage, ensuring consistency and accuracy in data flows. Adopting these practices together with the DAMA framework enable: - A comprehensive view of the data lifecycle. - Ensures the implementation of policy. - Promote a culture of data quality and responsibility throughout the company.

Assim como em uma notícia os dados também precisam de uma boa procedência. Pensando nisso a análise de procedência do dado que está sendo tratado e trabalhado tem de ser de alta confiabilidade, caso contrário a informação obtida através dele será errônea e parcial.

Combining data lineage and provenance is a great way to manage a large team and data structure when dealing with a lot of projects and changes at the same time. It helps the management to handle the requests and the outcomes in a better way and brings accountability amongst the team members. Through this errors could be easily traced and cross wires across projects can be dealt with ease.

I could list down the below benefits - Enhancing the decision making is the first benefit I could think of; with a clear understanding of where data originates and how it transforms, organizations can make more informed decisions, trusting the integrity and context of the data they use. - Second benefit is improving the Data Quality and Reliability; continuous tracking and documenting of data changes lead to higher quality, consistency, and reliability of data. This can significantly reduce errors and misinterpretations in critical business processes. - In industries where regulations dictate data handling, having a detailed lineage and provenance can simplify compliance, reduce risk, and provide audit trails.

A map of the data journey through the organisation allows controls to be implemented in the most appropriate location considering business/technical constraints, and importantly, to ensure maximum benefit for as many data consumers as possible.

Ocorreram em alguns momentos de minha carreira vários problemas quanto aos dados, quais as áreas de geração e manipulação dos dados, quem detém a verdade (isso é um ponto bem importante em empresas de grande porte) e quais são as pessoas chave para o trabalho em questão, entre outros fatores que muitos já passaram, passam ou passarão por isso entender o todo quanto se trata de dados é crucial para saber onde está e para onde deve seguir, obedecendo cada ponto de veracidade e confiabilidade.