Data needs to be effectively managed, protected, and leveraged to derive valuable insights. And that is where data governance frameworks come into play. Data governance frameworks provide a structured approach to establishing policies, processes, and guidelines for data management, ensuring data integrity, privacy, and compliance. In this article, we will delve into the importance of […]
Business glossaries and data catalogs play vital roles within data management. They are essential components in virtually any data architecture, but their purposes and interconnections are not always clear to everyone and as such worth exploring.Exploring the relationshipsA business glossary and a data catalog are closely related components within the field of data management. They […]
Data quality management is a critical component in the successful realization of your data strategy, and there are several hot topics that are currently gaining traction in this area. Here are some of the latest trends in data quality & data observability: DQaaS or DQSaaS Hybrid Usage & Island Hopping. Machine Learning has hit the […]
Reducing a 24-hour ETL process to 43 minutesFrom cereal to potato chips, Kellogg’s puts some of the world’s most popular packaged foods on grocery shelves every day. But its supply chain dashboards, powered by Hadoop and SAP Object Data Services, made it impossible for managers to get the fresh data necessary for daily profitability analyses. […]
As the world becomes increasingly digitised, organisations are generating more data than ever before. 80% of that data however remains untapped. This structured/unstructured, unprocessed data is known as dark data, and it has the potential to be a goldmine of insights for organisations.
In recent years, Environmental, Social, and Governance (ESG) has become a buzzword in the corporate world. Investors and stakeholders are increasingly concerned with a company’s commitment to sustainability, social responsibility, and ethical practices. As a result, many companies are now incorporating ESG factors into their decision-making processes. But what is the link between ESG and […]
Data sharing agreements and contracts are important to ensure that data is shared in a responsible and safe manner, and that all parties involved understand their rights and obligations. They help to establish trust and transparency between parties, and can help to prevent legal and financial consequences that may arise from data breaches or misuse.
This article explores the key differences between data observability and data curation, two important concepts in the world of data management. Data observability involves monitoring data in real-time to identify any anomalies or issues, while data curation focuses on managing and maintaining data over its entire lifecycle to ensure its accuracy and reliability. While there are some similarities between the two concepts, such as their importance for ensuring data accuracy and reliability, they have distinct differences in terms of their focus, purpose, and approach. Both concepts are essential for organizations that rely heavily on data analytics to make informed decisions.
This article explores the key differences between data observability and data curation, two important concepts in the world of data management. Data observability involves monitoring data in real-time to identify any anomalies or issues, while data curation focuses on managing and maintaining data over its entire lifecycle to ensure its accuracy and reliability. While there are some similarities between the two concepts, such as their importance for ensuring data accuracy and reliability, they have distinct differences in terms of their focus, purpose, and approach. Both concepts are essential for organizations that rely heavily on data analytics to make informed decisions.
As the volume and complexity of data continue to grow, organizations are looking for powerful and flexible solutions to manage, store, and analyze their data. Translytical data platforms are a new type of database management system that combines the capabilities of transactional and analytical databases. Unlike traditional databases that are optimized for either transactional processing or analytics, translytical data platforms enable businesses to perform real-time analytics on transactional data, simplify data architecture, improve data quality, and save costs by eliminating the need for complex ETL processes and multiple databases. With the ability to handle structured, semi-structured, and unstructured data, translytical data platforms are the future of data management and analytics, providing businesses with faster insights and improved decision-making.