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.
Data fabric and data mesh are two concepts that have gained a lot of attention in the world of data management. While they share some similarities, they have some fundamental differences that are important to understand. In this article, we will explain the difference between data fabric vs data mesh.What is a Data Fabric?A data […]
As the volume and complexity of data continue to grow, traditional data management solutions are struggling to keep up. This is where data lakehouses come in. In this article, we’ll take a closer look at what data lakehouses are, how they differ from traditional data warehouses, and their benefits for businesses.What is a Data Lakehouse?A […]
In today’s digital age, data has become one of the most valuable assets for organizations of all sizes and across all industries. However, with data being generated, stored, and accessed from multiple sources and locations, managing and analyzing it has become increasingly complex.To address this challenge, a concept called a data fabric has emerged in […]
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.