In the age of big data, sound metadata and data management are essential for organizations. But what does metadata management exactly entail? This lack of data visibility creates costly consequences: users lose 30–40% of their time simply trying to find and identify useful information. The problem is no longer data scarcity, but the inability to understand, govern, and exploit […]
The rise of big data, coupled with the ever-growing need for data-driven insights, has led organizations to continuously adapt and refine their data strategies. Centralized data lakes and data warehouses have been staples for many years, but as organizations scale and data becomes more decentralized, these structures face limitations.
In today’s world, data is the driving force behind successful businesses. Efficient data management, particularly reference data management, is key to making informed decisions and driving growth. This article aims to demystify the concept of reference data management for those interested in exploring data knowledge options, discussing its definition, benefits, and why it’s an essential part of successful business operations.
For many organizations, “Becoming data-driven” is a long-term goal with no real path set to achieve it. Often, even starting the journey of organizational data management can be a daunting task that doesn’t offer a one-size-fits-all first step. Implementing the roles of Chief Data Offers (CDOs) and Chief Data Analytics Officers (CDAOs) is essential for accelerating organizational change toward a data-centric culture working to achieve data-driven business goals.
Data lineage tracks and manages data history as it moves through an organization. It visualizes the data lifecycle, including its transformations and uses, from acquisition to disposal. Data lineage is also a crucial component of data governance. Organizations handling protected or sensitive data, such as financial institutions, healthcare organizations, and technology companies, must be able to demonstrate compliance with regulations such as GDPR and SOX.
Data management is an essential aspect of any organization, large or small. Having a clear understanding of the data you have, how it is organized, and how it is used is crucial for effective decision-making and data-driven strategies. Two tools that can help with this process are data catalogs and data dictionaries. While these two tools may seem similar, they actually serve different purposes and have some key differences.
Finding the right information in your organization’s vast sea of data is no walk in the park. When it comes to efficient and meaningful metadata management, you don’t have to be lost! We gathered for you the top five best practices you need to unlock your data’s power in 2023. By following these steps, you’ll be able to take control of your organization’s data and use it to its full potential. Let’s get started!
Active metadata management is a new type of data management in which active metadata is used to give clear and understandable information to support business decisions.
In the age of big data, sound metadata and data management are essential for organizations. But what does metadata management exactly entail? How is it done? What are the benefits of managing metadata?
Knowing how to easily identify, list, and access reference data has become a must-have skill for any company that seeks to be data-driven. So, what is reference data and what implications does it hold for your operation’s performance? Keep reading.
Simply put, master data management helps you manage the company's master data. Find out everything you need to know about master data management, from its qualities to its limitations.
Metadata has long been the poor relation of IT to data. Until then, there was little interest in exploiting these descriptions of information. Time has done its work. After an initial phase of euphoria generated by the business potential created by big data, enthusiasm is waning due to the difficulty of exploiting the data collected and existing data. According to recent Gartner studies, only 10% to 15% of the data owned by the company would be used; The rest consists of redundant, trivial, and other unknown data.
