Organizations have long struggled with the “eternal data problem” – that is, how to collect, store, and manage the massive amount of data their businesses generate. This problem will become more complex as organizations adopt new resource-intensive technologies like AI and generate even more data. By 2025, the IDC expects worldwide data to reach 175 zettabytes, more […]
Data Governance vs. Data Management
When managers and workers discuss Data Governance (DG), a business program that supports harmonized data activities, vs. Data Management (DM), a comprehensive collection of practices, concepts, and processes dedicated to leveraging data assets, they often use the terms interchangeably. Sometimes, both words point to the same concepts, and these conversations remain productive. At other times, switching between […]
Understanding Data Observability Tools
Data observability tools have become increasingly important as businesses rely more and more on data-driven decision-making. These tools are used to support the data’s reliability, consistency, and accuracy throughout the business. Data observability has become necessary for developing trustworthy data and diagnosing data flow problems that interfere with the business’s objectives. Data observability tools normally […]
Weed Out Bad Data to Make Better Business Decisions
With data powering just about every modern business, the saying “garbage in, garbage out” is more relevant today than it’s ever been. Any data-based application, whether it’s a simple analytics engine or an advanced AI model, is only as effective as the data it’s fed. For any organization to become truly data-driven, they must weed […]
Data Quality Assessment: Measuring Success
The goal of a Data Quality assessment is not only to identify incorrect data but to also estimate the damage done to the business’s processes and to implement corrective actions. Many large businesses struggle to maintain the quality of their data. It is important to remember data is not always in storage and static but […]
Five Trends Shaping Enterprise Data Labeling for LLM Development
In an era where large language models (LLMs) are redefining AI digital interactions, the criticality of accurate, high-quality, and pertinent data labeling emerges as paramount. That means data labelers and the vendors overseeing them must seamlessly blend data quality with human expertise and ethical work practices. Crafting data repositories for LLMs requires diverse and domain-specific […]
Telmai Unveils New Release Aimed at Facilitating Data Observability Adoption
According to a new press release, Telmai, an AI-driven data observability platform, has introduced a significant new release featuring seven pioneering features aimed at simplifying and accelerating data observability adoption within enterprises. With the growing complexity of the data landscape, Telmai’s release addresses the need for continuous, reliable data flow. This release empowers data professionals to […]
Managing Data as a Product: What, Why, How
The concept of managing “data as a product” involves a paradigm shift. By treating data as a product designed for consumer use, rather than a pool of semi-chaotic information, businesses can increase their profits. Many businesses have set up customized data pipelines – or other extreme and expensive steps – in unsuccessful efforts to maximize the […]
What Is Master Data Management and Why Is It Important?
Master Data Management (MDM) is an important method for developing and maintaining the uniformity and accuracy of the organization’s shared “master data.” Master Data Management allows businesses to improve the accuracy and uniformity of their important data assets, such as customer data, product data, asset data, and location data. Master data can be described as an […]
Core Data Concepts for Digital Transformation
Without a clear understanding of core data concepts, communications around implementing an organizational Data Management initiative can become a muddle. As different teams come together to plan and organize data activities, they must integrate what they mean about data with any technologies. For example, take the term “Data Governance.” Data engineers building systems and tools to enable […]