Large organizations want to create a flexible environment to innovate and respond quickly based on new data insights. But at the same time, these businesses want some structure for good Data Quality, data fit for consumption, simplifying and speeding up data access. Using a data mesh, which is a decentralized data architecture (collecting, integrating, and analyzing […]
Getting Started with Data Quality
Imagine burning three trillion U.S. dollars. Businesses do this virtually every year because of poor data quality (DQ). In a data-driven age, organizations cannot afford to waste this time and money. Instead, they need to focus on achieving good data quality through a comprehensive program dedicated to business needs. But how does a company implement an effective […]
Evolving Data Governance in the Age of AI: Insights from Industry Experts
The AI revolution isn’t coming: It’s here, and organizational governance structures are woefully unprepared. While 64% of businesses expect AI to increase productivity, most AI models fail. This result highlights a stark gap between potential and reality. Getting timely and usable data sets for AI training and analysis is crucial for becoming data-driven. At the heart of this multi-faceted challenge lies […]
AI Governance in Action: Practical Insights from a Data-Driven Enterprise
As AI adoption skyrockets, how can organizations harness its power responsibly? AI promises enhanced decision-making and customer interactions, but it comes with ethical, compliance, and infrastructure risks. Consequently, AI governance is crucial to address these risks. In response, many businesses turn to their established data governance (DG), aiming to leverage existing controls and procedures. However, AI introduces […]
Data Product vs. Data as a Product (DaaP): Understanding the Difference
Data quality (DQ), which ensures that data is fit for business and consumer needs, remains a significant challenge and is growing more complex. According to a dbt Labs 2024 report, 57% of survey respondents identified data quality as a challenging aspect to data preparation, up from 41% in 2022. To address these data quality challenges, companies increasingly […]
Combining Data Mesh and Data Fabric
Data silos represent a major business challenge, as noted by 60.9% of organizations in a recent Trends in Data Management survey. Without shared information, companies risk duplication, poor data quality, and missed opportunities for innovation. Consequently, many companies turn to modern and integrated data architectures. When doing so, organizations often consider two main approaches: data mesh and data fabric. Data […]
Fundamentals of Data Collaboration
Data collaboration allows organizations to gain insights beyond what their data provides. By sharing information smartly and selectively with partners, companies can uncover new opportunities and insights beyond their internal repository. Moreover, the emergence of large language models (LLMs) applications – like Chat GPT – and cloud technologies, make this approach more attractive. As businesses become […]
Data Governance and AI Governance: Where Do They Intersect?
Artificial intelligence (AI) is transforming businesses and industries worldwide with new data products and services. A recent Stanford University AI Index Report found that about 55% of companies have already implemented AI in at least one business unit or function. To get the best results, organizations need to connect AI and its data activity with their business strategy. So, […]
Fundamentals of Dimensional Data Modeling
In today’s data-driven business environment, organizations demand reliable and stable business insights to make informed decisions. To cater to this demand, over 60% of companies turn to data warehouses (DWs) to store, manage, and analyze their data efficiently. The success of these DW implementations depends on dimensional data modeling – an analytical approach that organizes and categorizes data for efficient analysis and […]
Adaptive Data Governance: What, Why, How
In DATAVERSITY’s 2023 Trends in Data Management survey, about 64% of participants stated that their companies had Data Governance (DG), the formalization and enforcement of data operations across the company, in the initial stages. Yet, 60.9% listed data silos as the greatest Data Management challenge. If DG is supposed to break down data silos for better insights while ensuring compliance […]