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Choosing a Data Quality Tool: What, Why, How

Data-driven organizations are in a race to collect the information that modern analytics techniques rely on to generate insights and guide business decisions. The ever-growing flow of data into business systems challenges companies to devise new techniques for ensuring the quality of the data as its quantity skyrockets. Data quality tool vendors are rising to […]

A Brief History of Data Ethics

In this digital age, where data is an increasingly integral asset for every organization, the ethical implications surrounding data collection, storage, and usage have become prominent. The evolution and history of data ethics is a complex journey that connects technological advancements with societal values and legal frameworks. Understanding this evolution requires examining key historical milestones and the […]

Improving Data Quality with Data Stewardship

To get value from data, data stewards must understand and apply business requirements. When business ambiguity arises about how to best serve data stakeholders, data stewards need to know how to find out this information and with whom to speak. By doing so, stewards can align fit-for-purpose data with business needs and improve data quality. Data stewards understand […]

What Is Data Trust and Why Does It Matter?

A batch processing system fails on the eve of a company’s deadline for monthly reports, threatening the accuracy of its financials. One of the two systems powering the dashboard of a global supply chain company crashes, and a manager overbooks a transport ship because it displays inaccurate data, causing a costly delay in a customer’s […]

Data Ethics: New Frontiers in Data Governance

Would you feel comfortable serving as a Data Governance consultant for an organized crime family … but not for a brokerage with tax fraud in its past? Could the use of ransomware be considered socially acceptable if its demands benefitted needy children? These hypotheticals might sound outrageous – indeed, they are meant to be provocative – but […]

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 […]

Implementing Data Fabric: 7 Key Steps

Understanding the importance of data integration is vital in the intricate process of implementing a data fabric. Data fabric architecture aims to create a unified and integrated environment for managing an organization’s data sprawl across various platforms and systems. Within this context, the data integration step ensures disparate data sources are seamlessly connected, enabling a fluid exchange […]