Data strategy trends for 2025 reflect a growing paradox: While organizations race to adopt generative AI and automation, their disconnected data initiatives are creating new silos rather than breaking them down. DATAVERSITY’s® 2024 Trends in Data Management survey highlights this growing challenge, with 68% of respondents citing data silos as their top concern – up 7% from […]
The Impact of Advanced Data Lineage on Governance
In today’s data-driven business landscape, data quality – the availability of usable and business-ready information – remains a significant and worsening challenge for many organizations. To mitigate these effects, businesses need swift resolution of data issues with transparent and trustworthy information. However, in our fast-paced digital environment, complex data architectures with more system variables make it difficult to understand the problems. […]
Common Data Integrity Issues (and How to Overcome Them)
To say data has “integrity” means that it can be trusted and relied upon and is ultimately useful. Data integrity also conveys a sense of unity and completeness. The greatest challenges to ensuring that data has integrity are any characteristics or events that detract from the data’s usefulness, trustworthiness, and reliability, as well as anything […]
Innovating with Data Mesh and Data Governance
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 […]
Understanding Data Governance Maturity: An In-Depth Exploration
In the rapidly evolving landscape of data management, the concept of data governance has gained prominence as organizations strive to manage, protect, and leverage their data effectively. While developing and implementing a data governance program, it is important to consider how to measure the organization’s current state of data management and develop a plan for […]
Women in Data: Meet Liz Henderson, the Data Queen
The latest installment in our Q&A series with women leaders in data features Liz Henderson, aka the Data Queen. (Read our previous Q&A here.) Liz Henderson, also known as the Data Queen, has held many roles over the past two decades: head of data management, data governance director, chief data and analytics officer, business mentor, board […]
Quantifying the Value of Data to Business Leaders
When you hear the phrase “data monetization,” you might think of selling data to a third party – along with the many ethical and privacy issues that go with it. While this practice plays a significant role in our modern economy, there is another type of data monetization that receives less buzz but is equally […]
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 […]
How to Become a Data Steward
An essential role in any data-driven organization, a data steward is expected to manage and protect valuable data resources while ensuring that the integrity of the data remains intact. A steward must ensure that the data is useful to the company and meets its business objectives. Data stewards are also responsible for monitoring the data […]
Self-Service Analytics: Pros and Cons
Self-service analytics empowers the non-technical users in an organization. Traditionally, data analysis was the domain of specialized data scientists or IT professionals with skills to manipulate and interpret complex datasets. In self-service analytics, user-friendly tools enable ordinary business users to conduct data analyses without expert knowledge or support. These tools typically feature user-friendly interfaces, drag-and-drop functionalities, and pre-built templates […]