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 […]
What Is a Database Management System (DBMS)?
A database management system (DBMS) describes a collection of multiple software services that work together to store, compute, maintain, structure, and deliver the data as part of a product. This platform also provides metadata, a system of data labeling, so that engineers and users can understand and map what entities and properties are available and their […]
The Impact of Generative AI on Data Science
As businesses adopt generative AI, a technology that creates new content and finds patterns, what will Data Science, the processes and activity geared to getting insights from big data, look like? How will this kind of analytical work change? Will there be an increased or decreased need for data scientists? How should businesses adapt their information […]
Case Study: Establishing a Data-Literate Culture
Imagine a rapidly expanding small business with a 20% yearly growth tasked with establishing and maintaining a data-literate culture. Then, add a new Data Architecture upgrade with considerable changes to the business logic to this situation, causing a full-on panic. This journey, marked by confusion over data definitions and anxiety over system changes, describes the situation that Brooke Gajownik, director of […]