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Demonstrating the Value of Data Governance

As organizations strive to become more data-driven, they increasingly recognize the importance of Data Governance (DG), a business program supporting harmonized data activities. However, business leaders, colleagues, and workers often express confusion about DG policies and need clarity around its value. This article tackles this issue by exploring the top 10 ways for effectively articulating the value […]

Data Integration Tools

Data integration tools are used to collect data from external (and internal) sources, and to reformat, cleanse, and organize the collected data. The ultimate goal of data integration tools is to combine data from a variety of different sources, and provide their users with a single, standardized flow of data. Use of these tools helps […]

Women in Data: Meet Classification Guru Susan Walsh

To celebrate International Women’s Day, we’re kicking off a new Q&A series with women leaders in data. Our first installment features Susan Walsh, aka the Classification Guru.  Susan Walsh didn’t always aspire to be a data classification expert. But after spending a decade honing her skills – cleaning and classifying erroneous data – she founded […]

Metadata Governance: Crucial to Managing IoT

The Internet of Things (IoT), devices that produce and consume data through the internet, will likely comprise over 207 billion devices by the end of 2024. These widgets generate, consume, and send vast data over business networks. As a result, organizations must include IoT in their Data Governance programs to ensure better integration and legal compliance. Without effective governance, firms […]

How to Become a Data Engineer

The work of data engineers is extremely technical. They are responsible for designing and maintaining the architecture of data systems, which incorporates concepts ranging from analytic infrastructures to data warehouses. A data engineer needs to have a solid understanding of commonly used scripting languages and is expected to support the steady evolution of improved Data Quality, […]

A Brief History of Generative AI

Generative AI has a fairly short history, with the technology being initially introduced during the 1960s, in the form of chatbots. It is a form of artificial intelligence that can currently produce high-quality text, images, videos, audio, and synthetic data in seconds. However, it wasn’t until 2014, when the concept of the generative adversarial network […]

The Impact of Data Silos (and How to Prevent Them)

Data silos often develop unintentionally within businesses, catching leaders by surprise. They hinder cross-departmental collaboration while giving rise to inconsistent data quality, communication gaps, reduced visibility, and increased expenses. The gravity of impact can be gauged from a report by Forrester research, which finds that knowledge workers spend an average of 12 hours a week “chasing data.” […]

Common Master Data Management (MDM) Pitfalls

Leaders need to trust data within the organization to make sound business decisions. So, many turn to master data management (MDM), a solution to get and keep uniform and accurate data that increases business value. Yet, according to Gartner, 75% of all MDM programs across organizations fail to meet business objectives. Moreover, this trend has worsened since 2015, […]

AI Governance Best Practices

AI governance is meant to promote the responsible use of artificial intelligence for the betterment of humankind. Artificial intelligence has proven itself quite useful in completing a large variety of tasks quickly and efficiently. Unfortunately, it can also be used to support criminal behavior or to create and distribute misinformation. AI governance is an effort […]