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How to Ensure an Effective Data Pipeline Process

In this contributed article, Rajkumar Sen, Founder and CTO at Arcion, discusses how the business data in a modern enterprise is spread across various platforms and formats. Data could belong to an operational database, cloud warehouses, data lakes and lakehouses, or even external public sources. Data pipelines connecting this variety of sources need to establish some best practices so that the data consumers get high-quality data delivered to where the data apps are being built.

2023 Trends in Data Governance 

In this contributed article, editorial consultant Jelani Harper offers his perspectives around 2023 trends for data governance. The valuation of data governance, both to the enterprise and to data management as a whole, is evinced in two of the most discernable trends to shape this discipline in 2023.

How AI Enables Organizations to Move from Network Monitoring to Proactive Observability

In this special guest feature, Stephen Amstutz, Head of Strategy and Innovation, Xalient, discusses the role of AI in the shift from network monitoring to observability, highlighting the benefits of AI observability in limiting downtime, protecting brand reputation, and ultimately saving money!

What to Avoid When Solving Multilabel Classification Problems

In this contributed article, April Miller, a senior IT and cybersecurity writer for ReHack Magazine, suggests that If you are working with a model with a multilabel classification problem, there is a likely chance you will run into something in need of fixing. Here are a few common issues you may encounter and what to avoid when solving them.

The Key Role Missing in Most Data Science Teams

In this contributed article, Wendy Lynch, Founder of Analytic-Translator.com, shares her experience of working with small to large global clients on how to break down the communication barriers in an organization to deliver results. This often happens between the analyst teams and the business teams.

Stop Building Models, Start Training Data

In this special guest feature, Sanjay Pichaiah, VP of Product Growth at Akridata, highlights why it is time for data scientists to stop building models and start training data. The path to better models and greater model accuracy doesn’t lie exclusively with the model, even though that has been the greatest focus in recent years. To truly accelerate and increase model performance, we need to be focusing more on the training data sets we are supplying the models and stop hoping the data is good enough.

2023 Trends in Artificial Intelligence and Machine Learning: Generative AI Unfolds  

In this contributed article, editorial consultant Jelani Harper offers his perspectives around 2023 trends for the boundless potential of generative Artificial Intelligence—the variety of predominantly advanced machine learning that analyzes content to produce strikingly similar new content.

Six Key Components to Enhance Your MDM Program

In this special guest feature, Robert Eichelman, Solutions Architect at Experis, shows that whether you are starting to build your Master Data Management (MDM) approach or are continuing to evolve your current program, there are key MDM fundamentals to keep in mind. This article outlines six steps to help businesses evaluate and sustain an extendable program to help ensure future success.

With Sustainability Analytics, Big Data Can Save the World

In this contributed article, Cashion “Cash” East, Director of Analytics for Higg, discusses how sustainability analytics requires a lot of data from many different sources. Some of that data already exists and is standardized, while other data is coming from new sources with no established standards. In order for companies to collect and organize ESG data in a meaningful and efficient way, the article recommends that companies start doing five things as they begin building out their sustainability insights platform.

2023 Trends in Data Modeling 

In this contributed article, editorial consultant Jelani Harper discusses how the centrality of data modeling to data management will likely remain for some time. What’s most notable about this fact is it’s becoming considerably simpler to perform some of the basic tasks associated with this discipline. From flexible, schema-on-read models to the multitude of options for clarifying semantics and implementing the basics of domain models, data modeling’s effectiveness is increasing almost as much as the effort it requires is decreasing.