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A step-by-step guide to setting up a data governance program

IBM Big Data Hub

In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture.

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8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

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The What & Why of Data Governance

erwin

Modern data governance is a strategic, ongoing and collaborative practice that enables organizations to discover and track their data, understand what it means within a business context, and maximize its security, quality and value. The What: Data Governance Defined. Data governance has no standard definition.

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The Modern Data Stack Explained: What The Future Holds

Alation

Chances are, you’ve heard of the term “modern data stack” before. In this article, I will explain the modern data stack in detail, list some benefits, and discuss what the future holds. What Is the Modern Data Stack? It is known to have benefits in handling data due to its robustness, speed, and scalability.

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SAP Datasphere Powers Business at the Speed of Data

Rocket-Powered Data Science

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Plus, AI can also help find key insights encoded in data.

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“You Complete Me,” said Data Lineage to DataOps Observability.

DataKitchen

What is data lineage? Data lineage traces data’s origin, history, and movement through various processing, storage, and analysis stages. It is used to understand the provenance of data and how it is transformed and to identify potential errors or issues. What about DataOps Observability? How does it compare?

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