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TDWI Articles

Contributor: Troy Hiltbrand


Troy Hiltbrand is the Chief Digital Officer at Kyäni where he is responsible for digital strategy and transformation. You can reach the author at [email protected].

 

  


All articles by Troy Hiltbrand


Can Data and Analytics Help Save the World?

As a member of the data and analytics community, do you have what it takes to help save the world? Following the United Nations’ call for support, many companies and organizations are leveraging advances in data and analytics to solve some of the world’s greatest challenges.

Three Models Leading the Neural Network Revolution

In recent years, we have seen great advances in machine learning and artificial intelligence that could usher in a new era of progress. In the area of natural language processing, three algorithms have been the cornerstone of this innovation: GPT, BERT, and T5.

Distributed Advanced Analytics Through Citizen Data Scientists

For analytics teams to be successful, they need to learn how to scale their human resources using citizen data scientists. This will require new thinking and new practices.

Understanding the EU Cyber Resiliency Act

As information becomes pervasive throughout our lives and societies, so does the threat of cybercrime. To combat this, the EU has proposed a law to increase cyber resiliency that, if adopted, will be felt worldwide.

Three Ingredients of Innovative Data Governance

Data governance doesn’t have to be the enemy of innovation. The two concepts can exist in harmony but require some important features to be in place to succeed.

3 Ways NoSQL Can Be Part of Your Data Architecture

As big data becomes more commonplace, it is important for data management teams to be able to understand where NoSQL fits into their architectures.

3 Ways You Can Optimize Your Sales Team with Analytics

Analytics organizations that make the effort to support their sales teams can elevate their status within an organization.

5 Things to Consider When Operationalizing Your Machine Learning

Operationalizing machine learning models requires a different process than creating those models. To be successful at this transition, you need to consider five critical areas.

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