TDWI Upside - Where Data Means Business

Contributer: David Loshin


David Loshin, president of Knowledge Integrity, Inc, (www.knowledge-integrity.com), is a recognized thought leader and expert consultant in the areas of data quality, master data management, and business intelligence. David is a prolific author regarding business intelligence best practices, as the author of numerous books and papers on data management, including the just-published “Practitioner’s Guide to Data Quality Improvement.” His best-selling book, “Master Data Management,” has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at www.mdmbook.com.

  


All articles by David Loshin


Transition Point: Integrating Hadoop into the Enterprise

Although many are moving toward broader adoption of big data for enterprise use, barriers and concerns remain. Is Hadoop part of your enterprise’s plan for modernization?

Managing Customer Data for Analytics (Fourth in a Series)

Customer analytics can help you optimize business processes, but first you must assess how you are managing your customer data.

Planning for Customer Analytics (Part 3 of 4)

Analytics can help you improve customer experience and customer value for your enterprise, but it requires both data management and the careful choice of analytics techniques.

Optimizing the Stages of the Customer Life Cycle (Part 2 of 4)

Examining opportunities in each stage of the customer life cycle proves the potential benefits of a customer analytics program.

Analytics for Customer Engagement (Part 1 of 4)

New technologies make it easy to develop customer analytics, but a new platform doesn't create immediate improvement without work.

Taking Advantage of Predictive Models (Last in a Series)

Predictive analytics is most powerful when it’s incorporated into your business processes but that may be hard to enable. Here’s how to start incrementally.

Supervised vs. Unsupervised Learning (Part 3 in a Series)

Will a new website visitor be a good customer? We look at two approaches to creating an analytical model that can help you answer that question.

How Machine-Learning Techniques Use Methods (Part 2 in a Series)

These six machine-learning techniques are worth getting to know.

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