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


The Maturation of Hadoop -- Adoption and Experience

New survey results suggest that even when organizations have decided to commit to some use of Hadoop, their teams may still need additional training to come up to speed.

Balancing Static and Dynamic Data Models for NoSQL Data Systems

To get the most out of a NoSQL database, you must understand the best way to balance the advantages of static and dynamic data models.

Choosing Data Virtualization/Federation Tools

A pilot evaluation project can be an effective way to choose the best data virtualization and federation tools to integrate your EDW and Hadoop systems.

Define Your Business Case for Streaming Analytics

For a successful streaming analytics project, first you need to identify which business processes can benefit. Use this list of questions to frame your business case.

Career Advice for Analysts: Become an Agile Problem-Solver

To become a recognized expert, you cannot just rely on your technical knowledge. There are two parts to becoming an agile problem-solver: the "problem-solver" part and the "agile" part.

Career Advice for Analysts: Find the Passion

Do you know what skills make different analytics practitioners more valuable to an employer? Here's advice on reaching the next level so you can compete for the most coveted jobs.

Graph Databases for Analytics (Part 4 of 4): Addressing Performance Challenges

Once you put graph databases into practice, you may find performance does not scale at large volumes. We explain how you can improve the performance of your graph applications.

Graph Databases for Analytics (Part 3 of 4): The Basics of Graph Structure and Analytics

If you have an application for graph databases, what does the graph structure actually look like? We go further in-depth into the parts of a graph database and the purposes of common graph analytics.

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