RESEARCH & RESOURCES

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Ten Mistakes to Avoid in Building Out a Data Science Program: Download

 

By Fern Halper and Martin Pacino

Organizations are moving past traditional structured data and are embracing text data from emails, machine-generated data, and streaming data to enhance their analyses. Data science is an outgrowth of this of this evolution, but many companies are still in the early stages of their data science efforts. In this Ten Mistakes to Avoid, TDWI’s Fern Halper and Martin Pacino identify the mistakes most detrimental to building out a successful data science program.

File Type: .pdf File Size: 437 KB Duration: 15 pages


 

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Ten Mistakes to Avoid When Implementing a Data Quality Strategy: Download

 

By Patty Haines

Data quality is essential to getting more value from your organization’s data assets. Analysts, data scientists, and managers must know and understand the quality of the data they are using to make decisions and to set direction for their organizations if they are to make the best decisions.