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Best of TDWI's Data Digest

Interesting articles we found on the Web focus on tips for using data for business, quantifying big data's ROI, using storyboards for big data stories, and understanding BI scalability.

Nate Silver: Tips for Using Data for Business
(Source: Tech Target)

Nate Silver knows a thing or two about data analysis. The man made famous for accurate voting predictions in the last elections has some tips for those in data-driven business fields based on his experiences running FiveThirtyEight.

Quantifying Big Data's ROI
(Source: Datanami)

Big data is important for business, but how important is it, and and great is ROI of big data? A recent study by Teradata tackled these questions, and this article highlights the report's main findings.

Making Big Data Stories Understandable with Storyboards
(Source: Tech Republic)

Although visualizations done poorly can be misleading, crafting an accurate and captivating story from data is still an important job. Using a storyboard process to visualize the data can make the insights easy to understand and make the follow-through clear, as this article explains.

Understanding Business Intelligence Scalability
(Source: Solutions Review)

Scalability is a term used with regards to system storage; a better scaling system is one that can support a larger load without crashing. However, scalability can also be applied to business intelligence and analytics in productive ways, as this article explains.

Big Data Complicates "Single Version of the Truth"
(Source: Datanami)

Data warehousing and analytics are supposed to answer any sort of question thrown at it in the exact same way. However, with more people privy to the same set of data, the more answers people are coming up differ despite asking the same question. As this article reveals, a larger pool of people analyzing data leads to a larger pool of truths.

Analytics Speed More Important than Data Size
(Source: Tech Republic)

Just because big data is increasingly popular doesn't mean that having lots of data is the most important part of analytics. Everybody has access to huge amounts of data. It's the speed of data analytics -- and stream-based processing -- that's more important.

Quint Turner is an editorial intern at TDWI and an undergraduate English student at Skidmore College. Follow his blog at

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