Unlocking the power of big data might have once seemed an enterprise-only activity, requiring resources SMBs could only dream of. In many cases, these challenges have overshadowed the potential opportunities for
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We've searched our sister sites for pieces that highlight the pros and cons of a data-driven strategy, uncovering tips and advice to assist SMB organizations in understanding big data trends and using analytics. Here are some strategies, resources and tools to get your own conversation started -- and maybe even inspire you to bet big on big data.
Big data is becoming increasingly prevalent, following in the footsteps of the emerging social media, mobility and cloud computing movements. Though analyzing big data can mean big savings and revenue increases, there are some hurdles that SMBs must first clear. Use our big data analytics guide to take stock of these big data concerns and learn how using data can give SMBs a competitive edge.
SMBs are seeing that cloud elasticity provides an environment in which big data scientists can crunch large volumes of data. But, in some cases, the cloud isn't quite up to par for big data processing. CIOs looking to store and analyze big data in the cloud may face architectural obstacles with respect to capacity, operation and agility. In this feature, cloud providers weigh in on the core issues concerning cloud and big data analytics and the strategies they're formulating to boost cloud's storage performance.
Business intelligence systems were primarily designed for large enterprises with tons of data, but companies in the SMB world have since realized that BI systems are an answer to their own data questions. In this piece, which looks at enterprise versus SMB business intelligence systems, we see where the systems are the same and where the differences are distinct. Find out why it's sometimes simpler for SMBs to implement a BI system and take advantage of the many functions it offers.
"Big data is not about the data, it's about the analytics" -- wise words from Harvard professor Gary King. He asserts that humans are prone to errors and thus make bad computers, so we should leave much of the analytics work to the systems designed for it. But, he cautions, the real business value in big data analytics lies in the "last mile" that often requires a human touch. In our CIO Matters column, we follow what's working and what's not when it comes to big data analytics.
Using the Boston Marathon bombings as a stark reminder, this piece focuses on how intelligence agencies were unable to connect the big data and security dots, both before and after the attack. Given the public-safety stakes, we clearly see why there is a rush to understand big data analytics. Through a series of case studies, the author offers real-world data science success stories -- and failures.
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This was first published in September 2013