Twitter & Big Data Management: Trend, Whim Or Necessity?

Twitter is making big changes in the way big data is managed — and that’s not unusual. Twitter has been looking for solutions in that field for about a decade now.

Last Friday night, it took another step to build its Big Data business, signing a deal that will allow it to move all data analysis in-house.

The impact on other social networks is less clear-cut, however.

Back in 2001, Gartner analyst Doug Laney defined Big Data as “high-volume, –velocity and –variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” 

Word Cloud "Big Data"

In simple terms, Big Data helps organizations manage, organise and connect massive amounts of structured and unstructured data through a technology that processes data calculation, usually in a reasonable amount of time.

Through Big Data analysis, companies essentially build the know-how to operate efficiently all the way through the decision-making process. As of today, though, it’s unclear whether the economics and the benefits associated to Big Data are sustainable or not.

 

Image source: STARTUPWEEKEND.ORG

Twitter, just like others, may — or may not — need to devote more resources internally to better understand and interpret the data.

My quick takeaway obviously goes down to the 3V Concept — Volume, Velocity & Variety.

  • Due to constant storage improvement and decreasing costs, the storage of a large data Volume is not a major issue anymore. However, a secure, cost & time-efficient technology is key. As new products are rolled out, it’s vital clients understand the technology and properly assess the economics associated to it.
  • Due to the Velocity of data changes and new inputs that could alter the data, clients must accordingly react via correlation models, for example.
  • Different data formats (Variety) and unstructured data needs to be managed and controlled, in order not to lose any complementary data which could impact the later data analysis.
  • Data quality control is a must at any step of the process.

 

If you want to discuss this topic further, feel free to email us at info@hedgingbeta.com

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