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Tweet jam recap: Hire a data scientist or train a data analyst?

In this big data tweet jam recap, learn whether participants prefer to hire an external data scientist or train a data analyst from within.

Are you enlisting the help of an outsourced data scientist to tackle big data at your organization? Are you training in-house employees to put on their data analyst hat? In SearchCIO's big-data-themed tweet jam on March 27, we asked participants to weigh in:

According to tweet jam participants, both outsourcing and insourcing are valuable when it comes to big data analytics on an enterprise scale:

Outsourcing data analysis can spearhead big data analytics efforts while increasing available resources and helping IT teams gain new perspectives on the value of their data. On the flip side, in-house talent development might be less-expensive -- in theory, who knows your data better than your current employees? Our tweet jammers sounded off on the pros and cons of developing data analysis skills internally:

Time restrictions, hiring resources and launch costs are factors in a CIO's decision for one data analysis solution over another, but there are other considerations. For example, tweet jam participants advise organizations to take into account company size, industry and company-needs when researching a data analysis solution:

Our tweet jam participants all agree on one thing: There's no simple answer. Whether your organization chooses to nurture in-house data analyst talent or outsource for data scientist skills depends on the advantages that are important to your individual situation.

Read what tweet jam participants had to say about the big data analytics dilemma by searching the #CIOChat hashtag on Twitter. Follow the rest of our big data conversation at: Is analysis of data worth your IT staff's time? ... Data interpretation and visualization techniques add business value

Follow @searchCIO on Twitter to be notified about upcoming Twitter discussions.

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