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 SearchCIO.com 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.

This was last published in April 2013

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Should CIOs nurture in-house talent for big data analytics, or outsource for skills?
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Analytics will continue to be fueled by the people who know the business well. sourcing internally for the next crop of data scientists will be from finance, marketing, operations, in addition to IT. It will be one thing to stand up the infrastructure, but an entirely other skillset to determine what to do with the data.
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Training and mentoring in-house talent requires a competent, experienced expert mentor/trainer/leader (which means, for most firms, hiring someone from the outside).
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An effective data science effort depends on business acumen and understanding of data management ecosystem in an organization. Those features makes internal resources more effective. Outside should be used as complimentary and as jump starter (if internal resources need it).
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