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August 2015, Volume 3, Number 4

Finding a way off data isolation island

New technology is supposed to eliminate problems. With data isolation, new business tools are only making the longstanding issue worse. Companies are amassing on-premises tools and cloud applications. And those cloud apps come in many varieties: Some are in-house while others are from a provider and give the customer various levels of control.

Regardless of the types of apps companies are using, neglecting them and letting data isolation continue is not an option. For the cover story of this issue of Business Information, news writer Joel Shore talked with companies suffering from data isolation -- and he tells how they found a way out of the mess.

But if companies don't first address security concerns, jumping into integration might feel more like falling off a cliff. As Shore writes in another story, governance is an important part of cloud app integration because it establishes rules that keep company and customer data safe.

Also in this issue, news writer Dan Ring profiles Steve Berger, who helped clean up and streamline human resources processes at Compass Minerals. Berger says a human capital management (HCM) tool helped managers at the minerals producer better understand their role as supervisors and better lead their employees. "The HCM tool was a way to help our managers feel empowered to manage," Berger says.

Companies that can't afford an extensive IT department often look to outsourcing for financial relief. But as contributor and CIO Celso Mello writes, companies aren't going to save a lot of money with outsourcing if they just hand off work to the outsourcer. Instead, the provider and customer need to discuss what work should be prioritized.

If outsourcing is one misunderstood trend, then data science is certainly another. "Despite all the hype, data science jobs aren't in high demand," writes Ed Burns, site editor of SearchBusinessAnalytics. "The fact is there's more call these days for data engineers." In his column, Burns takes a look at the numbers of Google searches for data scientist and data engineer, contrasts them with the numbers of job postings for the same terms and posits why the disparity between hype and reality exists.

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