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Don't drop ball on using data analytics tools -- 'big' or otherwise

Analytics software can provide big benefits to organizations -- or trip up the unwary. Before you get started, make sure your company is ready to reap the rewards and avoid the roadblocks.

As you might expect, with a site name like, we’re pretty keen about covering analytics technologies and issues. And over the past month, we’ve taken a broad look at different types of data analytics tools and how organizations can put them to effective use -- and avoid the pitfalls that often snare unwary organizations.

“Big data” analytics was one of the items on our agenda -- again, not a big surprise considering that it’s increasingly on the agenda of business intelligence (BI) and analytics teams in organizations looking to make sense of large pools of both structured and unstructured data. Consultant Lyndsay Wise kicked off our recent coverage with a pair of articles: an overview of issues to consider in planning big data analytics projects, and a checklist of five first steps toward big data analytics success -- choosing the technologies that will underpin your initiative is just the start, she says. Consultant Rick Sherman looked at the other side of the coin: He detailed a list of worst practices for big data analytics, from focusing too much on technology to overselling the potential benefits of projects within an organization.

In-memory analytics was also on our editorial to-do list for March. One story by contributor Alan R. Earls looked at the different varieties of in-memory analytics tools and their common denominator: the potential BI speed boost they provide to business users. There’s also a flip side there, though. In a follow-up story, analysts and consultants explained why the power and flexibility of in-memory analytics requires care on the part of end users and increased vigilance by IT and BI managers on use of the tools.

We delved as well into the potential benefits and challenges of predictive analytics software. News Editor Nicole Laskowski reported on a survey conducted by Ventana Research showing that a lack of analytical skills and problems with training and end-user support are the biggest obstacles to successful predictive analytics programs. One of the most salient survey findings was that the respondents most satisfied with their programs worked for organizations in which data scientists or other analytics specialists were taking the lead on the analytics process.

Our content pipeline will continue to spill forth more news, analysis and advice on using data analytics tools in the weeks ahead. Otherwise, we’d have a hard time saying we were living up to our name.

Twitter: @BizAnalytics_TT

Each month, editors choose recent articles and other content to highlight here for our readers. We welcome your feedback on these items and our site in general -- you can contact us directly or at

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