News
News
- January 18, 2012
18 Jan'12
Sustainable practices for updating SMB business intelligence processes
In small and midsize businesses as well as larger organizations, BI systems need to be flexible and adaptable in order to able to meet changing business needs, analysts say.
- January 16, 2012
16 Jan'12
Telecoms lead pack in use of BI tools, analytics
While telecommunications may be maturing in its use of analytics and BI tools, new research finds that more than half of its analytical process time is applied to data-related tasks.
- January 12, 2012
12 Jan'12
Q&A: Effective predictive modeling techniques more than a math problem
In an interview, analytics and data mining consultant John Elder offers advice on managing successful predictive modeling initiatives and a head’s up on common mistakes to avoid.
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- January 11, 2012
11 Jan'12
Coping with midmarket BI project management challenges: Tips for SMBs
Many small and midsize businesses don’t have a lot of money or staff resources to devote to business intelligence programs -- making sound project management processes essential, BI analysts say.
- January 10, 2012
10 Jan'12
Building predictive models requires business engagement, mix of skills
To generate useful predictive analytics results, predictive modeling teams need a combination of analytical and business know-how plus close ties to business users, say analytics pros and consultants.
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- January 09, 2012
09 Jan'12
'Big data' to inspire streamlined analytics programs in '12, IIA predicts
Faculty members from the International Institute for Analytics, a research firm that focuses on how organizations use analytics, weigh in on their predictions for 2012.
- January 06, 2012
06 Jan'12
Midmarket business intelligence problems and pitfalls to avoid
There are lots of ways that BI deployments in small and midsize organizations can go wrong, according to analysts who offer advice on how to sidestep the roadblocks to a successful deployment.
- January 04, 2012
04 Jan'12
Checklist items: Business intelligence best practices for SMBs
Midmarket companies investing in BI systems should follow the same basic steps as larger organizations, analysts say. But, they caution, there's typically less room for error in smaller businesses.
- January 01, 2012
01 Jan'12
Skills shortage, training present pitfalls for 'big data' analytics
A shortage of skilled data scientists and a user-unfriendly set of technologies are the biggest obstacles impeding big data analytics programs, say analysts and IT professionals.
- December 30, 2011
30 Dec'11
Best practices for 'big data' analytics rely on familiar disciplines
The terrain may seem foreign, but many proven data management and business intelligence best practices translate well to big data analytics programs, according to analysts.
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- December 23, 2011
23 Dec'11
Business intelligence in the cloud: Is SaaS BI in your future?
The growth of cloud computing as an application platform means business intelligence teams should at least be prepared for the possibility of Software as a Service BI deployments, analysts say.
- December 22, 2011
22 Dec'11
From the Editors: Looking back, looking ahead on advanced analytics
Big-data analytics and other forms of advanced analytics will be a big battleground for BI vendors in 2012. Our recent coverage highlights that, and ways users can up their analytics game.
- December 22, 2011
22 Dec'11
Year in review: 'Big data' gets big chunk of news in 2011
The term ‘big data’ took on bigger meaning in 2011 with new product releases from major vendors as well as new terminology to describe the big data analytics landscape.
- December 21, 2011
21 Dec'11
Copper keeper: Advanced data visualization helps curtail copper thefts
A Virginia-based energy company is relying on advanced data visualization, geospatial data and visual analytics to stay a step ahead of thieves who’ve taken a shine to copper wire.
- December 16, 2011
16 Dec'11
Q&A: Lack of trust in predictive analytics models can snarl projects
In an interview, predictive analytics consultant Eric Siegel offers advice on building organizational trust in the findings of predictive models -- and warns how the process can go wrong.