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Download this issue and learn which data-related technologies are attracting more interest in the age of big data; learn about the intersection of technologies that allow us to work with data in the cloud; find out whether big data spells the end of traditional data warehouse architecture; and more.
Average salaries rose 3.5 percent in 2014 for full-time U.S. BI professionals. Read more in the 2015 TDWI Salary, Roles, and Responsibilities Report.
By Krish Krishnan
In this Ten Mistakes to Avoid we identify the mistakes with the most negative impact on Hadoop implementations and recommend solutions you can apply to your own environment.
Download this issue and learn why revenue management is driving new big data and BI initiatives; how to formulate your company’s big data strategy; whether we still need data modeling in the age of big data; how a BI staff can become more business oriented; and more.
By David Stodder
This Ten Mistakes to Avoid focuses on helping organizations
ensure satisfaction as they democratize BI and analytics. It
recommends a balanced approach for meeting user needs while
addressing the necessities of data governance and management.
Find out why MIS graduates are the ideal choice for BI positions; learn how to efficiently use resources such as hard disks, RAM, and CPU to enable strong analytics performance; read our experts' recommendations for big data projects; and more.
TDWI’s annual BI Benchmark Report enables business intelligence (BI) teams to compare themselves to their peers on a series of organizational and performance metrics. The 2014 BI Benchmark Report is based on a Web survey of 357 BI professionals conducted worldwide in spring and early summer 2014.
By Mark Giesbrecht
Presented in this issue are the 10 mistakes to avoid as you take your agile BI practice to the next level.
Put the customer first. The customer is king. Keep the customer satisfied. Those
familiar adages ring especially true in this issue of the Business Intelligence Journal.
By Fern Halper
This Ten Mistakes to Avoid covers what works and doesn’t work when it comes to big data analytics projects.
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