By Fern Halper, VP Research, Advanced Analytics
There was a time when choosing a programming language for data analysis had essentially no choice at all. The tools were few and they were usually developed and maintained by individual corporations that, though they ensured a reliable level of quality, could sometimes be quite difficult to work with and slow to fix bugs or innovate with new features. The landscape has changed, though.
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Posted on July 26, 20170 comments
By Meighan Berberich, President, TDWI
Analytics and data science have moved to the forefront of business decision making. The size and scope of the organizations and the complexity of tools and technologies that support these mission critical initiatives only continues to grow. It is critical for analytic leaders to maintain focus on the key factors that drive success of their analytics teams and deployments.
TDWI Accelerate will not only provide analytics leaders with insight on what’s new (and what’s next) in advanced analytics, but also on the factors beyond technology that are instrumental to driving business value with data.
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Posted on July 21, 20170 comments
By Meighan Berberich, President, TDWI
Data prep. Wonderful, terrible data prep. According to John Akred of Silicon Valley Data Science, “it’s a law of nature that 80% of data science” is data prep. Although our surveys average closer to 60%, even that’s an awful lot of time to spend not analyzing data, interpreting results, and delivering business value—the real purpose of data science.
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Posted on July 19, 20170 comments
By Meighan Berberich, President, TDWI
Communication—the process by which information is exchanged between individuals. In the analytics field, we like to call it “data visualization,” but it’s really just a particular form of communication. There’s nothing special about that. Even bacteria can communicate with each other. So why can it be so difficult for data professionals to get their meaning across?
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Posted on July 17, 20170 comments
Are they still relevant?
By Chris Adamson, Founder and BI Specialist, Oakton Software LLC
Technological advances have enabled a breathtaking expansion in the breadth of our BI and analytic solutions. On the surface, many of these technologies appear to threaten the relevance of models in general, and of the dimensional model in particular. But a deeper look reveals that the value of the dimensional model rises with the adoption of big data technologies.
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Posted on July 11, 20170 comments
A hub should centralize governance, standards, and other data controls, plus provide self-service data access and data prep for a wide range of user types.
By Philip Russom, Senior Research Director for Data Management, TDWI
I recently spoke in a webinar run by Informatica Corporation, sharing the stage with Informatica’s Scott Hedrick and Ron van Bruchem, a business architect at Rabobank. We three had an interactive conversation where we discussed the technology and business requirements of data hubs, as faced today by data management professionals and the organizations they serve. There’s a lot to say about data hubs, but we focused on the roles played by centralization and self-service, because these are two of the most pressing requirements. Please allow me to summarize my portion of the webinar.
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Posted on July 12, 20160 comments