The challenge today for big data is that 85% of on-premises based Big Data projects fail to meet expectations and over 2/3 of Big Data potential is not being realized by organizations. Why is that you ask? Well, simply put on-premises “Big Data” programs are not that easy.
When I attended the Pacific Northwest BI & Analytics Summit this summer, we had a great discussion about data interpretation and the way organizations consume data.
In the past blogs, we have learned how to install Talend Open Studio, how to build a basic job loading data into Snowflake, and how to use a tMap component to build more complex jobs.
In part one of this blog posting series, we introduced that the analytics lifecycle is much more than authoring models. As brands develop and invest into creating models to solve critical business problems, so does the requirement to manage these assets as valuable competitive differentiators.
The information revolution - which holds the promise of a supercharged economy through the use of advanced analytics, data management technologies, the cloud, and knowledge - is affecting every industry.
In the last few years, we’ve seen the concept of the “Cloud Data Lake” has gained more traction in the enterprise. When done right, a data lake can provide the agility for Digital Transformation around customer experience by enabling access to historical and real-time data for analytics.
If your work environment is like ours here at SAS, you're seeing more of your data and applications move to the cloud.