Why Data Scientists should consider switching from Python to Spark

Switching to Spark, Python runtime comparison. Python has been the language of choice for Data Scientists for last several years. It is easy to use, versatile, and tremendously rich in libraries – such as Pandas, Numpy, Sklearn, Keras, … not only are there many libraries, but the libraries are also mature and comprehensive.

Read More

The use of personalization in direct mail campaigns

Marketers’ campaigns are evenly split between personalized, segmented, and mass mailings, and when personalization is used, it’s moved well beyond the simple name-only personalization that used to dominate the market.

Read More

Why could the promise of Big Data and Analytics fail?

There is talk of big data and analytics all around and the organizations of all hues are excited about the promises that they hold. Analytics is expected to make organizations more competitive, more cost-efficient and more customer oriented. The most obvious sign of this excitement is the huge resource allocation to anything related to analytics …

Continue reading “Why could the promise of Big Data and Analytics fail?”

Read More

Why experimentation could guide you to failure?

In the last entry we have talked about potential pitfalls in the use of analytics and how some of those pitfalls could lead organizations to abandon the path of analytics.

Read More