Data science is all around us these days. It’s the new big thing and considered to be the solution to almost any challenge that companies face. Being a data scientist is supposed to be the sexiest job of the 21st century and the faith in the power of analytics seems to be the mantra of […]
The pressure on big data projects to deliver is huge. On the one hand, there is a high demand for data analytics tools for better decision making. On the other hand, projects often struggle with technical issues when implementing and operating the large variety of software and hardware in the big data stack. As a result the time to value is often too slow.
It may be surprising to some readers that the first few articles of our “Big Data, Broken Promise?” series have not covered many Technology topics at all. Is it really that easy to set up a suitable IT infrastructure and all the required tools for Data Scientists?
Some consider data enrichment as cheating, some as magic. Actually, it is neither. Data enrichment is a term that comprises a bunch of methods that range from engineering to science, plus a pinch of experience. In this blogpost, I would like to shed some light on different methods of data enrichment, their concepts and requirements. No worries, I will not dive into math.
In the latest blog post of our “Big Data, Broken Promise?” series, we have shared our number 1 observation of why numerous Big Data projects fail: They start without a clear understanding of the use case(s) that should be addressed and without sufficient interaction with the final end user or target group.
As announced in our last week’s blog post, in this series we are aiming to share our learnings and experiences on how to successfully approach big data projects and continuously transform into a data-driven company.