Many traditional businesses hate to fail. They embrace concepts such as Fail Fast in public, but deep inside their hearts, they are scared to fail. Failure is what gives corporate employees sleepless nights. In fact, many corporate initiatives fail, such as data science projects, for example. Why is failure so unloved, and what can we do about it? We have to change the way we think about failure.



Failure is not an event, it's a process.

This distinction is…

With the growing demand for scalability and flexibility, NoSQL databases are gaining huge momentum in the market. These databases are preferred as the first choice to develop complex web and mobile applications and operate with unstructured data. A ripple effect of which is that the NoSQL market is anticipated to hit $4.2 billion by the end of the year. 

This has made both new and experienced database…

Summary:  Finally there are tools that let us transcend ‘correlation is not causation’ and identify true causal factors and their relative strengths in our models.  This is what prescriptive analytics was meant to be.

Just when I thought we’d figured it all out, something comes along to make…

Summary:  Remember when we used to say data is the new oil.  Not anymore.  Now Training Data is the new oil.  Training data is proving to be the single greatest impediment to the wide adoption and creation of deep learning models.  We’ll discuss current best practice but more importantly new breakthroughs into fully automated image labeling that are proving to be superior even to hand labeling.

 …

In all business areas making decisions is a natural and integral part of any company’s management process. Doesn’t matter if it’s a small family business or a huge multi-national corporation. At some point or another they all need to make decisions to ensure their continued operation. And that’s exactly what a manager’s job in an organization comes down to – a constant process of decision-making to ensure continued growth and…

How does the typical data science project life-cycle look like?

This post looks at practical aspects of implementing data science projects. It also assumes a certain level of maturity in big data (more on big data maturity models in the next post) and data science management within the organization. Therefore the life cycle presented here differs, sometimes significantly from purist definitions of 'science' which emphasize the…