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From the blog
Software involves working with people and people tend to respond predictably to problems. In the United States, we often find our motivation to achieve fueled by a culture that rewards success, while commonly denying the realities of failure. As a culture, we feel terrified to fail, despite the reality that true long-term success is often […]
The reality is that most of us don’t work at Google, but instead with your average little league aka small or medium sized company. Most of us don’t have our pick of staff, we don’t work necessarily with the best and brightest. And to be honest, you and I may not fall into the category of the best and brightest ourselves. However, this reality does not mean that we won’t be successful. There is still much we can do to develop effective procedures and processes, and develop our talent to maximize the skills and the resources that we have at hand. What we can learn from Google may indeed be of value to our teams, especially if we are able to avoid the same mistakes and pitfalls that Google has learned to avoid.
Big data is a hot topic because it introduces us to both incredible possibility and potentially terrible consequences. Big data essentially means that engineers can harness and analyze traditionally unwieldy quantities of data and then create models that predict the future. This is significant for a variety of reasons, but primarily because accurate prediction of the future is worth a lot of money and it has the potential to have an effect on the lives of everyday citizens. It also raises questions, such as can algorithms be held to the same moral standards as their human developers or should the developers be held responsible for the outcomes? If the answer to either or both of these questions is “yes,” then how can this be achieved both effectively and ethically? When ethically questionable patterns are identified by an algorithm, we need to establish an appropriate response.