The World Cup has come and gone. There’s no doubt that throughout the last several weeks companies all over the world have been capturing as much data as they can to better understand soccer fans. Knowing the real reasons behind people’s decisions can serve many business needs. Solid data about what fans truly think is a starting point that fundamentally affects the clarity, precision, and confidence of your strategic initiatives. We’ve helped many organizations get solid data from tens of thousands of people. We’ve learned a few things we want to share from this experience.
How to present data analytics effectively has been a popular topic during Survature user workshops. Typically the question goes something like, “We’ve got so many findings here, any suggestion on how to present them?” While users and their specific cases for presenting results may vary, there are some things everyone should watch out for and consider when presenting your survey findings. Here are four things we have found that can make or break your presentation.
Ask the leadership team of any organization and they will tell you that making difficult decisions is part of the job. One word they often love and hate is “strategy”. My co-founder, Jian, wrote a blog about how the paralysis of analysis can kill strategy and how to avoid that. Jian referenced an oft quoted phrase that resonated with readers and myself personally. That was: “culture eats strategy for lunch”.
So, there is a lot of data at our disposal these days. Lots of it. And along with all this data often comes the feelings of “I am not getting what I need from the data” or “it’s just a lot of wasted time and effort”.
Good feedback, a willingness to understand, a desire to improve … These altogether lead to meaningful actions and positive changes.
Data jobs are “the sexiest jobs this century”. Data jobs are also among the hardest to positions to fill. The qualifications are so high that candidates have the leverage to “name their own price.”
Deep learning was prominent in the venture capital world of 2016 and rightfully so. This wave of excitement about AI and computing grew strong, because of a new-found comfort on letting unprecedented rich data guide progress. Interestingly, the term “deep learning” draws another contrast, that is, previous generations of machine learning lacked support of real data — in other words they were “shallow”.