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”.
Making heads or tails from all the available data can be like drinking from a firehose. Simply overwhelming. Fortunately, there is a way to deal with this dilemma. As simple as it sounds – you need to narrow your focus on the things that truly matter. Doing so may only require as few as 4 data points. There are plenty of interesting examples to look at.
Hard to believe the holiday season is already upon us … Time once again to race towards a strong Q4 finish and prepare for a new year …
Here at Survature we run this race but are also uniquely positioned to watch others do the same. As a founder of a software startup, part of my job (and joy) entails talking to users and helping them to envision and manage their feedback data cycle. Through that process, many users have become friends of ours. A recent conversation really resonated with me. The reason – my friend leads innovation efforts at a multi-billion dollar company, his work makes a big impact on their global business, he is respected throughout the company, just architected a $100M partnership through a business model innovation, … yet he feels “innovation” has become a bad word.
Strategy is a big word. Every decision maker faces the task of developing and deploying strategic initiatives many times throughout the year. Whether it is the C-suite, VPs with profit and loss responsibilities, Directors in charge of initiatives, and Managers leading front-line operations, everyone is looking for better information that will help drive strategic decisions.
Much of the modern economy runs on fuel. Much of the future economy will run on data. In this analogy, data is the oil and analytics is the gasoline. Given this data economy, how do you build a strong data analytics pipeline of your own? Should you build your own refinery or should you buy gasoline direct? Simply put, does it make sense to outsource or not?
During the recent StartUp Week, there were many events taking place nationwide. Data analytics was a popular topic across the board. At one of these events I spoke on a data analytics panel attended by aspiring entrepreneurs (who are pursuing ideas related to data analytics), practitioners, software developers, users, and bosses (who are paying for data analytics), … A multitude of “what-if” questions flew in the air. It was an exciting event for all.
Knowledge workers from C-suites to entry-levels alike are adopting new web services to make previously difficult tasks easier to manage. However, these new solutions can result in headaches for IT departments, because many 3rd party software platforms exist in the “cloud”, which is open to business and compliance concerns due to privacy and security issues.
Congrats to Survature's own Lynn Youngs for reaching the summit of Mount Kilimanjaro! The dormant volcano rises around 16,000 ft from its base on the plains of Tanzania and is the tallest mountain in Africa. The ascent from the base took a little over a week for Lynn to complete. Upon his return Lynn said, “It is certainly one of the most physically demanding things I have ever done.” The team here is proud of Lynn's accomplishment and all he does to help Survature reach the top!
Businesses of all shapes and sizes are increasing their use of data to reveal insights and drive decisions. No matter the source of the data – IoT sensors, predictive computer models, electronic transactions, human feedback, or other; there is a rapidly expanding need for a faster, more extensive, and more thorough view of what is happening, the causes, what to do about it, and why.
In early 2015, the journal editors of “Basic and Applied Social Psychology” announced they would no longer publish papers that contain P values. Later, Nature published an article to further discuss the controversy revolving around P value. It doesn’t matter which side you are on, it is obvious that we are all obsessed with P values, because they have served as a rule of thumb for flagging noteworthy findings across the sciences. As the recent controversy shows, P values are open to exploitation, which is sometimes known as “P hacking”.