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”.
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.