The evolution of apps: from reactive to pre-emptive
Last week I wrote about how the next wave of enterprise apps will stand out in two ways: they’ll be smart and make experiences as effortless as possible for the end user. As machine learning and predictive modelling become more of a mainstream reality, apps will shift from being reactive “sense and respond” to more […]
What’s your data strategy? Defining the data hierarchy
Over the past decade, startups and enterprises have devoted hefty resources to collecting and analyzing huge volumes of big data. For some, data is used to fine-tune a product; in other cases, data forms the foundation of the product itself. When it comes to building a startup around data, the more unique that data, the […]
Data, not algorithms, is key to machine learning success
There has been an explosion in machine learning activity, and Shivon Zilis recently mapped out the current machine intelligence ecosystem as we enter 2016. This is one of the key areas that we’ll be following this year. While the opportunities here are tremendous, the exuberance surrounding machine learning distracts startups from a key hurdle: it’s […]
The art of hiring a data scientist
Nearly four years ago, I joined the Insight Data Science team and we launched an intensive 7 week post-doctoral training fellowship bridging the gap between academia and data science. Since then, over 400 Insight alumni have been hired as data scientists or data engineers at top tier companies like Facebook, LinkedIn, Twitter, Airbnb, and Google. Although I formally […]
Metrics that matter to social platforms (Part 3/3): measuring engagement by relationships
Over the past two weeks, we have introduced our social platform KPI dashboard to track the key metrics for social platform startups. We first covered the high-level metrics (such as DAU, MAU and stickiness) and then took a deeper dive into content engagement. When we talked about engagement, we related it to a specific action […]