Early in December 2016, I spoke at the Strata+Hadoop World 2016 Singapore conference on sensor data analysis approaches, specifically, time series analysis. My company, The Data Team, were represented at Strata+Hadoop World at the innovator’s pavilion. It was a wonderful learning experience for me at the conference, and I have the following key take-aways: There […]Read more "Video: Talk from Strata+Hadoop World 2016 Singapore"
The Year 2016 in my mind will be associated with three key things, with respect to data: A career transition in engineering, product development and quality management to a career in data science, big data analytics and strategy consulting Learning how to learn better – learning to update my own skills by constant study, reinforcement […]Read more "The Year 2016 in Data"
Strata + Hadoop World 2016 is happening next week, between December 5th and 8th in Singapore. I’m excited to be presenting at the conference on the subject of time series analysis for sensor data. More about my talk here. One of my key focus areas during the last several months at The Data Team, where […]Read more "Onward to Strata+Hadoop World 2016, Singapore!"
One of the changes envisioned in the big data space is that there is the need to receive data that isn’t so much big in volume, as big in relevance. Perhaps this is a crucial distinction to make. Here, we examine business manifestations of relevant data, as opposed to just large volumes of data. What Managers Want From […]Read more "Big Data: Size and Velocity"
Introduction Effective measurement is as important in the data science revolution as effective analysis is. Without data that is measured correctly, we fly blind into data analysis, and such a scenario can hardly be effective at extracting insight from the data we possess. In this post, I discuss some challenges facing effective measurement in the […]Read more "Challenges of Effective Measurement"
As a data science consultant that routinely deals with large companies and their data analysis, data science and machine learning challenges, I have come to understand one key element of the data scientist’s skill set that isn’t oft-discussed in data science circles online. In this post I hope to elucidate on the importance of domain […]Read more "Domain: The Missing Element in Data Science"
Data products are one inevitable result and culmination of the information age. With enough information to process, and with enough data to build massively validated mathematical models like never before, the natural urge is to take a shot at solving some of the world’s problems that depend on data. Data Product Maturity There are some […]Read more "Insights about Data Products"