Right Now
I help boards and executives at growth-stage technology companies continue (or resume) rapid acceleration by advising them on improving their leadership capabilities, operations, and technology. While I specialize in executive advising on leadership and process, I can also dive into deep technical problems with Data Science or Software Engineering departments. Feel free to contact me directly to learn more.
I have a few tech projects in progress that will undoubtedly become blog posts. I’m working on an e-book about leadership in organizations, as well as a field manual for triaging performance and architecture in growth-stage startups. I sometimes write code for open-source projects like TinySite and CompressTest. I also work on ApplyByAPI, a tool that helps companies focus on quality over quantity in their tech hiring process.
Recent Publications
Stop Collecting So Much Data
In many discussions with people in the industry, the topic of how much data is collected or processed often arises. Although it’s useful for getting a feeling for the scale of data challenges at a company, as a metric for the skills or effectiveness of a data organization it’s a pretty useless topic. However, even when the topic is raised in the context of getting a feel for data challenges at a company, that only makes sense if all the data are necessary for solving problems. However, that is usually not the case and I propose that blindly collecting all possible data is an anti-pattern and bad for data programs generally. Assuming it’s not necessary for a valid reason, like reproducibility of results, collecting all possible data can indicate that the underlying problem has not been properly identified, or that proper data governance is not in place to guide the process of collecting data. ... read moreOn Data Governance
Overview The short version, is that Data Governance is all of the business concerns surrounding data. This means things like data quality, management, risks, and similar non-technical things. It’s comprised of the kind of concerns that business people would typically have surrounding data, although the quality topic is critically important for machine learning practitioners. For our purposes, I’ll group the concerns generally into quality, management, and risks. Each of those things can be pursued very deeply, but we need only an overview in order to make some key points. ... read moreOn Salary Negotiations
Salary negotiations are often presented from the perspective of a potential employee, but I rarely read anything regarding strategies for companies who want to do their best to hire a particular candidate. As someone not formally trained in HR, my opinions on the topic have been developed strictly through my experiences. If you’re an HR expert and think I’m crazy then I ask you contact me and help me improve my perspective. ... read moreUsing Twitter as a Stream Processing Source
Recently, I was providing a lecture on stream processing and planned to use the Twitter streaming API as an example. To provide a framework for the attendees, I created the one below. It has been tested with Python 3.4 but as it does not include testing or other things it should only be used as an example. from threading import Thread from collections import deque from twython import TwythonStreamer from requests.exceptions import ChunkedEncodingError For the imports, we need them for the following reasons: ... read more