ai architecture artificial-intelligence blog blogging Book Review business career Compliance Content Corporate Life Customer Support cybersecurity data data-science DevOps education entropy fitness garmin leadership Licensing life marketing microservices Monitoring music Observability Operations Partnership philosophy Product Management Products saas Sales Security software-development technology User Experience wordpress writing
-
Tools and the Analyst
Let’s say I’m responsible for a complex system. I might have a lot of titles, but for a big part of my job I’m an analyst of that system. I need tools to help me see into it and change its behavior. As an analyst with a tool, I have some generic use cases the
-
Dev and Test with Customer Data Samples
The short answer is don’t do it. Accepting customer data samples will only lead to sorrow. REDUCE THE DATA At first, you may look at a big data problem as a Volume or Velocity issue, but those are scaling issues that are easily dealt with later. Variety is the hardest part of the equation, so
-
From Enterprise to Cloud, Badly
Sometimes enterprise companies try to go cloudy for bad reasons with bad outcomes. I’m going to talk about those instead of well-planned initiatives with good outcomes because it’s more fun. So you’ve got an enterprise product and you’re facing pressure to go to the Cloud… after all, that’s regular recurrent revenue instead of up-front tranches,
-
It’s not a platform without partners
What are the major decisions that a platform needs to make in order to balance incentivizing development vs. maintaining quality and control over their 3rd party app marketplace? Let’s look at this on three scales, in which the right answer for a given team is somewhere between two unrealistic and absolutist extremes. First decision scale:
-
GDPR is great for Facebook and Google
GDPR is going to be great for Facebook and Google. “Over time, all data approaches deleted, or public.” — Norton’s Law. See Haunted By Data by Maciej Cegłowski and The State of Artificial Intelligence by Andrew Y Ng for more background and viewpoints. Picture two types of data store, public and private. If your store
Monkeynoodle.Org 