Modern ethos is that all data is valuable, should be stored forever, and that machine learning will one day magically find the value of it. You’ve probably seen that EMC picture about how there will be 44 zettabytes of data by 2020? Remember how everyone had Fitbits and Jawbone Ups for about a minute? Now Jawbone is out of business. Have you considered this “all data is valuable” fad might be the corporate equivalent? Maybe we shouldn’t take a data storage company’s word on it that we should store all data and never delete anything. Infoworld, December 7, 2017.
I never deleted any of my email. I archived it all away where I could then quickly search it. I found that things happened on projects in regular patterns. If something went wrong in integrating some widget I could pretty much always find when it had happened before. The associated discussion and sequence of actions that had ensued projects ago was then almost always useful at quickly resolving our current problem.
For more see Be An In-box Hero
I knew a team manager who would after every completed project deleted all the past project data and started over new. He had no hard evidence for how his team performed in the past, except for his memory, and he just generally made up any estimates that were needed. I had luckily worked with his team on previous projects and so had copies of their project data (did I say I never deleted anything?) and so I always knew better than he did how his team would perform. In one case I told a similar manager that his estimate was too low based upon the data I had on his team. He looked at me incredulously. He went back and examined his past data and doubled his estimate. His team was the first to ever deliver that phase of a project on time and it took no longer than any other previous effort but without a slipped schedule and with rock solid quality at initial delivery.
Reading in this article the notion that data should be kept forever resonated with me. I can say that once I’ve analyzed a project’s data and synthesized it down to what I needed to know that I rarely had to look at the raw data again. But as we all now know, new questions can arise and that old data can often be the definitive source on answering those questions. Just don’t necessarily rely on a machine learning algorithm to find those answers.
Finally see First Have An Idea Then Mine Your Data
Are you keeping your project data for a sufficient period of time that it helps you to successfully plan and manage your current projects?