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When I turned 54 I already had 35 years on the job. Plus the five I bought with my 401(k) money, that gave me 40 years. At two and a half percent per year I was eligible to receive 100% of my highest year's pay if I retired. Coincidentally, I had just reached the top of my pay scale. Although I could have worked longer and made more money, I was completely content to receive 100% of what I was already receiving. If you take into account the fact that no more deductions for retirement or 401(k) would be taken I would end up bringing home more money after retiring.I really didn't know what would happen that day I walked into my supervisor's office. I am fond of saying “I rolled the dice.” My new deputy director had denied my request for time off which was an agreed upon stipulation of my taking the position with the former deputy director. I had done my homework and gone to the Retirement Board and determined if I left at that point I would be able to have a pension equal to 100% of my salary. I filled out a retirement form and another request for leave. Towards the end of my meeting with my supervisor I brought up the subject of my vacation. She once again commented that she had denied my vacation and that was that. I told her I was sorry she felt that way and presented her with my two-week notice. I said she knew where to find me if she changed her mind. She didn't and two weeks later I retired. That was 12 years ago. I have never regretted one second of it. I have been able to do many things since then and have also realized how fragile life can be. Carpe diem.

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Getting access to dataWhen it’s in another system or data warehouse and can’t be directly accessOr requires additional authorization and associated requests for accessOr requires regular manual interaction with other teamsJoining dataWhen there’s no natural join key across different data sets but they need to be joinedOr the format of standard values like timestamps isn’t standard across data setsOr the format of one data set isn’t consistent over time and needs to be changed to even be coherently “joinable” with itselfTransforming dataWhen it’s in some ad-hoc made-up legacy format that can’t be read without writing custom codeOr using a legacy library to read correctlyOr abuses a format (think: JSON embedded in CSV)Dealing with “bad” dataWhen some values are just plain missingOr the values are nonsensical (e.g. 0 age — is that really “null”)Or the values are nonstandard (e.g. null or “NULL” or “NONE” all meaning null)Or the meaning isn’t ambiguous (e.g. time 13:45:01, in what timezone?)Or the meaning isn’t fully documented (e.g. average salary that’s actually “average salary for all employees hired after June 30 who aren’t special type T”)Maintaining and Debugging the Tool KitWhen you can’t get access to preferred tool or version in your infrastructure, or it requires a dependency you can’t installOr because tool X has a key bug that you had to debug and fix and patch manuallyManaging the organization(Usually) debunking tool hype: “shouldn’t we be using a GPU / deep learning / a graph database / the social graph / Twitter sentiment / sensor data / real-time streaming / SQL on Hadoop / today’s new hosted ML service / this thing that says it’s better than Spark?”Saying “no” to requests for analysis projects that are unlikely to be feasible or valuable