I was giving a talk on DevOps recently and one of the questions from a person in the audience was how to get others to buy in. This person also had complaints that "DevOps" wouldn't work because their boss wasn't interested.

I think these are great examples of things that many developers and managers don't think about when they look to embrace DevOps. While there is no shortage of technical articles and videos on how to introduce automation or deliver features in a new way, what gets lost is the need for cultural change.

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One of the sales managers at Redgate Software posted an origin story about how he came to work at Redgate Software. It's an interesting story, and while I don't work in sales or have the same origin story, I don't know that I could have seen the place I'm in now early in my career.

When I saw the video, it made me think of this movie scene, where Rafiki asks us to look beyond what we see. I think this is a hard thing for many of us, especially when many of us have been leaning on our skills in the past. What we know and what we are good at doing has led us to this place. Picking a new direction, or thinking in a new way might be hard.

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I like the feedback system that Microsoft built for SQL Server. This used to be the Connect system, but all the bug reports and feature requests are now at feedback.azure.com. That's the place where you can send notes to Microsoft or vote on those that others have submitted.

In case you are wondering if this makes a difference, it does. Years ago, there pushback for Service Packs from Microsoft, and after a lot of lobbying from myself and others, and a number of votes, Microsoft released more Service Packs for a few versions. They've also made a few other changes in the product, or fixed bugs when enough people vote. Not always, but it does happen at times.

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One of the more successful uses of AI (artificial intelligence) has been in the medical field. It seems that there is a tremendous amount of data, high variability in some aspects of the target of the data (the patient), and a need to constantly review very tedious volumes of data. A place where AI might, and has, made a difference to help humans, not replace them.

There's a neat article that dives into a bit of how a medical algorithm works for detecting sepsis. As you scroll, the article tries to explain in layman's terms what the AI system(s) are doing. It goes through the necessity of regular data, which I think is a good design to push humans to gather data. It then talks about a variety of neural networks being used to analyze data, based on previous training from humans and past results.

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I use Libsyn to store podcast files. They've been a reliable service for me for years. Once in awhile uploads are slow, but things seem to work. The other day I went to upload a file and got a status message that there was an issue with database maintenance. When I looked at the status page, I saw a few updates. This was for a platform issue, and I saw this set of updates. I have the timing with my own comment:

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The last two years have changed the way many of us work in technology. The pandemic allowed many of us to work remotely, a challenge that many companies are struggling with. Or perhaps, management is struggling with and employees appreciate. In any case, no matter what your situation, the way we work has changed.

The change wasn't easy at times, and even though many people appreciated the extra time in their day from not commuting to work, they often had other challenges. Co-existing with others in a residence, finding a place to work that allows concentration, and even learning how to separate work from home life. These have been issues for many data professionals and other technologists. Even if we've had an easy time adapting, not all of our colleagues have felt the same way.

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Microsoft wants more people to move their database workloads to Azure. They are constantly adding new features, capabilities, even tools to help people move databases to some part of Azure. It's working well, as the latest quarterly report shows tremendous growth in Azure revenue. We don't know precisely how SQL Server or Azure SQL databases impact this, but I certainly see more and more customers moving relational workloads to the cloud. Some to Azure, some to other providers.

In an effort to entice people, Microsoft has a promotion that they were running in March, which was asking SQL Server 2012 customers to move to Azure and save with free extended security updates. They reiterated the promotion on July 12, when SQL Server 2012 went out of support. They calculate savings and promote benefits, which might be tangible and desirable to some of you. After all, not having a supported platform isn't an option for some people.

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I found this article to be an interesting look at how we might add ethics to AI systems in one area. As the article points out, "... today there is no broadly accepted AI ethics framework, or means to enforce it. Clearly, ethical AI is a broad topic ...".

Glad someone is thinking, or many people are, but sad that we aren't really moving in a direction that creates a better system for us humans to work under, or be bound by. To be fair, I do think this is a very difficult topic and hard for any large group of people to agree on what should be done.

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I wrote the other day about a culture of allowing mistakes. We know mistakes are going to happen, so we ought to accept them. Even stupid ones. I make them at times, my wife does, my kid does, so I have been working to not get upset and emotional about them. Instead, hold someone accountable, let them fix things, and try to do better in the future. It's what I'd want, and what I am trying to do with my family, friends, kids I coach, and others.

Many of us share knowledge and information. One of the reasons I love events like SQL Saturday is that others share their knowledge. I especially appreciate is when someone shares a solution and how or why they build it. Hearing their stories, the reasons for their decisions, and what works well is great. I learn in a similar way from blogs, which is why we syndicate blogs at SQL Server Central and include them in the newsletter.

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Building a culture that promotes teamwork, efficiency, retention, and other positive attributes can be challenging. While many organizations want accountability and responsibility, it is easy for managers to tip over into a blame-and-chastise pattern. When someone makes a mistake and a deadline is missed or a client gets upset, how do you respond?

I saw an article that gives some examples from the cybersecurity world about how to build a culture that allows mistakes and forgives them. We all make mistakes, and we should strive to correct them while working to avoid repeating the same mistake. But we will make mistakes again in the future, so how do we deal with them?

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