BK’s Professional Learning Plans for 2018 #tsql2sday

With 2018 coming, I’ve thought about the question, “What do I want to be when I grow up?” I ask this question every few years because it helps me develop a plan of attack with respect to learning and opportunities to pursue. As a result, this T-SQL Tuesday topic comes at exactly the right time.

Back on the Certification Train – IT Architecture

I maintain some legacy certifications, MCSE (NT 4 – yeah, I’m a grey beard) and Security+, and I have an on-going certification, CISA, that requires continuing education credit renewal. However, I’ve not working on any certifications in a while now. Within the IT architecture realm there are several certifications that are out there and they’re becoming more well known in the industry. So one of my focal areas will be preparing for those certifications.

However, there’s a difference between pursuing certifications to get the credentials and preparing for certifications to ensure you have the knowledge those certs are supposed to represent. I’ve always been a believer in the latter category and I tend to “over prepare” for the test because I care about the knowledge. I know this is the right way to go, so there’s a lot in this area alone I’ll need to work on. As with any infrastructure architect, I have areas of deep knowledge in particular segments of IT, however, it will be good to go back and fill in gaps and strengthen areas where I don’t know as much because it’s not an area I work with regularly.

Data Science

I was a physics and mathematics major as an undergraduate at The Citadel, the Military College of South Carolina. Before that, I went to a specialized magnet school for science and mathematics for high school, a school consider a “super school” here in the United States. As a result, the fundamentals of data science are something I’ve been working with for a long time, just not specific to business. You don’t face performing aerodynamic testing (high school) or trying to solve elemental abundance / nucleosynthesis questions (college) without having to do data science. We just didn’t call it data science. We considered a part of proper research. However, as a “separate” discipline, data science is an area I’ve always loved. Looking at data, understanding what it’s trying to tell us, and figuring out the patterns in it – those are things I’ve always loved doing. So naturally I’ll be working on the Data Science track from Microsoft. I’m about a third of the way done, so I’ve got a lot of work in the coming year.

Cloud Stuff

Not “star stuff,” but cloud stuff (physics pun intended). The majority of my experience has been with on premises solutions. While I understand the cloud solutions at the 10,000 foot level and I have a lot of experience in virtualization specifically along with some experience in Azure, none of it compares with what my on premises skill set looks like. The reality is that cloud isn’t going away, whether we’re talking private or public cloud offerings. Cloud offers the ability to get a jump start on delivering a product. That’s a compelling reason right there to pursue a solution using it for organizations. After all, the usual advice is to either be the first or the best in a space. But the reality is that if you’re first with a great story, it’s harder for anyone else to make a presence. It takes a lot more resources to do so. Therefore, I expect that the cloud trajectory is only going to continue upward.

Don’t get me wrong: there’s still a huge place for on premises solutions. That won’t be going away any time soon. However, most organizations of any size have moved to a hybrid approach. If you’re in the data field and you aren’t prepared or preparing for this, you’re making a huge gamble. Our data skill sets will always be useful. After all, we’re collecting more and more data each and every day. However, being able to apply the skill set to where the new technology is will be a determining factor for career/employment security.