Data for Profit $$

As a systems and database administrator, my world revolves around structure—clean tables, optimized queries, and reliable data flows. So when I was tasked with analyzing several rheumatoid arthritis patient databases to extract treatment insights, I expected a straightforward dive into well-organized records. Instead, I hit a wall: fields meant for prescriptions, dates, and test results were blank, while the notes field overflowed with entries like “Pt started infliximab last wk, CRP high.” The data was there—just not where it was supposed to be. That’s when I turned to natural language processing (NLP) paired with machine learning (ML), and it transformed how I approach messy systems. Here’s what I learned about their practical power—and why human intuition still plays a critical role.

The Challenge: Data Defying Structure

In an ideal world, medical databases are tidy—columns like “Prescription_Date” or “Test_Result” filled with precise entries. But in these datasets, doctors had bypassed the structured fields, pouring vital details into free-text notes. Traditional SQL queries were useless; you can’t search what isn’t indexed. This wasn’t a fluke—it was consistent across multiple systems. As a database admin, I could’ve thrown up my hands, but I saw an opportunity. The data wasn’t missing; it was just hiding in plain sight.

Why NLP + ML? A Systems Solution

NLP can read and interpret unstructured text, while ML learns to spot patterns across variability—perfect for taming this chaos. My goal was to extract drug names (like infliximab), dates, and test results from those notes and rebuild them into a structured format. This isn’t just a medical fix—it’s a systems-level solution. Think customer support logs, audit trails, or any database where free-text fields hoard critical info. NLP and ML can bridge the gap between human input and machine-readable data.

How I’d Implement It: A Database Admin’s Playbook

Here’s how I’d deploy NLP and ML to solve this, step by step:- **Define the Target:** Pull entities—drugs, dates, results—from notes into a table like {Drug: “infliximab,” Date: “03/15/2025,” Result: “CRP 50 mg/L”}.- **Prep the Data:** Clean the text with tools like Python’s `nltk`, normalizing terms (e.g., “Remicade” to “infliximab”) using a medical lexicon like UMLS.- **Go ML:** With the range of note styles—terse to verbose—I’d pick a machine learning model like BioBERT, pre-trained on medical text. I’d train it on labeled samples to recognize entities, adapting to doctors’ quirks.- **Extract and Link:** Using libraries like Hugging Face, the model would tag “infliximab” as a drug, “last wk” as a date, and link them contextually (e.g., “started infliximab”).- **Rebuild the System:** Output a structured dataset, validate it against any filled fields, and integrate it back into the database—ready for analysis or real-time use.

The Human Edge: Seeing What ML Misses

where humans shine. ML excels at patterns, but it might’ve scanned those empty fields and concluded the data was absent—game over. I noticed something different: the notes were packed with what we needed, just misplaced. That intuition—spotting intent behind the chaos—isn’t easily coded. Doctors weren’t neglecting data entry; they were prioritizing narrative over structure. Recognizing that shift let me redirect the problem to NLP and ML, rather than writing off the dataset. Humans can connect dots that algorithms might not even see.

The Impact: From Notes to Insights

With this approach, I could’ve turned those rheumatoid arthritis databases into a goldmine—tracking infliximab use, treatment timelines, and outcomes like CRP shifts across thousands of patients. Beyond healthcare, imagine applying this to system logs to catch anomalies or to CRM databases to mine client feedback. The payoff is efficiency: what took weeks of manual review becomes hours of automated extraction, with accuracy hitting 80-90% (per studies like those in the *Journal of Biomedical Informatics*).

The Caveats: Systems Aren’t Foolproof

It’s not flawless. Doctors’ shorthand (“inflix”) or vague terms (“CRP up”) can stump models. Training requires labeled data, and large datasets demand robust infrastructure—cloud resources or a beefy server. Still, as a systems admin, I’d take that trade-off over unstructured limbo any day.

Why It Matters to Me

This experience reframed my role. As a database admin, I’m not just maintaining systems—I’m bridging human behavior and technology. NLP and ML didn’t just solve a problem; they showed me how to adapt when data defies design. And that human spark—catching what’s there but out of place—keeps us ahead of the machines. Next time you’re managing a system where the data’s hiding, consider this combo. It’s not just about structure; it’s about finding the signal in the noise.

If you’re anything like me, your career looks like a vibrant mosaic—decades of technical expertise, leadership, client wins, and a knack for tying tech to business value. I’ve spent 25+ years mastering Linux, architecting data pipelines with Hadoop and Snowflake, leading teams, and winning clients through pre-sales demos. But here’s the catch: in a job market obsessed with specialists, a varied background can feel like a hurdle. Sound familiar?

Business execs often want candidates who fit neatly into boxes like “DBA” or “cloud admin.” If you’ve worn multiple hats—say, building Kubernetes clusters one day and pitching solutions to C-suites the next—it’s harder to convince an ATS or recruiter you’re the “perfect fit.” But here’s the truth: your diverse experience is a strength, not a liability. It’s what makes you adaptable, strategic, and ready to deliver results where specialists might fall short.

Here’s my guide to navigating the job hunt when your experience is “too varied”:

1️⃣ Own Your Narrative: Don’t let rigid job descriptions define you. Tailor your resume and LinkedIn to highlight skills that match the role, but frame your breadth as a unique edge. For example, I emphasize how my Linux and cloud expertise, paired with pre-sales success, lets me solve technical problems and drive business outcomes. Pro tip: Use metrics (e.g., “cut system costs by 20%”) to show impact, not just skills.

2️⃣ Target Generalist-Friendly Roles: Seek jobs that value cross-functional expertise, like Solutions Architect, IT Consultant, or Pre-Sales Engineer. These roles reward your ability to bridge tech and business, unlike narrow admin positions. Companies with innovative cultures (think cloud providers or tech consultancies) are more likely to see your versatility as a win.

3️⃣ Tackle the Specialist Bias Head-On: In interviews, prove you’ve got depth and breadth. Share detailed stories—like troubleshooting a complex data pipeline or leading a multi-cloud rollout—to show you’re as skilled as any specialist. Then, highlight how your diverse perspective delivers extra value, like aligning tech with strategic goals.

4️⃣ Network to Bypass Filters: ATS and HR often favor specialists. Go straight to hiring managers or peers via LinkedIn or industry groups. Pitch your unique blend of skills: “I bring hands-on IT, client-facing expertise, and leadership to deliver end-to-end solutions.” It’s how you get past the “box-checking” mindset.

5️⃣ Upskill Strategically: If you’re eyeing a specific industry (e.g., healthcare IT), grab a quick certification (like ITIL or cloud credentials) to boost your fit without narrowing your scope. It shows you’re adaptable and ready to dive in.

Your varied experience isn’t “too much”—it’s what makes you a problem-solver who can connect the dots others miss. Whether you’re architecting systems, leading teams, or winning clients, you’re delivering value that rigid roles can’t contain.

Fun fact: I crafted this post with the help of AI, based on my resume and current job search. It’s one more tool I’m learning to use to navigate the market and share insights with this community. Embracing new tech is just part of the journey!

💬 What’s your take? You’ll have to go to LinkedIn to comment. 🙂

#JobSearch #CareerAdvice #ITCareers #TechLeadership #GeneralistVsSpecialist

I have opted out of renewing this Domain and Website. Thankfully, the good folks at the Internet Archive will keep the history of this site for “all time” or ultimate entropy, whichever comes first.

It’s been fun, sometimes funny, and always safe. Remember kids, safety is third. 🙂

PS: I just realized this site is 13 years old! WOW! :-O

So I ordered some flip flops. When I got the email that they had been delivered. I went outside to look around and they’re not anywhere. I tracked the package via FedEx. Hilarity ensued. And I never got my package.

This shouldn’t be news to anyone, but it needs to be said. Buzzfeed has no, Zero, probably negative value on a healthy society. So why does it exist?

I could go on at length from a biblical perceptive, but let’s focus on the local perspective. (See how when I say “but” nothing before the but matters?

Buzzfeed is class cannibalism. If you’re rooting for the rich peoples (Projecting your life onto theirs) or rooting for the “take down” pieces (fuck those elite fucks), either way you win. You can view everything they write from your own perspective and feel like a winner.

The only way you win this game is to totally fucking ignore anything they “publish”. There are probably a few other sites that should be ignored, but I don’t know your newsfeed. BTW, Twitter is NOT news.

IMHO.

A few months ago I was thinking about buying an SSD to put in a FreeNAS system. Mind you, I already have 2 free standing NAS devices using traditional spinning disks. I was considering this because I can download a 2.4G file in about 1 minute to my OS Disk, and M2 SSD. It takes nearly 5 mins to download it directly to my NAS. Then I realized I could by a 2TB M2 SSD for $200 and use it instead. I still have my NAS for backup.

The point is, I was considering using old tech to solve a problem because I had it laying around when a “better” new tech solution was available cheaply.

I see IT organizations doing similar things. 5 year old technology finally broke and was replaced. The old tech still had “usable” or “fixable” parts and was put in a corner. A few years later it’s still there, covered in dust, and taking up space. It’s worthless and now takes effort to recycle it. Worse, you have several other things in similar condition that have been impeding your work by being in the way.

Those old laptops, tablets and PC’s aren’t getting any younger. Given the rate of change in technology, if it’s over 3 years old, it’s probably obsolete and better replaced than reused. This is not a hard rule, but you should always consider the reasonable lifetime when keeping replaced tech. Puppy Linux exists to give new life to old hardware. Worth it? Not for most.

I’m currently helping an organization that doesn’t have full time IT. Well intended people who want to save money, stack “fixable” or “old but running” laptops all over the place. So much so, that it’s now difficult to find anything. Your 10+ year old desktop it not worth re-purposing.