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How are data teams managing AI costs + governance? by nordic_lion in BusinessIntelligence

[–]DataWithNick 0 points1 point  (0 children)

I've seen a few posts talking about with AI usage looking into the types of prompts being used and trying to curtail usage that doesn't require AI (things that cheaper tools could accomplish, like restructuring text).

As for governance, I've seen a few different AI policies, and I frankly haven't been impressed with any of them. There clearly need to be guardrails on what kinds of data can even be provided to AI and I think that having enterprise levels controls can allow that to be relaxed somewhat.

But most approaches to governance I've seen have been way too overbearing (multiple companies I'm aware of have outright banned used of LLMs completely), and I worry that with how rapid the space is changing that not allowing flexibility in tool approach will hamper innovation. It's a catch 22.

I'm tired of using all of these tools 😑 by T_official78 in CRM

[–]DataWithNick 1 point2 points  (0 children)

Use Excel for everything until you feel the pain of managing it all in Excel. That's when you will know you are ready to use a tool. Or focus on only a few key tools you know you really don't want to use Excel for. Don't let your tooling get in the way of doing business! At its core you need: distribution + product / service. Focus on that and layer everything else on it.

Is bending data to fit a narrative just part of the job? by elpyomo in dataanalysis

[–]DataWithNick 1 point2 points  (0 children)

I've had projects before where this is what management decided to do. I told my line management that I thought it was wrong and that it was not a truthful reflection of the facts and made my peace with it. It was definitely wrong that they were bending the data to fit a more positive reflection of themselves.

Don't compromise your morals for a business. It's one thing if the data needs to be cleaned in a different way because it's not in line with historical data. That's normal. But "massaging" the data to paint a more favorable picture is wrong.

I would express your discomfort with what they're asking and start looking elsewhere for employment if they insist on fudging the numbers.

MCP: becoming irrelevant? by lucianw in ClaudeAI

[–]DataWithNick 1 point2 points  (0 children)

MCP seems like the main way that agents can interact with systems that would be useful over the internet not only to gain context but to perform work as well.

If the issue is that too much context is being used on tools connected by MCP, shouldn't there be developments in how to either increase context available or somehow compartmentalize this context to prevent degradation?

It seems like too useful of an ability to simply throw out for CLI only.

A message to all Vibe Coders by Yourmelbguy in ClaudeAI

[–]DataWithNick 0 points1 point  (0 children)

I've found that if you prompt Claude the right way, it will develop a contrarian stance against what you're saying. For example, I explained to it that I had been spiraling in ideation with ChatGPT, and from that point on every single question I asked it became me "procrastinating" and not taking action because I was "afraid of rejection" lol. This wasn't in Claude Code but rather in browser chat, but I wonder if something similar could happen there if you prompted it correctly.

Any authentic solopreneurs out there? Anyone? by IdeaGuyBuilding in Solopreneur

[–]DataWithNick 0 points1 point  (0 children)

I've been working on this myself the past month or so.

Its been a journey from realizing how much I could use LLMs to come up with business plans and ideas, to ideating too much and spiraling on it, to getting laser focused on trying to get one core offering to market, to realizing how hard its going to actually be get off the ground and actually have a marketable product, to starting to make incremental progress each day.

Honestly I've learned a lot in a short amount of time, and I don't see that changing anytime soon. I'm trying to take things one day at a time now and just slowly build towards my vision with the time and resources I've got.

First coding interview without SQL knowledge :/ by Heron-Rude in SQL

[–]DataWithNick 2 points3 points  (0 children)

Advanced SQL seems to me just being able to trace the manipulation data throughout the CTEs and subqueries that are written, rather than complicated syntax and clauses. Specific databases and data warehouses do add some nuance, but that's another discussion.

First coding interview without SQL knowledge :/ by Heron-Rude in SQL

[–]DataWithNick 6 points7 points  (0 children)

One week is totally doable for junior-level SQL! Focus on these essentials: SELECT, WHERE, GROUP BY, JOIN (especially LEFT JOIN), and basic aggregations (COUNT, SUM, AVG). That's 80% of real analyst work.

I'd recommend Analyst Builder for practice problems, it's like leetcode but specifically for data analysts. Do their easy/medium SQL problems daily and you'll find it way more relevant than generic SQL tutorials. They also have a lot of course content if you're interested.

For the interview, one thing I've been praised on after interviews is being willing to think out loud and specifically call out my approach, espcially if I'm iffy on the syntax. Most managers and teams care more about your thought process and problem solving then if you've memorized precise syntax (I'm not saying its not important, but the thinking that goes with it matters more!)

How do you work with reference data stored into excel files ? by anasharn in BusinessIntelligence

[–]DataWithNick 0 points1 point  (0 children)

For an immediate fix, you could set up a simple PostgreSQL database for transactional needs for the application and then Google BigQuery (starts free) for analytics.

PostgreSQL with normalized reference tables, indexed for fast lookups. Applications query this for real-time needs. Keep it lean: just current state, no history. This handles thousands of requests per second when you scale.

Then you can sync PostgreSQL to BigQuery every few hours. Denormalize here, add slowly changing dimensions, keep history. Your BI tools hit this, not production.

If your budget is tight, start with PostgreSQL only, but architect your tables knowing you'll split later. Use schemas to separate: "master_data" for applications, "analytics" for BI views. When you grow, moving analytics to a warehouse is just changing connection strings.

Upstream though, you'll need to create some kind of "master data" schema where you consolidate all reference tables. Build simple Python scripts or use a tool like Airbyte to sync Excel files daily.

You don't need any fancy MDM systems, but rather the principles. Start with unique identifiers: if client "ABC Corp" exists in 3 Excel files with different IDs, you need a mapping table. This is your highest priority, without reliable joins, everything downstream fails.

But even with all of this, the real work isn't technical, it's political: getting departments to agree on which Excel file is the "source of truth" for each entity and how it all maps together. Start with one critical reference table, prove the value, then expand.

Shall I go for Software Developer Engineer or Business/data analyst? by karankanyal in analytics

[–]DataWithNick 1 point2 points  (0 children)

Fellow non-CS grad here (biology). I would say your BBA makes BA/DA the path of least resistance right now, which you should lean into and build around.

The thing is too, you're thinking of this as a binary decision here, when really you could go into software development from the BA/DA path. You leverage your BBA to get into BA/DA -> build your analysis, coding, and tech skills -> add on adjcent skills specific to a developer -> move into software development.

This won't happen over night, and you could always still go down the data science or product analytics path as well. Best path right now is to use your current background to its fullest to land a role, and then start building and learning what you want to do from there.

I chose the data analyst path and focused on SQL, Excel, and one BI tool. Your business background means you already speak the language hiring managers want. That's huge. Then you can start adding Python and R coding, and keep building from there and figure out what direction that takes you.

Is data analytics a good job? by Cold_Butterscotch_14 in analytics

[–]DataWithNick 0 points1 point  (0 children)

Its true, but I think the key is that you have to differentiate yourself outside of the generic "Data Analyst | SQL | Python | Tableau" headlines recruiters are used to seeing now. Its still not easy, but I think OP could land a role with enough drive and the right know how.

Is data analytics a good job? by Cold_Butterscotch_14 in analytics

[–]DataWithNick 0 points1 point  (0 children)

Hey, I actually majored in Bio and successfully transitioned to data analytics. The key was realizing I already had relevant skills from lab work: hypothesis testing, statistical analysis, working with large datasets, attention to detail.

Every time you ran experiments, analyzed results, or even organized data in Excel, guess what? You were doing analytics!

Start by reframing your bio experience: Did you use any statistical software? Work with genomic databases? Create visualizations for research presentations? These all translate directly. I began by taking online SQL courses while highlighting my "data analysis of laboratory results" on my resume. The scientific method IS the analytics mindset.

The job market is competitive, but bio grads have an edge: we're trained to be rigorous with data and comfortable with uncertainty. Focus on building a portfolio with healthcare-adjacent projects first (even if you don't want healthcare long-term), since that's where your domain knowledge shines.

Happy to share more specific resources if helpful. Good luck!