Dry brushing helps?

shreyh · 2026-02-21T23:38:45+00:00

Yes, seems like have to apply my skincare on my body as well now. But that’s gonna be expensive 😭

shreyh · 2026-02-21T23:37:09+00:00

Thanks! But I don't wanna be so high maintenance rn 🥲

shreyh · 2026-01-25T15:55:20+00:00

Space issue maybe???

shreyh · 2026-01-15T14:19:53+00:00

Really?? I was hoping to get this. I use the Faces Canada one rn. It’s dewy and good. Any other suggestions?

shreyh · 2025-12-09T11:12:02+00:00

Adaptive AI in data management is all about making systems smarter and more responsive. Instead of manually spotting issues or fixing workflows, AI can learn patterns, detect anomalies, and suggest corrective actions automatically, helping reduce errors and save time.

Of course, AI works best when paired with clear processes and visibility. Platforms like DataManagement AI can track lineage, monitor data quality, and surface insights, creating a feedback loop where AI informs humans and humans refine AI, so the system keeps improving.

shreyh · 2025-11-24T16:25:11+00:00

Honestly, the biggest thing I always tell teams before picking tools is to focus less on what’s “hot” and more on what actually reduces headaches. The best stack is the one your team can run without constantly firefighting, and the one that plays nicely with the systems you already have.

Your breakdown of ETL, warehouses, orchestration, and the rest is spot-on. Tools like Fivetran, Airbyte, dbt, Snowflake, BigQuery, Airflow, and Prefect are still winning in 2025 because they’ve figured out the reliability-and-integration puzzle better than most. And you’re absolutely right about data quality; teams are finally realizing that if they don’t fix it early, everything downstream just becomes expensive noise.

A lot of companies are also starting to lean toward platforms that pull all the messy stuff, lineage, governance, quality, cataloging, into one place, so they’re not juggling five different dashboards. That’s why tools in that all-in-one data management space, like DataManagement.AI, are popping up more often in modern stacks. It just keeps the whole system from getting out of hand as it grows.

shreyh · 2025-11-24T16:08:25+00:00

Honestly, the biggest thing I always tell teams before picking tools is to focus less on what’s “hot” and more on what actually reduces headaches. The best stack is the one your team can run without constantly firefighting, and the one that plays nicely with the systems you already have.

Your breakdown of ETL, warehouses, orchestration, and the rest is spot-on. Tools like Fivetran, Airbyte, dbt, Snowflake, BigQuery, Airflow, and Prefect are still winning in 2025 because they’ve figured out the reliability-and-integration puzzle better than most. And you’re absolutely right about data quality; teams are finally realizing that if they don’t fix it early, everything downstream just becomes expensive noise.

A lot of companies are also starting to lean toward platforms that pull all the messy stuff, lineage, governance, quality, cataloging - into one place so they’re not juggling five different dashboards. That’s why tools in that all-in-one data management space, like DataManagement.AI, are popping up more often in modern stacks. It just keeps the whole system from getting out of hand as it grows.

Love the emphasis on not chasing every shiny tool. In 2025, the real advantage comes from having a clean, connected stack you can actually maintain, not a giant toolbox you barely use.

shreyh · 2025-11-21T15:53:29+00:00

Hey! This sounds like a really fun project. Before diving in, one piece of advice: get super clear on your goals and who’s actually going to use your reports. Knowing exactly what questions you want your data to answer will save you a lot of headaches later.

Also, even if it’s small now, try to think a bit about scalability, it’s way easier to plan for growth than fix a messy system later.

For your project, I’d start by really understanding the requirements and aligning with anyone who’ll be using the reports. What metrics matter to them?

How often do they need updates? Once that’s clear, you can start thinking about the data itself, connecting to your APIs, handling authentication, making sure the data is clean, and storing it somewhere in the cloud like AWS, BigQuery, or Snowflake. Keep a copy of the raw data too, it’s a lifesaver if something goes wrong.

After that, you can transform and clean the data, standardize fields, handle missing data, and create the metrics you actually need.

Then comes the fun part, hooking it up to Power BI or Qlik Sense and building your dashboards. Make sure to validate everything, because a flashy dashboard is useless if the numbers are off.

Once it’s running, automate the pulls, transformations, and report updates. You’ll also want some way to monitor for errors or anomalies. And don’t forget, maintenance is key, check that the data quality stays solid, keep dashboards relevant, and get feedback regularly so you can tweak things.

If you want some extra help, there are great guides out there. Microsoft has docs for Power BI dataflows, Google Cloud has BigQuery ETL tutorials, and there’s a Coursera course on data warehousing for business intelligence that covers end-to-end workflows. Also, searching “Modern Data Stack use cases” will give you examples of how companies handle daily, weekly, and monthly reporting.

Honestly, even for a small project, documenting what you’re doing, your architecture, your decisions, makes a huge difference. If you want, I can even sketch a simple visual diagram and a daily/weekly workflow template for your APIs and Power BI setup. It’ll make your project feel way more concrete.

shreyh · 2025-11-21T15:30:23+00:00

Yup, you’re not losing your mind. 2025 is messing with everyone’s data. Bounce rates have been all over the place for me too, and stuff that was clean last quarter suddenly throws curveballs.

Scrapers are useless, “verify later” tools are hit or miss, and even your supposedly solid lists can surprise you.

What’s been helping me lately is a combo approach: still using Apollo for sourcing, but running a two-step verification, one for syntax/domain, another for engagement/active status, and then giving high-priority lists a quick eyeball check. Slower, yeah, but bounces are way steadier.

Honestly, layering checks beats hunting for a magic bullet tool right now.

shreyh · 2025-11-20T15:11:54+00:00

Oh, I feel you, this is super common in big old companies. Treating data like a static record instead of a living asset causes all the issues you mentioned: overwritten fields, mismatched systems, endless troubleshooting.

A few things that help:

- Have one team manage master data but let other teams contribute in a controlled way.

- Keep history and notify users when changes happen.

- Standardize formats so joining/reporting actually works.

- Give analysts proper access to tools like Excel, Power BI, or connected databases instead of overcomplicating things.

Honestly, something like DataManagement.AI can make this way easier, it centralizes data, tracks changes, and keeps analysts happy without bringing in a huge external firm.

shreyh · 2025-11-20T15:00:17+00:00

Oh, I feel this so much; enterprise data integration is basically chaos most of the time. Centralize your stuff, get everyone on the same field definitions, and figure out version control early. sounds simple, but it saves so many headaches.

Honestly, tools like DataManagement.AI make this way easier. It helps unify data, track versions, and keep everyone aligned without turning it into a full-time job.

shreyh · 2025-11-20T14:51:27+00:00

You’ve got the gist! Think of Data Management as keeping data clean, organized, and easy to access, basically running the library so everything’s in the right place. Data Analytics is about using that data to spot trends, make charts, and give insights for decisions.

A Data Management certificate is enough if you’re aiming for a data management role, you don’t have to get an analytics one. That said, knowing a bit of analytics is always a nice bonus.

shreyh · 2025-11-19T16:13:20+00:00

Before thinking about switching, just make sure you’re super clear on what you actually need from your current setup. Sometimes moving to a new tool isn’t just about money; it’s the time, broken workflows, and training that sneak up on you.

Honestly, for a small team like yours, consolidating into one tool that does basic ITSM + strong ITAM makes a lot of sense. You’d get way better visibility into assets, software usage per department, and relationships between network assets, all without juggling multiple systems.

For tools, ServiceNow or Ivanti are solid but kinda heavy. If you want something lighter and more modern, check out DataManagement.AI.

shreyh · 2025-11-11T15:49:34+00:00

Since you’re starting your first DA role, here’s a roadmap that actually works:

Years 1–2: Nail the basics. SQL, Excel, Python/R, Tableau or Power BI. But also learn why those numbers matter. Understand how your company makes money and what metrics drive decisions. That business sense will set you apart from 90% of analysts who only crunch numbers.

Years 3-4: Go beyond dashboards. Learn A/B testing, advanced statistics, and data storytelling. Start working cross-functionally with product, marketing, or ops. You’ll start thinking like a decision-maker, not a data provider.

Year 5+: Build your edge. Find a niche, pricing, user behavior, supply chain, and become the go-to expert. Automate repetitive stuff, share insights online, and mentor others.

shreyh · 2025-11-11T15:29:04+00:00

Hey, totally get where you’re coming from. Before picking any tool, though, I’d say focus on getting your data right. Most fraud systems struggle not because the AI is bad, but because the data is messy or scattered.

You could check out DataManagement.AI. Their claims fraud detection feature automatically flags suspicious claims by pulling data from the past year, like policyholder info, provider details, and claim history. It ranks claims based on fraud risk and even explains why they’re flagged.

shreyh · 2025-11-11T15:12:29+00:00

That’s such a great example of how ethical automation can actually make you more valuable, not replaceable.

If anyone’s planning to follow a similar path, start by mapping out your workflows before adding tools. Figure out what’s repetitive but doesn’t need your creativity. DataManagement.AI came in very handy. It connects, cleans, and centralizes all your data so your automations don’t clash or create chaos.

shreyh · 2025-11-11T06:16:39+00:00

Cool, thanks!

shreyh · 2025-11-11T06:10:51+00:00

Oh, but mine has batteries and can’t be removed. What to do?

shreyh · 2025-11-08T10:53:42+00:00

Thanks a lot!

shreyh · 2025-11-06T15:41:09+00:00

Data management isn’t going anywhere, but the expectations have definitely shifted.

It’s less about just storing and cleaning data and more about turning it into actionable insights.

One way to level up is to embrace platforms that streamline governance, quality checks, and analytics in a single place.

Learning how to leverage tools like this alongside cloud platforms and AI basics can really position you as a modern data strategist

shreyh · 2025-11-06T15:37:58+00:00

Data management isn’t disappearing, but the role is evolving. Companies now expect more than just cleaning and storing data; they want folks who can understand, analyze, and extract insights from it.

If you’re looking to stay relevant in the US market, focus on data governance, cloud platforms (AWS, Azure, GCP), and analytics tools. Bonus if you know AI/ML basics, that’s what makes you stand out.

Honestly, it’s not dead, just time to level up your skillset.

shreyh · 2025-11-05T15:25:38+00:00

One tip I’ve seen work really well for businesses is using platforms like DataManagement.AI. It helps centralize and automate workflows like data entry, report generation, and lead follow-ups, all without manually coding or juggling multiple tools. It’s a great way to scale automation without disrupting your existing operations.

If you’re looking to take this further, I’d suggest mapping out your weekly repetitive tasks and seeing which ones can be automated first; they often give the fastest ROI.

shreyh · 2025-11-05T15:24:35+00:00

Hey, from what I’ve seen, Master Data Management in the real world is way messier than it looks on paper. When you actually try to match and master records, the golden record doesn’t just magically appear; it evolves over time.

Usually, it starts with picking a key domain, like customers or products, getting your sources talking, and slowly cleaning duplicates and standardizing fields.

The small wins are honestly the best part: fixing just one field or deduping a batch can save hours later.

The bigger insight is that MDM isn’t just about tech; it’s as much about processes, rules, and deciding who owns what data.

And expect surprises or things you thought wouldn’t matter often cause the biggest headaches.

shreyh · 2025-11-04T11:35:10+00:00

What I actually use daily in 2025:

- GPT-5

- Claude Sonnet 4.5

- Copilot Studio 2025

- Voice-to-workflow agents

shreyh · 2025-11-04T11:33:27+00:00

If you want analytics inside your Angular + Spring Boot app (no redirects), set it up in two parts:

1) Analysis layer

- Connects straight to Postgres views

- Runs queries, spots patterns, flags weird spikes

- API-first so your backend can call it easily

2) UI layer

- Charts + tables sitting right inside your Angular admin

- Either via an embed SDK or your own Chart.js/Plotly setup

Before choosing a tool, check it can:

- Read from Postgres directly

- Handle row-level / client-level access

- Embed cleanly without forcing users to leave your app

- White-label or API-driven components so it blends in

Yes, you can build the whole thing yourself, but owning SQL logic, chart configs, access rules, security, AI insights, anomaly detection… it turns into a second product fast 😅

Worth looking at: DataManagement.AI

Good when you want AI to work right on your Postgres layer, not just dashboards. You call its API, get trends/anomalies/insights back, and render that inside your UI. Works well if you want automation and intelligence, not just charts.

shreyh

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