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When should you fix data quality at the source instead of adding transformation step?

A common challenge in analytics is dealing with inconsistent formats, duplicate records, missing values, and mismatched definitions across systems. Transformation tools can standardize much of this, but repeated fixes can turn into a growing collection of exceptions and workarounds.At some point, it becomes worth asking whether the pipeline is solving the problem or simply compensating(Read More)

A common challenge in analytics is dealing with inconsistent formats, duplicate records, missing values, and mismatched definitions across systems. Transformation tools can standardize much of this, but repeated fixes can turn into a growing collection of exceptions and workarounds.
At some point, it becomes worth asking whether the pipeline is solving the problem or simply compensating for it.
For those working with ETL, Alteryx, SQL, or Python pipelines, what tells you that a transformation belongs in the pipeline versus being a problem that should be fixed in the source system?
How do you handle it when the source team cannot make changes?

0 0 36 2 weeks ago

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Naomi Teng

October 14, 2026

When should you fix data quality at the source instead of adding transformation step?

A common challenge in analytics is dealing with inconsistent formats, duplicate records, missing values, and mismatched definitions across systems. Transformation tools can standardize much of this, but repeated fixes can turn into a growing collection of exceptions and workarounds. At some point, it becomes worth asking whether the pipeline is solving the problem or simply(Read More)

A common challenge in analytics is dealing with inconsistent formats, duplicate records, missing values, and mismatched definitions across systems. Transformation tools can standardize much of this, but repeated fixes can turn into a growing collection of exceptions and workarounds.
At some point, it becomes worth asking whether the pipeline is solving the problem or simply compensating for it.
For those working with ETL, Alteryx, SQL, or Python pipelines, what tells you that a transformation belongs in the pipeline versus being a problem that should be fixed in the source system?
How do you handle it when the source team cannot make changes?

0 2 59 2 weeks ago

Subscriber

Naomi Teng

October 14, 2026

Will Deep Learning Look Different in 2027?Will Deep Learning Look Different in 2027?

For over a decade, progress in deep learning has largely been driven by larger models, more data, and greater compute power. But as we approach 2027, the industry seems to be shifting toward efficiency, specialized architectures, and domain-focused models rather than simply scaling everything up. Advances in Mixture of Experts (MoE), Small Language Models (SLMs),(Read More)

For over a decade, progress in deep learning has largely been driven by larger models, more data, and greater compute power. But as we approach 2027, the industry seems to be shifting toward efficiency, specialized architectures, and domain-focused models rather than simply scaling everything up.

Advances in Mixture of Experts (MoE), Small Language Models (SLMs), model compression, and agentic systems are raising an important question: will the next wave of breakthroughs come from bigger models, or from smarter ways of building and deploying them?

With compute costs, energy consumption, and inference efficiency becoming major concerns, many researchers and enterprises are rethinking the traditional scaling-first approach.

By 2027, do you think deep learning will still be dominated by larger foundation models, or will efficiency and specialization become the industry’s primary focus? Curious to hear where the community thinks the field is heading

0 2 131 2 months ago

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Naomi Teng

September 9, 2026

Are BI dashboards making teams more data-driven, or just better at looking at data?

Most organizations invest heavily in dashboards, reports, and KPI tracking. Yet many teams still struggle to turn insights into action. I’ve seen cases where everyone agrees on the numbers, but decisions are still made based on intuition, politics, or urgency rather than what the data suggests. So I’m curious: What separates a dashboard that’s actually(Read More)

Most organizations invest heavily in dashboards, reports, and KPI tracking. Yet many teams still struggle to turn insights into action.

I’ve seen cases where everyone agrees on the numbers, but decisions are still made based on intuition, politics, or urgency rather than what the data suggests.

So I’m curious:

What separates a dashboard that’s actually used for decision-making from one that’s just monitored?
Is the biggest challenge data quality, stakeholder buy-in, or something else entirely?
Have you seen a BI initiative genuinely change how an organization operates?

0 3 157 2 months ago

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Naomi Teng

August 17, 2026

Why am I getting SettingWithCopyWarning in Pandas?

Hi everyone, I’m new to working with Python and Pandas at my job, and I’ve started seeing the SettingWithCopyWarning while cleaning data. The confusing part is that my code still runs, so I’m not sure whether this is something I can ignore or if it’s actually causing problems. I’ve read a few explanations online, but(Read More)

Hi everyone,

I’m new to working with Python and Pandas at my job, and I’ve started seeing the SettingWithCopyWarning while cleaning data. The confusing part is that my code still runs, so I’m not sure whether this is something I can ignore or if it’s actually causing problems.

I’ve read a few explanations online, but I’m still struggling to understand what this warning really means. Is it telling me that I’m modifying a copy instead of the original DataFrame? If so, what’s the recommended way to avoid this warning and make sure my changes are applied correctly?

I’d appreciate a beginner-friendly explanation, especially if someone can explain why this warning exists and the best practices for handling it in real projects.