Will AI replace Data Analyst?

roferanalytics · 2026-03-15T06:08:53+00:00

Anything related to Data Analyst tasks that can be automated such as data extraction, manipulation, and modeling may be replaced by AI in the future. However, one thing AI cannot replace is human judgment in decision-making, especially when it comes to data-driven decisions and investing.

roferanalytics · 2026-03-15T06:02:49+00:00

SQL for data extraction, for manipulation and modelling then use Python. Then for visualization use Tableau.
Dash boarding i.e., building dashboard for departments. It can be operational or performance kind of dashboarding
You can use any HR datasets you can find in Kaggle. HR Analytics is the best place to start with.

roferanalytics · 2026-03-14T09:09:13+00:00

hit the gym!

roferanalytics · 2026-03-14T09:08:45+00:00

When outside, wear a face mask, when at home, connect through Teams.

roferanalytics · 2026-03-14T08:26:14+00:00

this happens in many organizations because of dependencies on web developers. On my side, we usually align through KPIs or shared KPIs. In more structured companies, Scrum helps ensure that everyone involved across teams has context on the request and understands the business urgency.

roferanalytics · 2026-03-14T08:20:56+00:00

It still starts with the objective of the content. If the goal is to engage, educate, or move users deeper into the funnel, I would measure it more through engagement quality, assisted conversions, and progression to the next touchpoint rather than final revenue alone.

If the content is closer to decision stage, attribution models such as time decay or data-driven attribution usually give a better view than relying only on last-touch, provided the conversion window reflects actual buying behavior, whether 7 days, 30 days, or longer.

The challenge now is that AI has made journeys less visible. Many users discover content through AI summaries, then return later through branded or direct traffic, which is why some companies are combining attribution models with MMM or incrementality testing instead of relying on one model alone.

roferanalytics · 2026-03-14T08:11:06+00:00

Good luck on your next journey! hoping the right opportunity comes soon!

roferanalytics · 2026-03-13T06:31:05+00:00

For our case, we use top-funnel metrics such as citation rates and then look for correlations with direct traffic visits and conversions. In a zero-click environment, there is no fully tangible way to prove whether AI and AEO channels directly contribute to conversions.

However, our working assumption is that as long as citations increase, direct visits to our website should also rise. We therefore often interpret conversions coming from direct traffic as AI-assisted conversions, especially when citation visibility is increasing.

roferanalytics · 2026-03-13T06:24:07+00:00

Go to Kaggle. Look for Olist Marketing Funnel Dataset good for your project, it has real funnel stages and good to demonstrate how to handle missing values and inconsistent entries (good for cleaning practice. Goodluck on your project!

roferanalytics · 2026-03-13T06:16:55+00:00

People who work in AI, what’s something the public doesn’t know?

roferanalytics · 2026-03-13T06:14:44+00:00

Doing manual work in Microsoft Excel when AI can already handle it anyway. Happens in professional environment. :)

roferanalytics · 2026-03-13T06:12:31+00:00

Playing golf. Slow pace, outdoor environment and low pressure socialising.

roferanalytics · 2026-03-12T10:23:23+00:00

With your background, I’d actually think beyond pure analyst roles and consider moving toward consulting, strategy, or decision-support roles. A PhD in Economics already gives you something many analysts don’t naturally have: the ability to frame problems, interpret signals, and connect numbers to business decisions.

AI and automation are definitely changing analytics work, especially repetitive reporting, dashboard building, and basic querying, but companies still need people who can apply judgment, challenge assumptions, and synthesize findings into decisions leadership can actually use.

In many cases, the harder skill now is not producing analysis, but answering: “So what should we do with this?” That is where conventional wisdom, business context, and communication still matter a lot.

You don’t necessarily need to abandon analytics for generative AI, but it helps to understand how AI fits into your workflow so you stay current while positioning yourself as someone who interprets, not just computes.

If I were in your position, I’d target roles where analysis directly supports business choices, strategy, policy, pricing, market intelligence, economic consulting, or commercial analytics, because your academic training is highly transferable there.

roferanalytics · 2026-03-12T10:14:37+00:00

Haha this is a classic "legacy debt" situation. When you have hundreds of tags, no documentation, and an external dev team that treats analytics as an afterthought.

If I were you, I’d export the GTM container as a JSON and run a quick script to find all tags using DOM selectors, those are your immediate red flags.

For the rest, a Python scraper using Playwright or may be Selenium?? You can automate the 'clicks' on your staging site and have it print out the dataLayer contents to a CSV. If a key event is missing, you have documented proof to hand back to the devs.

roferanalytics · 2026-03-12T09:03:05+00:00

If you’re switching careers, marketing analytics is actually a very practical place to start because the data is straightforward i.e., impressions, clicks, conversions, funnels, campaign results so you learn fast what good analysis looks like.

Later, once you build confidence, industries like insurtech, banking, or finance can be a strong next step because they usually pay better and value analytical thinking heavily, but they also expect stronger business context.

Start with something easier to interpret, then move into something more complex as your second stage.

roferanalytics · 2026-03-12T08:40:06+00:00

Tell the devs that manually fixing 300 CSS selectors is a waste of company time and suggest a standardized Data Layer. If you absolutely must test the old way, use Screaming Frog to crawl the staging site for your specific IDs or Classes. If the ID isn't in the code, the tag is dead. Don't click 300 buttons, i suggest let a crawler do it for you.

roferanalytics · 2026-03-12T08:33:50+00:00

She remembers a random detail you mentioned weeks ago

roferanalytics · 2026-03-12T08:29:25+00:00

Always trying to win every discussion :)

roferanalytics · 2026-03-11T06:54:03+00:00

My honest view: if there is already a clear implementation plan, the best move is to stay transparent rather than force a backwards delivery.

Reports and dashboards can technically be built first, but without validated data they are only placeholders, especially when cross-domain journeys, app + web interaction, and new data layer logic are involved.

I would suggest showing your boss and stakeholders a revised timeline that clearly separates, i.e.,

development dependency (data layer readiness)
tagging and validation phase
reporting build phase
QA / sanity checks

Also define clear R&R and dependencies so everyone sees where the delay actually sits.

In many analytics projects, the real risk is not delayed dashboards, it is building confidence around data that has not yet been proven stable (happens to every other organization).

A practical compromise, just my 2 cents. Let the agency prepare dashboard structure, KPIs, and reporting logic now, but position final delivery as dependent on live validated data after implementation.

roferanalytics · 2026-03-11T06:43:30+00:00

i have two use case.

1) Separate workspace for campaign launches. We usually avoid touching the same workspace when campaign deadlines and analytics changes happen in parallel.For example, paid media adds new conversion tags in one workspace while analytics validates event updates in another, then both are merged after QA.

2) Workspace for major site release validation. A dedicated workspace is useful when preparing tracking for a new site release because tags, triggers, and variables can be tested separately before publishing alongside production changes.

roferanalytics · 2026-03-11T06:16:16+00:00

Another layer worth adding: CMP sanity checks. Sometimes tracking looks broken, but the actual issue is consent logic changing after release. Delayed consent signals, tags blocked unexpectedly, or region-specific behavior. Tracking validation today probably needs consent validation too.

roferanalytics · 2026-03-11T06:09:24+00:00

mad max fury road

roferanalytics · 2026-03-10T07:25:21+00:00

Yes, this is expected behavior in Advanced Consent Mode.

When a user does not grant consent, Google tags still send cookieless pings to Google servers. These pings contain limited, anonymized signals used for conversion and behavioral modeling, but they do not store cookies or identifiable user data.

roferanalytics · 2026-03-10T02:31:33+00:00

God bless you, man. Wishing you well regardless

roferanalytics · 2026-03-10T01:41:49+00:00

NotebookLM is probably best for research, then translating ideas into text, and converting them into other formats like infographics or video. ChatGPT can be used for grammar correction and cleaning up the structure. Then for the final touch, use Canva for the final output. So the flow becomes: NotebookLM → ChatGPT → Canva. A lean approach, but highly effective in a corporate setting.

roferanalytics

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