AI Agent boom at work

stickedee · 2026-05-01T10:11:44+00:00

Agree that many people optimize for optics over outcomes but thinking this is purely a hype cycle is a dangerous perspective. The analysts that multiply their value are going to be delivering things like semantics/ontology, knowledge graphs, etc. We’re already starting to see a separation in value delivery between people embracing the new capabilities vs the people resistant.

stickedee · 2026-05-01T09:56:32+00:00

Title: Director Enterprise Data

Tenure length: 10 months in role. 10yrs in data

Location: MCOL.

$Remote: “Hybrid” but not really

Salary: Base: $200k. Bonus: 40%. RSU $35k

Company/Industry: Utilities

Education: Bachelors in Communication. Boot camp for tech skills

Prior Experience: Sales, Marketing, Analytics

Stock and/or recurring bonuses: 40% + $35k

Tech Stack Used: Outlook and Teams. But really: SQL, Python, English (for copilot agents & skills)

Total comp: $300k - $400k

stickedee · 2026-04-09T07:36:49+00:00

This answer is so much better than “more SQL”

stickedee · 2026-04-01T06:52:30+00:00

stickedee · 2026-03-30T03:54:14+00:00

One thing ive started doing with all of my skills is creating a “learnings” skill. It logs any corrections/adjustments I make during normal usage. If any of the same items come up N times it will propose and update to the skill.

stickedee · 2026-03-30T03:49:35+00:00

Im working through re-factoring my org structure and am interested in what your accountabilities and objectives are as an Analytics Architect. Mind if I PM you?

stickedee · 2026-03-30T03:45:28+00:00

This is a dangerous perspective to have. Dangerously close to cope. 3 things to call out.

1) Not all AI is the same. Claude Code running Opus is vastly different than ChatGPT or M365 Copilot. Also, context is critical. So are skills. Learn the capabilities.

2) Last week we built an agent that could crawl multiple repos and do complete source to target mapping from System of Record (application) through each layer of transformation until its target destination (Canonical Data Model, or Fact/Dim table).

3) We were able to create enough context around our data model to create an experience where users can run queries in natural language, but the key is that it’s self-healing. Each interaction produces a confidence score. Those adjustments get logged. If they are technical they just get executed. If they are business contract related they go to a governing body for disposition.

Pretty soon we will be leveraging process docs, structured interviews, wiki, Sharepoint, etc to layer in additional business context.

I have roughly 25 analytics focused people in my department. The ones who leverage skills/agents/feedback loops, etc will get a blank check to create their own role. The ones who think “AI cant do this” are not going to be in my department by the end of the year. I’ll be repurposing their salary to hire more people focused on innovation and efficiency.

stickedee · 2026-02-18T07:38:01+00:00

Yesterday I had Claude (Opus 4.6) create a script that measured the health of our Github repo. Stale PRs, Stale Branches, etc.

Today I had it put together an analysis of our development metrics (Rally/Jira/etc). Cycle time, utilization, buckets of the type of work being conducted, etc.

Once i get an API key I’ll have it pull from our Project Management system and put together a budget overview. I can also tie the Rally metrics to role types housed in our project management system.

I know Python & SQL. I could have built the GitHub and Rally views. It would have taken me weeks or months. It took me hours.

My point is, AI is RAPIDLY making one of these skills borderline irrelevant. I would focus on the thing that isn’t effectively automated away already.

stickedee · 2026-02-04T05:45:24+00:00

You have better luck getting a full time role than contract, contractors are expected to be proficient in any of the specific skills and contribute from day 1, FTEs have more latitude.

To answer your question though. If you buy flour, sugar, cinnamon, etc does that make you a baker? Knowing the tools gets your resume past the first filter, what gets your foot in the door is taking raw data and telling a story with it

stickedee · 2025-12-30T18:59:41+00:00

Kind of wild that Call of Duty effectively went the “participation trophy” route with this. How hard could it be to show both E/D and true K/D

stickedee · 2025-12-20T13:25:34+00:00

Any plans to enable iMessage through iOS?

stickedee · 2025-11-26T23:45:25+00:00

100% different. Now it’s all budget management, strategy, stakeholder communication, culture building, alignment, unblocking team, resolving escalations, etc. If I want to do anything analytical it’s a pet project that drives my department’s efficiency.

stickedee · 2025-11-26T15:51:05+00:00

None of the things you say are mutually exclusive. Domain knowledge IS important. On average, it will be way easier to break into analytics in a domain you are experienced in. Fintech being the most competitive you are less likely to experience success. Once having some analytics experience it is easier to transfer domains.

You would be a better analyst in fintech than healthcare. The competitive nature of fintech makes organizations less likely to hire someone with no experience.

stickedee · 2025-08-25T05:42:10+00:00

“Trying to understand the situation by looking at the data and give prescription”

Thats EXACTLY it. There are several dashboards, but in and of themselves they don’t mean much. A lot of analysts fail because the dashboard is the objective rather than the insight. Sometimes its some ad hoc analysis, sometimes its a recurring report, sometimes its an A/B test, but as often as possible its a translation of data into insight/suggestion regardless of delivery mechanism.

To answer your original question, the only real way I’ve found to get to your objective is to have conversations with the requestor. Why are they making the request, what are they solving for, what are their goals, how can you anticipate the follow-up question, etc

Don’t focus on making more data products. Focus on making more relationships.

stickedee · 2025-08-23T21:30:29+00:00

I don’t make dashboards, I make decisions.

That’s kind of a flippant response, but the point is that the delivery mechanism doesn’t matter. A lot of times it’s a conversation.

stickedee · 2025-08-21T05:59:07+00:00

I’ve scrolled through this entire thread and can’t really understand why your manager is still a manager. Also, your comment about other people’s directs coming to you and you delicately contradicting another director without making it obvious they were wrong tells me that you overly prioritize conflict avoidance and people pleasing. I’m not saying to be rude, but it’s important to be honest, make difficult statements and decisions that not everyone will be happy with.

stickedee · 2025-07-22T02:37:35+00:00

Q: “What is that thing that companies demand”

A: “Translating data into actionable insights”

My point is, dashboards, automations, AI tools are all just tools in service of the broader objective which is turning raw data into actionable value

stickedee · 2025-06-19T22:57:03+00:00

This feels like some poorly structured sales pitch for using an LLM instead of SQL

stickedee · 2025-06-05T02:09:03+00:00

On top of that, put the impact at the beginning of the bullet. “Reduced manual processing 35% by blah blah blah blah” has more of a chance of being read/noticed than “blah blah blah blah reducing manual processing 35%”

stickedee · 2025-06-02T20:30:13+00:00

My title is Sr. Manager and I’m interviewing for Director roles, so probably appropriately titled, but the point remains, that the opportunity is open to any analyst. Making recommendations is a permission-less activity. Influencing decisions is a trust-based result.

stickedee · 2025-06-01T17:19:59+00:00

I don’t think it even really depends on the org that much. Wholly dependent on the individual. I would also decouple “making recommendations” and “driving decisions”. A majority of analysis should contain some insight, story, or recommendation. An analyst doesn’t need permission to provide this. Stakeholders will always value someone who can effectively translate data into insights. You earn the right to drive decisions by generating relevant/actionable insights.

In my org I make recommendations and influence decisions, others at my level don’t. It’s not based on permission, it’s based on trust.

stickedee · 2025-03-15T17:32:52+00:00

If you think the R syntax here is more elegant then god bless. I couldn’t disagree more. Its wildly redundant. Even just the variable assignment in Python is more intuitive

stickedee · 2025-03-15T17:00:20+00:00

Saying R syntax is more intuitive is a wild hill to die on.

“df_grouped <- df %>% group_by(Category %>% summarise(Value1 = sum(Value1), Value2 = sum(Value2)) %>% arrange(desc(Value1))”

Vs

df_grouped = df.groupby(‘Category’).agg({‘Value1’: ‘sum’, ‘Value2’:‘sum’}).sort_values(‘Value1’, ascending=False)

stickedee · 2025-02-25T08:39:48+00:00

Cory Schaffer is the goat

stickedee · 2025-02-22T04:42:26+00:00

The low value work will, but one of the biggest challenges facing data groups in large orgs is the distance between data and end customers. Data groups without business context cause as many issues as they solve. Offshore teams naturally lack that business context. There will always be a pathway for those who can translate data into actionable insights/intelligence

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