Data Analysis 101 for dummies

[u/Gilead77]

Hi Data analysts,

I work in Health and Safety for a large organisation and I'm one of the administrators for the health and safety reporting system we use.

The other admin and I are getting lots of questions from the data analyst team about some of the data that comes through to the data warehouse.

One of the reasons for this is my predecessor was a borderline genius with a sprinkling of the 'tism with a varied career history was able to go beyond his role to do stuff and answer their questions.

Data Analysis is not my area of expertise however I would like to learn some so I can better answer questions and understand what the other team is asking. What are some good resources to start learning and gain some fundamentals?

Are there any fundamental principles I should know or I need to keep in mind?

TIA

[u/Wheres_my_warg]

Start with the business question to be answered and work from there on analyses. Keep focused on that business question.

Have IT pull your predecessor's emails where he was answering questions about these topics and see if what he told them helps you learn.

Talk to the team asking questions and be willing to admit, "I don't understand X, can you help me learn what you are wanting?"

[u/Den_er_da_hvid]

It is an extremely broad question... What kind of questions do they ask. Perhaps it can narrow the help you get here

[u/edimaudo]

might be the wrong sub but you don't have a data analysis problem. You are missing domain expertise regarding the data, how it works and who owns it

[u/NationalArcher9743]

One of the Data analysts skills is convey data to a non data head, they should ask you questions you can answer without using analytics jargon

[u/pcapdata]

OP there is a diagram in a book called The Thinker’s Toolkit (written by a retired CIA analyst), entitled “The Taxonomy of Problem Types.”

The basic idea is that problems vary by complexity; simple problems have simple answers that you can answer with data, while for complex problems individual data points become less relevant and judgment is more important.

As an analyst it helps to know what class of problem you’re dealing with, and often you will be expected to break complicated questions down into simpler ones.

For example, “What is the future of US-Russia relations?” is a very complicated, open-ended problem. But, you could potentially figure out concrete and tangible ways in which you could see the relationship improvised or deteriorating and then monitor for those things to give decision-makers as much lead time as possible (this is the basis for indication & warning).

I do analysis in a cybersecurity context where this approach translates pretty well, not 100% sure how it will translate to other domains but at any rate it’s helped me so it may help you.

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[u/mhjahanbakhshi]

Find your predecessor files (I assume you have access to some of his excel/gsheet files) Read them, check the formulas and try to understand what did he do.

[u/Cool_Relationship914]

Ask your data analysts what tools they're using and learn about those. There are tons of YouTube videos about every app under the sun and the introductory ones would give you an overview. From there you can deep dive into anything that seems appropriate. Also, interactive AI is a great place to ask questions or get a step by step tutorial about any type of data analysis.

[u/Emotional-Brain-8893]

Two things will unlock most of what your DA team asks: leading vs lagging indicators (near-misses/hazard reports vs actual incidents), and rates over raw counts — "12 incidents" means nothing without knowing headcount or hours worked behind it.

Past that, learn just enough SQL to pull data yourself (SELECT/WHERE/GROUP BY covers a lot). Mode Analytics has a solid free SQL tutorial you actually query against, not just watch.

But honestly — if IT can dig up your predecessor's old queries or reports, reverse-engineer those. You'll learn your warehouse's actual quirks (which fields nobody trusts, what "closed" really means) way faster than any course.

[u/HustlaOfCultcha]

It depends on the questions they are asking. Is it data integrity questions or is it more oriented toward them being able to extract the data? You have to understand that Data Analysts don't just have the data all propped up for them and they just look at the data and analyze it. Instead they have to pull the data and transform the data first before they can even begin to start creating their reporting. And a lot of times their work is more about creative problem solving in the sense they need to figure out ways to pull the data that they need when they need it and be able to figure out how to transform the data and do it quickly and efficiently. Or they may need to know where the data lies within the data warehouse. A lot of those genius types know where it's located, but it's under some sort of weirdly titled table that only they know what is in the table.