Company-wide data requests

CoreData EngineeringData AnalysisBI & Data VisualizationRequirements Definition

Organisation: MIIDAS CO., LTD.
Period: Jan 2025 – Present
How it started: Assigned
My part: First contact through aggregation design and delivery
For whom: Sales planning, CS, business development and science teams, plus the product and CRM engineering teams
Scale: 332 of the 799 requests the team received through the intake form (April 2025 to August 2026, from 80 requesters), in an environment of roughly 2,000 tables and tens of millions of records

What I did

Work across an environment of roughly 2,000 tables and tens of millions of records.
Turn anything that needs looking at regularly into a table or view rather than a one-off extraction.
Designed and implemented the aggregations where accuracy matters: company-master matching, CRM code linkage, performance-based fee calculation.

Moving the questions that keep coming back out of ad-hoc work and into defined metrics and mart tables.
Putting terms, metric definitions and domain knowledge somewhere referenceable, so the premises do not have to be explained each time.

Start every request by pinning down definitions and conditions, putting the easily-misaligned concepts into words and agreeing on them before answering.
Decide per request how personal data is handled and whether to hand over aggregates or raw rows.

Extraction that goes inside the implementation, visualisation that serves the business goal.
I work alongside the business from the definition of the metric itself, and stay with it through to the insight it was asked for. From IoT systems to web and mobile, I have broad experience of building software, so I extract data with a deep understanding of the implementation behind it. Visualisation and analysis are the same — I put generative AI to work and deliver quickly, in the form the person will actually use. Underneath that, I can shape the data, the context around it and the ETL that feeds it for the age of generative AI, so that people and AI alike can work with what is there.

I read the code, then deliver the data
Table definitions rarely tell you what a column means to the business, so I read the specification and follow the code until I know. Years of building software myself means I can open a running system, watch how it behaves, and produce data — or answer a question about how that data comes to exist — on that basis. It changes how I use generative AI for the same work: because I know where to look, I can direct it precisely, get an answer quickly, and judge whether the answer holds.
I go and learn the business itself
An IoT system for a screw factory as a student, and a secondment to a steelmaking division in my first year of work, taught me the same lesson: what matters is the business outcome, the data that comes out of it, and the domain knowledge you need to connect the two. Sometimes the right answer is to change the workflow rather than to build anything at all. I would rather understand what the person asking is trying to achieve — including where AI genuinely helps — than simply implement what I was handed.