Frames are shared references I built for analysts and product builders who need jobs, places and skills to match across products. I kept seeing the same work repeated. One dataset called a job by one title, another grouped it differently, and place and skill lists followed their own conventions. Before anyone could answer the question, somebody had to build and maintain a translation table.
I wanted the shared identity to be public, where more than one product could use it. A frame gives each subject a stable key. That leaves each product free to serve its own purpose while keeping a common reference underneath. JobFrame, GeoFrame and SkillFrame apply that idea to work, place and skill.
I am building the family toward industries, workforce segments, personas and organizational problems. The aim is practical: data about the same subject should line up without another round of name cleanup. Analysts can spend more time on the question they came to answer, and product builders can stop creating another private list.
Who it is for
Frames are shared references for analysts and product builders who need jobs, places and skills to match across datasets and products. They are for people connecting pay and development data who are tired of every system spelling the same job, city or skill a different way.
The problem
Analysts lose time because every dataset can name or group the same job, city or skill differently. Before pay or development data can be compared, someone has to reconcile the labels and maintain a translation table. Those tables drift as sources change, so the cleanup returns with each new dataset or product. Product builders inherit the same burden by creating another private list that must be matched and maintained.
What I built
Frames are public references for analysts and product builders who need records about the same subject to match across products. Each frame assigns a stable key, a concrete code that a dataset can store and use to match records. In JobFrame, `ACCO.GEN.P3` is a profile key for one job level. Two products that store the same key can refer to the same level without matching names again. JobFrame covers jobs and job levels, including title mappings and pay. GeoFrame covers places, with 393 U.S. metro profiles, 7 world regions and pay by place. SkillFrame records what a skill is, which jobs use it and how rare it is. The references are public, so they can be reused outside the product that first needs them.
What is new in it
- Each live frame gives its subject a machine-storable key. JobFrame’s `ACCO.GEN.P3`, for one job level, shows how datasets can keep a shared reference without interpreting the name each time.
- GeoFrame brings place profiles and pay by place into the same reference, covering 393 U.S. metro profiles and 7 world regions.
- SkillFrame keeps a skill’s definition, the jobs that use it and how rare it is together, so those details remain attached when products reference the skill.
Where it stands
Frames are being built so jobs, places, skills and related subjects keep the same reference as data moves between products. JobFrame, GeoFrame and SkillFrame are live today; frames for industries, workforce segments, personas and organizational problems are in development. The aim is to make shared references the starting point for analysis and product building.
