Skills Data Your Organization Can Actually Trust

Start from the jobs, not from a skills wish list. Critical skills are the ones your most business-critical roles genuinely depend on, and you can only see that once every job is mapped to the skills it requires at the level it requires. iMocha maps your jobs and job descriptions onto the Lightcast Skills Taxonomy, so criticality falls out of your own role structure instead of an opinion formed in a workshop.

Normalize them against a common standard rather than negotiating a merged list in workshops. Each list maps to the Lightcast Skills Taxonomy, duplicates across lists collapse into single canonical skills, and terms with no equivalent are surfaced for a decision. You end up with one skills language that nobody had to lose an argument to agree on.

Normalize it against a single standard instead of cleaning it by hand. iMocha matches every raw skill string — duplicates, misspellings, course titles, legacy competency names — to one canonical skill in the Lightcast taxonomy, and a few thousand raw strings typically collapse into a few hundred real skills. Anything without a clean match is surfaced for review rather than dropped, and the cleaned taxonomy maps back into your HCM.

The real choice is not build versus buy — it is whether you end up owning your vocabulary or renting someone else's. Building from scratch means a permanent team tracking which skills are emerging, being renamed, merging or fading, which produces no competitive advantage. But adopting a vendor's proprietary taxonomy wholesale means your skill definitions live inside their product, and you lose them when the contract ends. The workable middle is to keep your own IDs and definitions as the point of reference, and align them to an independently maintained standard so they stay comparable outside your organization.

Use your existing job descriptions as the input rather than rewriting them first. iMocha's enrichment engine reads jobs and job descriptions, discovers and classifies the skills inside them, and maps each one to the Lightcast taxonomy with a required proficiency level attached. Human review then becomes a check on a draft instead of authorship from a blank page.

Far fewer than most organizations start with. Raw skill lists pulled out of HCM and learning systems routinely run to several thousand entries, but a large share are duplicates, misspellings or course titles rather than distinct skills. Normalizing against a standard taxonomy typically reduces them several times over, and the right number is whatever survives once every string resolves to a single canonical definition.

Use a consistent rubric attached to the skill itself, rather than free-text descriptions written job by job. iMocha layers proficiency rubrics onto the Lightcast taxonomy so a given level means the same thing everywhere that skill appears, then maps the level each job requires. Without that, two teams can both report a level 3 and mean entirely different things.

Watch the taxonomy, not only your own list. Skills get renamed, merge into others, or fade in relevance, and a static framework hides all three. iMocha applies ongoing Lightcast taxonomy updates inside its Taxonomy Management capability, so superseded and declining skills stay visible in your taxonomy instead of quietly aging in place.

Treat it as maintained infrastructure rather than a project deliverable. iMocha applies ongoing Lightcast taxonomy updates — emerging, renamed, merged and declining skills — and reflects them through your jobs-to-skills mapping. The taxonomy updates in place instead of needing a refresh project every 18 months.

Considerably less than building one from scratch, because the standard already exists. The sequence is: normalize your existing skill terms, map jobs and job descriptions, enrich with skill categories and proficiency levels, then publish into your HCM. Most organizations start with one function or job family, prove the loop there, and scale from a working reference rather than a slide.

No. iMocha maps the enriched taxonomy natively into SAP SuccessFactors (Attributes Library), Workday HCM (Skills Cloud) and Oracle HCM (Dynamic Skills — Skills Nexus). The taxonomy strengthens the HCM you already own instead of sitting beside it as a second system your teams have to remember to open.

A competency framework describes broad behaviors, usually written once per job family; a skills taxonomy is a granular, structured library of individual skills with defined relationships between them. Lightcast structures it as Domain → Sub-Domain → Skill Cluster → Skill. The practical difference is resolution — competencies rarely map cleanly onto a job posting, a learning item or an HCM field, whereas skills do.

Keep them as first-class entries in your taxonomy rather than as exceptions. A working taxonomy balances two things: definitions specific to your organization — proprietary products, internal systems, in-house methodologies, mission-specific capabilities — and definitions aligned to the market so they stay comparable outside your walls. Normalization maps whatever has a standard equivalent and surfaces whatever does not, so your own skills sit alongside the standard as recognized additions instead of quietly diverging from it.

HR owns the definitions, IT owns the integration, and the business owns the jobs-to-skills mapping. The common failure is HR owning all three, because the mapping then reflects HR's reading of a job description rather than how the work is actually structured. Grounding the taxonomy in an external standard removes much of the friction, since the definitions are nobody's internal opinion.

You should own it, and it is worth settling before you sign anything. Your curated taxonomy — your skill IDs, your own skill definitions, your job-to-skill mappings — is your intellectual property, and iMocha's role is to maintain the infrastructure underneath it rather than to own your vocabulary. The Lightcast library beneath it is licensed as the market-aligned reference layer; the taxonomy you build on top of it is yours. The distinction matters most at renewal, because if your definitions exist only inside a vendor's product, you end up negotiating for access to your own work.

Lead with what the organization cannot currently answer. Most leadership teams cannot say how many distinct skills the business actually runs on, which jobs require which skills at what level, or how much of the learning budget is aimed at skills that are declining in relevance. Grounding the work in a recognized taxonomy also removes the first objection you will hear, which is whether the underlying skills data can be believed at all.

Anchor every regional list to the same underlying skill rather than maintaining a separately translated list per country. The Lightcast reference library covers English, Spanish and French, so terms captured by teams in different markets can resolve to the same canonical skill and consolidate without a manual reconciliation exercise. Coverage beyond those languages is worth scoping explicitly against your own footprint — talk to your iMocha representative about the markets you operate in rather than assuming parity across all of them.

Nothing visibly — until you try to compare anything. Skill reports stop reconciling, the same job is described differently in two countries, learning spend is duplicated across near-identical skills, and no number rolls up to a board-level answer. The sharpest consequence is internal mobility: when two divisions describe the same work using different frameworks, a role in one is not comparable to a role in the other, so movement between them stalls. Fragmented lists do not just make reporting harder — they quietly build the silos a skills strategy was meant to remove.

Map jobs to skills to proficiency levels, in that order. Your job architecture provides the structure, the taxonomy provides the vocabulary, and the mapping between them is what makes either one usable. iMocha reads your existing jobs and job descriptions, maps each to Lightcast skills, and attaches the proficiency level the job requires — so job architecture and skills stop being two disconnected projects.

Three things: jobs described in a consistent taxonomy, the skills each job requires, and the proficiency level required for each. Without proficiency levels you cannot model whether a capability gap closes through development or requires hiring, which is the question workforce planning exists to answer. Note that this partnership covers the Lightcast Skills Taxonomy; Lightcast's labor-market intelligence and job-postings analytics are separate offerings and are not part of it.