data entry supervisortodata quality specialist
A data entry supervisor already meets 42% of what the data quality specialist role asks for. The move turns on 11 required skills not yet in the profile.
42%
61.4/ 100
1 Share of the data quality specialist role’s weighted skill requirement already met by the data entry supervisor profile. Required skills count in full, supplementary skills at 0.35. Directional: the figure for the reverse move differs. 2 Combines what is missing with how specialised it is, so a gap of general skills scores easier than the same number of narrow ones.
Why this move works
A data quality specialist role treats 9 of its required skills as things a data entry supervisor already does. These are the ones it depends on most.
- database
- establish data processes
- implement data quality processes
- manage data
- normalise data
- perform data cleansing
What stands in the way is 11 required skills the profile does not yet cover. Table 3 groups them; Table 4 says which to take first.
Table 2 · What you already bring
Of the 16 skills that carry over, these are the ones fewest other occupations ask for. A data quality specialist role needs them, and most people applying for one will not have them already. This is the part of a data entry supervisor background worth leading with.
- already held normalise data working with computers
- already held implement data quality processes working with computers
- already held perform data cleansing working with computers
- already held establish data processes working with computers
- already held database information and communication technologies (icts)
- already held LDAP information and communication technologies (icts)
All 16 carried skills, including the 9 the data quality specialist role treats as required.
Table 3 · What you would need to learn
The 24 missing skills fall into 9 areas of the ESCO skill hierarchy, numbered below in the order worth working in: the areas carrying the most required skills come first, and inside each one the required skills sit above the supplementary ones.
- required, not held handle data samples required
- required, not held manage database required
- required, not held report analysis results required
- optional, not held execute ICT audits optional
- optional, not held execute analytical mathematical calculations optional
- required, not held design database scheme required
- required, not held address problems critically required
- optional, not held design database in the cloud optional
- optional, not held build business relationships optional
- optional, not held train employees optional
- required, not held define data quality criteria required
- required, not held manage standards for data exchange required
- optional, not held perform project management optional
- required, not held utilise regular expressions required
- required, not held use data processing techniques required
- optional, not held perform data analysis optional
- required, not held data ethics required
- optional, not held visual presentation techniques optional
- required, not held information structure required
- optional, not held data engineering optional
3 further areas in the appendix
Table 4 · Where to start
The 3 entries a data quality specialist role is least likely to hire without. The ordering is computed from the skill data, not from what pays.
- 01 utilise regular expressions skill · occupation specific
- 02 define data quality criteria skill · sector specific
- 03 design database scheme skill · sector specific
Each entry opens a course search for that skill. Career Overlap earns nothing from these links.
Appendix · The rest of the record
All 16 skills that carry over
- already held database knowledge
- already held establish data processes skill
- already held implement data quality processes skill
- already held manage data skill
- already held normalise data skill
- already held perform data cleansing skill
- already held process data skill
- already held query languages knowledge
- already held resource description framework query language knowledge
- already held LDAP knowledge
- already held LINQ knowledge
- already held MDX knowledge
- already held N1QL knowledge
- already held SPARQL knowledge
- already held XQuery knowledge
- already held manage schedule of tasks skill
The 3 learning areas not shown above
- optional, not held data quality assessment optional
- optional, not held business processes optional
- optional, not held healthcare analytics optional
- optional, not held statistics optional
13 supplementary skills, helpful but not required
- optional, not held data quality assessment optional
- optional, not held design database in the cloud optional
- optional, not held execute ICT audits optional
- optional, not held healthcare analytics optional
- optional, not held visual presentation techniques optional
- optional, not held build business relationships optional
- optional, not held business processes optional
- optional, not held data engineering optional
- optional, not held execute analytical mathematical calculations optional
- optional, not held perform data analysis optional
- optional, not held perform project management optional
- optional, not held statistics optional
- optional, not held train employees optional
26 held skills the data quality specialist role does not ask for
- not needed by the target role ABBYY FineReader knowledge
- not needed by the target role OmniPage knowledge
- not needed by the target role apply information security policies skill
- not needed by the target role apply organisational techniques skill
- not needed by the target role coach employees skill
- not needed by the target role company policies knowledge
- not needed by the target role data models knowledge
- not needed by the target role data storage knowledge
- not needed by the target role develop working procedures skill
- not needed by the target role discharge employees skill
- not needed by the target role documentation types knowledge
- not needed by the target role estimate duration of work skill
- not needed by the target role evaluate employees skill
- not needed by the target role gather feedback from employees skill
- not needed by the target role implement data warehousing techniques skill
- not needed by the target role information confidentiality knowledge
- not needed by the target role introduce new employees skill
- not needed by the target role maintain data entry requirements skill
- not needed by the target role manage ICT data classification skill
- not needed by the target role manage data collection systems skill
- not needed by the target role manage employee complaints skill
- not needed by the target role motivate employees skill
- not needed by the target role optical character recognition software knowledge
- not needed by the target role recruit employees skill
- not needed by the target role supervise data entry skill
- not needed by the target role supervise work skill
8 gaps that are knowledge rather than practice
Knowledge gaps usually close through study. Practical skill gaps usually need something you can point at.
- required, not held information structure required
- required, not held data ethics required
- optional, not held data quality assessment optional
- optional, not held healthcare analytics optional
- optional, not held visual presentation techniques optional
- optional, not held business processes optional
- optional, not held data engineering optional
- optional, not held statistics optional
How this record was compiled
Both occupations are taken from ESCO, which lists the skills and knowledge each occupation is expected to have and marks every one required or optional. Nothing here is a prediction about hiring, and nothing here knows that a particular employer wants a particular certificate. Treat Table 3 as a starting point for your own research rather than a syllabus. The full method states what these figures can and cannot tell you.