Data quality specialist to data analyst
A data quality specialist already meets 43% of what the data analyst role asks for. The move turns on 19 required skills not yet in the profile.
68.3/ 100
1 How much of what a data analyst is expected to do is already part of a data quality specialist’s work. Directional: the figure for the reverse move differs, because moving into a speciality is not the same problem as moving out of one. 2 Every skill the data analyst role is recorded as needing, required and optional together. What these numbers mean.
Why this move works
A data analyst role treats 17 of its required skills as things a data quality specialist already does. You would walk in with these on day one.
- data engineering
- data ethics
- data quality assessment
- define data quality criteria
- establish data processes
- execute analytical mathematical calculations
What stands in the way is 19 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 26 skills that carry over, these are the ones fewest other occupations ask for. A data analyst role needs them, and most people applying for one will not have them already. This is the part of a data quality specialist background worth leading with.
- already held handle data samples information skills
- already held data ethics arts and humanities
- already held normalise data working with computers
- already held healthcare analytics health and welfare
- already held implement data quality processes working with computers
- already held perform data cleansing working with computers
All 26 carried skills, including the 17 the data analyst role treats as required.
Table 3 · What you may need to learn
The 41 missing skills fall into 8 areas of the ESCO skill hierarchy, numbered in the order worth working in. Areas carrying the most required skills come first, and inside each one the required skills sit above the optional ones. Nothing here is a verdict on you: it is a list of what this particular job asks for that your current one does not.
- still to learn data mining required
- still to learn data models required
- still to learn information confidentiality required
- still to learn information extraction required
- still to learn unstructured data required
- still to learn data science required
- still to learn data visualisation software required
- optional, not held Hadoop optional
- optional, not held cloud technologies optional
- optional, not held data storage optional
- optional, not held information architecture optional
- optional, not held online analytical processing optional
- optional, not held image recognition optional
- optional, not held web analytics optional
- still to learn analyse big data required
- still to learn apply statistical analysis techniques required
- still to learn collect ICT data required
- still to learn interpret current data required
- optional, not held create data models optional
- optional, not held gather data for forensic purposes optional
- optional, not held manage cloud data and storage optional
- optional, not held manage data collection systems optional
- optional, not held make data-driven decisions optional
- still to learn digital data processing required
- still to learn integrate ICT data required
- still to learn perform data mining required
- still to learn use databases required
- optional, not held manage quantitative data optional
- optional, not held store digital data and systems optional
- optional, not held use spreadsheets software optional
- still to learn documentation types required
- still to learn information categorisation required
- optional, not held social network analysis optional
- still to learn business analytics required
- optional, not held statistical modeling techniques optional
- optional, not held game theory optional
- still to learn business intelligence required
- optional, not held marketing analytics optional
2 further areas in the appendix
Table 4 · Where to start
The 3 entries a data analyst role is least likely to hire without. The ordering is computed from the skill data, not from what pays.
Each entry opens a course search for that skill. Career Overlap earns nothing from these links.
Appendix · The rest of the record
All 26 skills that carry over
- already held data engineering knowledge
- already held data ethics knowledge
- already held data quality assessment knowledge
- already held define data quality criteria skill
- already held establish data processes skill
- already held execute analytical mathematical calculations skill
- already held handle data samples skill
- already held implement data quality processes skill
- already held information structure knowledge
- already held manage data skill
- already held normalise data skill
- already held perform data cleansing skill
- already held query languages knowledge
- already held resource description framework query language knowledge
- already held statistics knowledge
- already held use data processing techniques skill
- already held visual presentation techniques 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 database knowledge
- already held healthcare analytics knowledge
- already held report analysis results skill
The 2 learning areas not shown above
- optional, not held multidisciplinary research optional
- optional, not held research design optional
- optional, not held deliver visual presentation of data optional
22 supplementary skills, helpful but not required
- optional, not held Hadoop optional
- optional, not held cloud technologies optional
- optional, not held create data models optional
- optional, not held data storage optional
- optional, not held gather data for forensic purposes optional
- optional, not held information architecture optional
- optional, not held manage cloud data and storage optional
- optional, not held manage data collection systems optional
- optional, not held online analytical processing optional
- optional, not held statistical modeling techniques optional
- optional, not held deliver visual presentation of data optional
- optional, not held game theory optional
- optional, not held image recognition optional
- optional, not held make data-driven decisions optional
- optional, not held manage quantitative data optional
- optional, not held marketing analytics optional
- optional, not held multidisciplinary research optional
- optional, not held research design optional
- optional, not held social network analysis optional
- optional, not held store digital data and systems optional
- optional, not held use spreadsheets software optional
- optional, not held web analytics optional
14 held skills the data analyst role does not ask for
- not needed by the target role address problems critically skill
- not needed by the target role build business relationships skill
- not needed by the target role business processes knowledge
- not needed by the target role design database in the cloud skill
- not needed by the target role design database scheme skill
- not needed by the target role execute ICT audits skill
- not needed by the target role manage database skill
- not needed by the target role manage schedule of tasks skill
- not needed by the target role manage standards for data exchange skill
- not needed by the target role perform data analysis skill
- not needed by the target role perform project management skill
- not needed by the target role process data skill
- not needed by the target role train employees skill
- not needed by the target role utilise regular expressions skill
24 gaps that are knowledge rather than practice
Knowledge gaps usually close through study. Practical skill gaps usually need something you can point at.
- still to learn business analytics required
- still to learn data mining required
- still to learn data models required
- still to learn documentation types required
- still to learn information categorisation required
- still to learn information confidentiality required
- still to learn information extraction required
- still to learn unstructured data required
- still to learn business intelligence required
- still to learn data science required
- still to learn data visualisation software required
- optional, not held Hadoop optional
- optional, not held cloud technologies optional
- optional, not held data storage optional
- optional, not held information architecture optional
- optional, not held online analytical processing optional
- optional, not held statistical modeling techniques optional
- optional, not held game theory optional
- optional, not held image recognition optional
- optional, not held marketing analytics optional
- optional, not held multidisciplinary research optional
- optional, not held research design optional
- optional, not held social network analysis optional
- optional, not held web analytics optional
Common questions about this move
Can a data quality specialist become a data analyst?
Yes, and it is a substantial move. A data quality specialist already covers 43% of what a data analyst is expected to do: 26 skills carry over, 17 of which the data analyst role treats as required.
What skills does a data quality specialist need to become a data analyst?
19 required skills are missing, plus 22 optional ones. They fall into 8 areas of the ESCO skill hierarchy, led by information and communication technologies (icts). The single most important is business analytics.
How hard is the move from data quality specialist to data analyst?
Its learning distance is 68.3 out of 100, which is the substantial band. That accounts for both how much is missing and how specialised those skills are.
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.