Chapter 2 — Data at a housing association
Lesson map
Lesson 02 published. Map, example, EX004.
flowchart TD
N0["Learning outcomes"]
N1["2.1 Main data sets"]
N2["2.2 Following a repair"]
N3["2.3 Who depends on repair data"]
N4["2.4 Safety data"]
N0 --> N1 --> N2 --> N3 --> N4
N4 --> EX["Worked example"]
EX --> Q["Exercise EX004"]
Logical drawing
flowchart LR
N0["Learning outcomes"]
N1["2.1 Main data sets"]
N2["2.2 Following a repair"]
N3["2.3 Who depends on repair data"]
N4["2.4 Safety data"]
N0 --> N1 --> N2 --> N3 --> N4 --> Q["EX004"]
[Learning outcomes]
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v
[2.1 Main data sets]
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v
[2.2 Following a repair]
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v
[2.3 Who depends on repair data]
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v
[2.4 Safety data]
|
v
[Worked example] --> [Exercise EX004]
Learning outcomes
You will (1) identify the main data sets in a housing association; (2) map how repair data flow; (3) recognise who depends on them.
2.1 Main data sets
Ravi lists Calder Valley's main data sets: properties with addresses, types and components such as boilers; tenants and tenancies; repair jobs with priority, dates and contractor; rent accounts; complaints; and safety inspections such as gas checks. Each is held in the housing management system, except contractor job updates, which arrive through a portal. He notes that properties link everything, because repairs, tenancies and inspections all refer to a property identifier. Ravi records each data set in a simple list with its system, its owner if known and the property link.
2.2 Following a repair
Ravi traces a repair. A tenant reports a leak by phone. A call handler creates a job with a priority code and assigns a contractor. The contractor attends, updates the portal with completion details and the portal sends data back to the housing system each night. Reports draw on completed jobs. He finds three handoffs where data are typed or transferred, each a point where errors can enter, and marks them on a diagram. Mrs. Okonjo confirms that his diagram matches how repairs actually work.
2.3 Who depends on repair data
Many people rely on repair data: tenants waiting for repairs, call handlers answering queries, managers monitoring contractors, finance paying invoices and the regulator assessing performance. Ravi notes that an error in a completion date can make a late repair look on time, misleading the regulator, or make an on-time repair look late, leading to an unfair penalty for a contractor. Understanding dependents explains why quality matters beyond the data team. Ravi adds each group of dependents to his diagram, so the effect of errors at each handoff can be seen at a glance.
2.4 Safety data
Some data affect tenant safety directly. Gas safety checks must be carried out every year, and records must show when each check happened. Ms. Moreau stresses that errors in such data are serious: a missing record might mean a check was missed. Ravi learns that data supporting legal safety duties need the strictest controls, and that technologists should flag any doubt about such records immediately rather than waiting for routine reports. Ravi notes that the gas check records are owned by the compliance manager, who must be told of any doubt.
Worked example
Ravi lists Calder Valley's data sets with property as the link, traces a repair through three handoffs, identifies dependents from tenants to the regulator and learns that safety data need the strictest controls.
Synthetic case. Data sets are synthetic.
Errors and limits
Treating safety records like routine data delays urgent action.
Exercises
- EX004 — Linking entity. What links most data sets? Rubric: 3 = the property identifier;; 2 = partly correct;; 1 = other;; 0 = none.
- EX005 — Handoff count. How many handoffs did Ravi find in a repair? Rubric: 3 = three;; 2 = partly correct;; 1 = wrong;; 0 = none.
- EX006 — Gas check frequency. How often must gas checks happen? Rubric: 3 = every year;; 2 = partly correct;; 1 = wrong;; 0 = none.
Continue studying — lesson 02 (chapter 02 of 30)
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