Whilst it’s nice to imagine a world of perfect data quality the reality is that most organisations will be dealing with data quality defects on a daily basis. I’ve noticed a wide variation in the way organisations manage the life cycle of defects and nowhere is that more apparent in the initial information gathering exercise that initiates the start of the data quality improvement cycle.

To help you improve your information gathering process I've included some questions that you may want to include in your formal review process but please feel free to add more in the comments section below.

• Presenting problem: What is the presenting complaint or defect as witnessed by the business user?
• Event history: What led up to the condition being observed? For example, what type of transaction was the user trying to complete? What actions were taken leading up to the problem?
• Previous history: Has this problem been observed before? What form did it take? Were there any preventative actions taken previously?
• Examination stats: Record a profile of the data at the time of assessment so that you have a full snapshot for later analysis.
• Problem severity (when reported): How severe a problem did this cause when reported? e.g. System failure, transaction failure, localised, corporate - create your own scale that makes sense
• Problem radiation: Does the problem exist in a single record, across multiple records in one table, across multiple tables, across multiple systems etc. Is it localised to your company or is the data shared externally?
• Information chain location: Which information chains are impacted? What is the identifier for determining specific locations on each chain?
• Data dictionary identifiers: Link across to any data dictionary identifiers e.g. Business term, technical terms that are known.
• Stage date/time: I prefer to record the date/time that each stage of the defect resolution pathway takes so that you can start to identify bottlenecks in the overall cycle.
• Plan: What is the proposed management plan for this issue?

You may be able to see something of a medical theme to the questions. I've found that the same interrogative process frontline healthcare workers adopt translates extremely well to the world of data quality where we need to get to the root-cause of a problem in a fast and structured way.

What you don't want is your staff adopting their own individual approaches. There needs to be a uniform system of information gathering and analysis so that you can onboard new team members swiftly and avoid confusion within the team.