GLOSSARY

chief data architect. The owner of the data definition process.

control. The managerial act of comparing actual performance to standards and acting on the difference (after Juran).

data creator. Any person or process who creates data in the course of his, her, or its work.

data customer. Any person or process who uses data to complete his, her, or its work.

data maestro. Any individual with broad and deep knowledge of data quality, considerable practical experience, and the gravitas to be a trusted advocate for data quality within his or her organization.

data provocateur. Any individual who takes steps to change the course of his work team, department, or company in respect to its management of data quality.

data quality (aspirational definition). Exactly the right data in exactly the right place the right time and in the right format to complete an operation, serve a customer, conduct an analysis, craft a plan, make a decision, or set and execute strategy.

data quality (day-in, day-out definition). Meeting the most important needs of the most important customers.

data quality (formal definition). Data is of high-quality if they are fit for their intended uses (by customers) in operations, analytics, decision-making, and planning. To be fit for use, data must be ‘free from defects’ (i.e., “right”) and ‘possess desired features’ (i.e., be the “right data”).

data quality team. Any group charged with improving data quality across a department or corporation.

embedded data manager. Any person embedded into a business function whose role involves helping data creators and customers complete their responsibilities in these roles.

Friday Afternoon Measurement. A process for measuring data quality that aims to develop a simple, defensible measurement as quickly as possible.

getting in front/getting in front on data/getting in front on data quality. An approach, including roles and responsibilities, methods, and tools, that aims to improve data quality by “getting in front” of the issues that cause data to be unfit for use.

hidden data factory. Non-value-added work, conducted by individuals, work teams, companies, and departments to make data they need to do their work fit for use.

process. Any sequence of work activities, characterized by common inputs and outputs and directed to a common goal.

process management cycle. A structured approach for ensuring that a process is managed, end-to-end, with special emphasis (here) on the roles of data creators and data customers.

proprietary data. Data owned by a single company, that it can protect and use to create a sustained competitive advantage.

quality improvement cycle. A structured approach for putting improvement teams in place and helping them identify and eliminate root causes of error.

rule of ten. A rule-of-thumb that advises that it costs ten times as much to complete a simple operation correctly when the data is flawed in any way compared to when they are all fit-for-use.

supplier management cycle. A structured approach for working with data suppliers to ensure that one’s needs, as a data customer, are met.

voice of the customer (VoC). On one level, the Voice of the Customer is a document that summarizes a customer’s needs of data from a particular supplier. More abstractly, the idea is that data creators understand who uses data they create, in “the voice of the customer.”